Public report of starlake-ai/starflow, published 21 October 2026. It has the same score, grades and number of findings as the full report. The details of security findings (where they are in the code, which known vulnerabilities and which passwords or keys were found) are left out, so nobody can use them against the project.
Public
Executive report Measured under the Code Assurance Index, an open standard for code quality, using its published scoring rules (rubric version 2026.10.5). Watchdog signed this survey and filed it in the standard's public register, where anyone can look it up by its survey ID. Check this survey → Signed and filed in the standard's public register, 21 October 2026, 10:52 UTC · Survey ID cd_e563d7e4f1ac44b2a4a8a7537a652c21

starlake-ai/starflow

Measured 21 October 2026, 10:43 UTC, at code version 8114462

CAI score 43out of 100 At risk Down 13 since the last survey

The score is built from these lenses. Each lens grades one area of the code, such as how easy it is to change or how well it is protected. The weakest lens counts most, so one weak area pulls the score down instead of being averaged away.

Architecture 85 Adequate Maturity 71 Adequate Code Health 61 Adequate Production Readiness 50 Adequate Accessibility 41 Weak Security and Compliance 35 Weak

A medium codebase: 65,215 lines of product code in 1 project, tests not counted · Estimated rebuild effort: about 0.6 person-years · Weakest lens: Security and Compliance, 35

Findings by how serious they are

279 critical 352 serious 67 minor

15 dimensions are left out of the score. For each one, Watchdog either couldn't measure it in this code, or found no evidence of the safeguard it looks for, such as a tested backup restore. They count neither for nor against the score, and Limitations lists each one with its reason.

This survey was produced by

Watchdog
Producer
Canine Development
Analyzer
Watchdog engine 1.0.0
Measured
21 October 2026, 10:43 UTC

A measurement, not a certificate. The Code Assurance Index records a number together with the evidence it was worked out from, so anyone can recalculate it, but it doesn't certify or guarantee the code. The standard was written by Canine Development, which also builds Watchdog. It is open to any tool that implements it, and today Watchdog is the only one that does.

A critical security finding is open, such as a library with a known critical vulnerability or a password or key found in the code. The score itself is unchanged, but its band shows At risk until the finding is fixed. See the Security and Compliance lens.

The score is deterministic, which means the same code always gets the same score. Watchdog works it out with tools that follow published rules, and an AI model never decides it, so anyone who applies the same rules to the same code gets the same number. How it is done is explained under Methodology ▸

64of 67 dimensions measured by tools. The other 3 are advisory notes from an AI model, which explain the code but don't count towards the score.
668of 698 findings point to an exact file and line, so you can open each one in the code. The other 30 concern the repository as a whole, such as missing documentation.
67dimensions across 6 lenses, read from 65,215 lines of product code in 1 project.
Chapters

Executive summary

starlake-ai/starflow scores 43 out of 100, but it is rated At risk while a critical security finding is open; the score itself is unchanged.

The weakest lens counts most in the score, and here that is Security and Compliance, which scores 35 and is rated Weak. Its Secrets (history) dimension scores 0 out of 10, and until that reaches 4, Security and Compliance can't be rated higher than Adequate. The first actions are to stop accepting every TLS certificate: delete the trust manager whose `checkServerTrusted` is empty, the `HostnameVerifier` that returns `true`, `InsecureTrustManagerFactory` / `NoopHostnameVerifier` and `acceptAnyCertificate`, and trust a self-signed or private server by giving the client a trust store holding its CA — never by switching verification off and to resolve the 4 Leaked secret finding(s) in REDACTED Scanning, and if Security and Compliance rose to 70, the score would rise from 43 to about 51.

279 findings are critical: 233 in Static Analysis (SAST), 35 in Secrets (history), four in REDACTED Scanning, three in Keyboard semantics, one in Dependency Vulnerabilities, one in IaC & Container Security, one in Knowledge Freshness and one in Web-Security Posture.

Watchdog couldn't measure 14 dimensions in this code, among them Code Coverage, Test Reliability and Documentation Quality, so they are left out of the score. The product code is 65,215 lines, which would take an estimated 0.6 person-years, around €87,000, to rebuild.

Code Health 61 Adequate, capped by Nullable reference types Architecture 85 Adequate, capped by Project Cohesion Maturity 71 Adequate, capped by Bus Factor Production Readiness 50 Adequate, capped by REDACTED Scanning and Security & performance tooling Security and Compliance 35 Weak, capped by Secrets (history), Static Analysis (SAST) and Web-Security Posture Accessibility 41 Weak, capped by Keyboard semantics

A lens is capped at Adequate while one of its dimensions scores below 4 out of 10, however well the rest of the lens does. That way a serious problem in one place always shows in the lens's rating.

How the score is built: what each lens counts for The lenses don't count equally. The weakest lens counts most, the next weakest a little less, and so on, so a weak area pulls the score down instead of being averaged away. In the bar below, each part's width shows how much that lens counts and its colour shows its rating. Improving the widest part moves the score most, and as that lens improves, it counts for less, because another lens becomes the weakest.
Security and Compliance 35 · counts for 46%Accessibility 41 · counts for 25%Production Readiness 50 · counts for 14%Code Health 61 · counts for 8%Maturity 71 · counts for 4%Architecture 85 · counts for 2%

If Security and Compliance rose from 35 to 70, the lowest score rated Strong, the score would rise from 43 to about 51.

Code composition: what the lines of code are
Product code 65%Tests 35%
New since the last survey: 53 findings

These 53 critical or serious findings weren't in the previous survey, on 5 October 2026.

Watchdog compares every finding with the previous survey's. A finding that has only moved to another line in the same file counts as the same finding, so only new findings appear here. Advisory notes from the AI model are left out, because the model words them afresh in every survey.

Fixed since the last survey: 4 findings

These 4 critical or serious findings were in the previous survey, on 5 October 2026, and are gone from this one.

  • Code Duplication · Duplicated block (8 lines × 2) src/main/scala/ai/starlake/serve/SingleUserRequestHandler.scala
  • Change Coupling · Change coupling: ExtractBigQuerySchemaCmd.scala ↔ AutoTaskInfo.scala src/main/scala/ai/starlake/extract/ExtractBigQuerySchemaCmd.scala
  • Supply-chain Provenance & Signing REDACTED
  • Supply-chain Provenance & Signing REDACTED
Rebuild cost: about €87,000 · Estimate
Cost to rebuild€29,000 to €140,000, for 479 to 1,521 hours of work, about 0.6 person-years for 1 to 2 engineers
Difficulty of the problemStandard, judged from the kind of software and how it is built. Code that solves a harder problem, such as payments or medical bookings, costs more per line to write.
Quality factor×0.7 at a score of 43. Better code takes more work to write, so the factor is ×1.0 at a score of 70, lower below it and higher above it.
SizeMedium: 65,215 lines of product code

How the estimate is made: Watchdog prices the hours a team would need to write this code again, at €60 to €95 an hour. Declarations and boilerplate are priced at a fast rate and logic at a slower one, and the logic is then multiplied by the difficulty of the problem and by the quality factor. The estimate can be about 30% off either way, and it moves most with the hourly rate and the difficulty. For this code, every line is priced as straight logic, which keeps the estimate on the low side.

Top priorities

The three actions that raise the score most for the work they take. The full ranked list is in the Roadmap.

1
Stop accepting every TLS certificate: delete the trust manager whose `checkServerTrusted` is empty, the `HostnameVerifier` that returns `true`, `InsecureTrustManagerFactory` / `NoopHostnameVerifier` and `acceptAnyCertificate`, and trust a self-signed or private server by giving the client a trust store holding its CA — never by switching verification off.
Raises the score by about 7 points · Medium effort · Web-Security Posture
2
Resolve the 4 Leaked secret finding(s) in REDACTED Scanning — start with REDACTED (4).
Raises the score by about 3 points · Low effort · REDACTED Scanning
3
Resolve the 6 REDACTED finding(s) in Supply-chain Provenance & Signing — start with REDACTED, REDACTED, REDACTED.
Raises the score by about 3 points · Low effort · Supply-chain Provenance & Signing

Diagnosis: what the measurements show together

Each conclusion below combines several dimensions into one point that no single dimension shows, and lists the measurements it rests on.

Where the investment is most exposed · Value at risk
This code would cost about €87,000 to build again, about 0.6 person-years of work. Its weakest lens is Security and Compliance, at 35 (Weak). Security and Compliance is about how well the product is protected, and how it handles personal data. Because the weakest lens counts most in the score, work on Security and Compliance also moves the score most.
Based on: the rebuild estimate · the Security and Compliance grade · how much each lens counts
What to do: Start with the Security and Compliance actions in Top priorities.
If you do one thing first · First action
Stop accepting every TLS certificate: delete the trust manager whose `checkServerTrusted` is empty, the `HostnameVerifier` that returns `true`, `InsecureTrustManagerFactory` / `NoopHostnameVerifier` and `acceptAnyCertificate`, and trust a self-signed or private server by giving the client a trust store holding its CA — never by switching verification off. Of all the actions in this report, it raises the score most for the work it takes: about 7 points for medium effort. The next survey will show the change in the score.
Based on: the ranked actions in Top priorities, by score gain and effort

Architecture: which parts of the code depend on which

The code is divided into modules, groups of code that each handle one part of the product, and a module depends on another when it uses code from it. The table lists the modules both down the side and across the top, in an order where each module should only use modules listed above it. A number in a cell means the module on that row uses the module in that column, and it counts the pairs of building blocks (types) that connect them. The one thing to look for is a mark above the diagonal line. It means two or more modules use each other in a loop (a dependency cycle), so none of them can be changed, tested or released on its own.

114 modules, with 330 dependencies between them. One loop runs through 31 of the modules, marked above the diagonal.

The table shows the 40 modules with the most connections; 74 more are left out to keep it readable.

Which modules use which. The module on each row uses the module in each column, and the number counts the pairs of types that connect them. A cell above the diagonal is part of a loop.
uses →1 extract.spi2 job.common3 lineage.AutoTaskDependencies4 utils5 extract.impl.restapi6 job.metrics7 lineage.ColLineage8 schema9 job.sink.bigquery10 job.ingest11 job.transform12 lineage13 schema.model14 job.sink.es15 schema.model.IndexMapping16 job.infer17 job.sink.jdbc18 job.sink.kafka19 schema.generator20 workflow21 job22 extract23 extract.impl.openapi24 schema.handlers25 config.Settings26 config27 console28 extract.freshness29 job.bootstrap30 job.convert31 job.ingest.loaders32 job.quack33 job.site34 job.strategies.TransformStrategiesBuilder35 job.tools36 migration37 semantic38 serve39 serve.catalog40 job.strategies
1 extract.spi51313
2 job.common32
3 lineage.AutoTaskDependencies1
4 utils16245211
5 extract.impl.restapi133733133
6 job.metrics1645219
7 lineage.ColLineage22
8 schema1212123
9 job.sink.bigquery6211686
10 job.ingest132313522121129120
11 job.transform29531169235
12 lineage55211510267
13 schema.model15111238298319
14 job.sink.es31213
15 schema.model.IndexMapping25
16 job.infer152222
17 job.sink.jdbc4332123
18 job.sink.kafka4321112
19 schema.generator1111861061016
20 workflow514214131111115
21 job411133
22 extract8128278410422
23 extract.impl.openapi1213112
24 schema.handlers111714236
25 config.Settings31216
26 config142224
27 console11212
28 extract.freshness123122
29 job.bootstrap112112
30 job.convert311222
31 job.ingest.loaders24111149178
32 job.quack24211
33 job.site1612112
34 job.strategies.TransformStrategiesBuilder3131
35 job.tools1211
36 migration22021310
37 semantic23212
38 serve1342374
39 serve.catalog11711
40 job.strategies31111
Uses a module listed above itPart of a loopThe module itself
1 extract.spi2 job.common3 lineage.AutoTaskDependencies4 utils5 extract.impl.restapi6 job.metrics7 lineage.ColLineage8 schema9 job.sink.bigquery10 job.ingest11 job.transform12 lineage13 schema.model14 job.sink.es15 schema.model.IndexMapping16 job.infer17 job.sink.jdbc18 job.sink.kafka19 schema.generator20 workflow21 job22 extract23 extract.impl.openapi24 schema.handlers25 config.Settings26 config27 console28 extract.freshness29 job.bootstrap30 job.convert31 job.ingest.loaders32 job.quack33 job.site34 job.strategies.TransformStrategiesBuilder35 job.tools36 migration37 semantic38 serve39 serve.catalog40 job.strategiesextract.spi1job.common2lineage.AutoTaskDependencies3utils4extract.impl.restapi5job.metrics6lineage.ColLineage7schema8job.sink.bigquery9job.ingest10job.transform11lineage12schema.model13job.sink.es14schema.model.IndexMapping15job.infer16job.sink.jdbc17job.sink.kafka18schema.generator19workflow20job21extract22extract.impl.openapi23schema.handlers24config.Settings25config26console27extract.freshness28job.bootstrap29job.convert30job.ingest.loaders31job.quack32job.site33job.strategies.TransformStrategiesBuilder34job.tools35migration36semantic37serve38serve.catalog39job.strategies4051313321162452111337331331645219221212123621168613231352212112912029531169235552115102671511123829831931213251522224332123432111211118610610165142141311111154111338128278410422121311211171423631216142224112121231221121123112222411114917824211161211231311211220213102321213423741171131111+74 more modules (most-connected shown)

At a glance — Code Health · 61% · Adequate · gated by X5 ·

The Good
D3 · Scala God Classes 8.4 D4 · Scala Code Duplication 9.6 D17 · Scala Explicit Debt 9.9 GD1 · Repo Unfinished & placeholder code 9.9 IC1 · Repo Incompleteness & stubs 8.2 X1 · Repo Async correctness 10.0 X12 · Repo Unreachable branch 7.7 X13 · Repo Undrained process stream 10.0 X16 · Repo Unfloored truncation loop 10.0 X19 · Repo Unrestored process-global state 10.0 X20 · Repo Mistyped argument guard 10.0 X21 · Repo Side-effecting pattern guard 10.0 X22 · Repo Contradicted release guard 10.0 X23 · Repo Unguarded diagnostic materialisation 10.0 X25 · Repo Inert configuration knob 10.0 X26 · Repo Unsynchronised callback handoff 10.0 X27 · Repo Collection changed while being enumerated 10.0 X28 · Repo Index access outside its own emptiness guard 10.0 X29 · Repo Per-element action decided by a fixed element 10.0 X30 · Repo Support guard that admits what it rejects 10.0
The Bad
D1 · Scala Cyclomatic Complexity 6.9 D2 · Scala Cognitive Complexity 5.7 X3 · Repo Exception handling 6.6
The Ugly
X18 · Repo Disposal-pattern correctness 4.8 X5 · Repo Nullable reference types 2.0

At a glance — Architecture · 85% · Adequate · gated by D26 ·

The Good
D6 · Scala Cohesion (LCOM4) 9.5 D35 · Scala Change Coupling 8.2 AX10 · Repo Code composition 9.6 AX3 · Repo Project dependency cycles 10.0 AX4 · Repo Dependency direction 10.0 AX6 · Repo Interface segregation 9.8
The Ugly
D26 · Scala Project Cohesion 0.0

At a glance — Maturity · 71% · Adequate · gated by D16 ·

The Good
D15 · Scala Churn × Complexity Hotspots 9.7 D21 · Scala Naming Consistency 10.0 D34 · Scala Knowledge Freshness 9.6 M1 · Repo Documentation (README) 8.0 M3 · Repo Folder & project structure 10.0 M4 · Repo Documentation accuracy 10.0
The Bad
M2 · Repo Architecture documentation 5.0
The Ugly
D16 · Scala Bus Factor 3.9 D20 · Scala ADR Quality 0.0

At a glance — Readiness · 50% · Adequate · gated by D13, P3 ·

The Good
D9 · Scala Test Distribution 10.0 D12 · Scala Dependency Hygiene 8.9 D14 · Scala License Compliance 10.0 P1 · Repo CI/CD gates 10.0 P2 · Repo Observability 7.0 P4 · Repo Deployment & Rollback 7.0 P6 · Repo Release Hygiene 10.0
The Bad
P10 · Repo Library API & versioning 6.0
The Ugly
D13 · Scala REDACTED Scanning 0.0 P3 · Repo Security & performance tooling 0.0

At a glance — Security · 35% · Weak · gated by D28, D29, S1 ·

The Good
D30 · Scala Dependency Vulnerabilities 8.6 D31 · Scala IaC & Container Security 9.6 D43 · Scala Malicious Dependencies 10.0 X14 · Repo Bypassable address classification 10.0 X15 · Repo Unvalidated length from an untrusted reader 10.0 X17 · Repo Uncapped recursion over a caller-supplied document 10.0 X24 · Repo Document value interpolated into markup unescaped 10.0
The Bad
D36 · Scala Supply-chain Provenance & Signing 5.0
The Ugly
D28 · Scala Secrets (history) 0.0 D29 · Scala Static Analysis (SAST) 0.0 S1 · Repo Web-Security Posture 2.0

At a glance — Accessibility · 41% · Weak · gated by AC4 ·

The Good
AC2 · Repo Forms & labels 10.0
The Bad
AC3 · Repo Page structure 6.6
The Ugly
AC4 · Repo Keyboard semantics 1.0 AC6 · Repo Visual & motion safety 4.3 AC7 · Repo A11y enforcement 4.0

Security & Compliance — OWASP Top-10 mapping

Findings mapped to OWASP categories; the specific CVEs/secrets are in the Security dimension cards below and findings.md (redacted only on the public version of this report).

OWASP categoryFindingsSeverity
A05:2025 — Injection241High / Critical
A07:2025 — Authentication Failures40High / Critical
A03:2025 — Software Supply Chain Failures11High / Critical
A02:2025 — Security Misconfiguration2High / Critical

Roadmap

Immediately disable insecure TLS configurations by removing empty trust managers and enforcing proper certificate validation to prevent man-in-the-middle attacks. Concurrently, remediate critical secrets by cleaning hardcoded credentials from configuration files and resolving environment variable leaks in CI pipelines. Finally, ensure accessibility by making custom controls keyboard-operable and fixing anchor links to improve usability for all users.

Ranked by impact ÷ effort. "Helps" is the estimated gain on the 0–100 health score.

Do thisHelpsEffortDimension
Stop accepting every TLS certificate: delete the trust manager whose `checkServerTrusted` is empty, the `HostnameVerifier` that returns `true`, `InsecureTrustManagerFactory` / `NoopHostnameVerifier` and `acceptAnyCertificate`, and trust a self-signed or private server by giving the client a trust store holding its CA — never by switching verification off.+7.4 ptsMediumWeb-Security Posture
Resolve the 4 Leaked secret finding(s) in REDACTED Scanning — start with REDACTED (4).+3.3 ptsLowREDACTED Scanning
Resolve the 6 REDACTED finding(s) in Supply-chain Provenance & Signing — start with REDACTED, REDACTED, REDACTED.+2.6 ptsLowSupply-chain Provenance & Signing
Resolve the 30 High secret finding(s) in Secrets (history) — start with REDACTED (6), REDACTED (6), REDACTED (5).+4.7 ptsMediumSecrets (history)
Resolve the 38 REDACTED finding(s) in Static Analysis (SAST) — start with REDACTED (5), REDACTED (5), REDACTED (4).+2.1 ptsMediumStatic Analysis (SAST)
Make custom controls keyboard-operable (role + tabindex + key handler), drop positive tabindex, and give anchors a real href.+3.8 ptsMediumKeyboard semantics
Resolve the 85 REDACTED finding(s) in Static Analysis (SAST) — start with REDACTED (22), REDACTED (22), REDACTED (16).+3.7 ptsMediumStatic Analysis (SAST)
Resolve the 5 REDACTED finding(s) in Secrets (history) — start with REDACTED, REDACTED, REDACTED.+1.5 ptsLowSecrets (history)

File quality

Per-file score 0–10 — a quality signature. Of 170 files carrying findings, judged against the Production bar: 9% slop · 58% mixed · 33% near-clean.

FileScoreBandWorst signal
REDACTED0.0SlopStatic Analysis (SAST): High: REDACTED
REDACTED0.4SlopStatic Analysis (SAST): High: REDACTED
REDACTED0.7SlopStatic Analysis (SAST): High: REDACTED
REDACTED1.1SlopStatic Analysis (SAST): High: REDACTED
REDACTED1.3SlopStatic Analysis (SAST): High: REDACTED
REDACTED1.8SlopStatic Analysis (SAST): High: REDACTED
REDACTED2.3SlopStatic Analysis (SAST): High: REDACTED
REDACTED2.3SlopStatic Analysis (SAST): High: REDACTED
REDACTED2.3SlopStatic Analysis (SAST): High: REDACTED
REDACTED2.3SlopStatic Analysis (SAST): High: REDACTED
REDACTED2.5SlopStatic Analysis (SAST): High: REDACTED
REDACTED2.7SlopStatic Analysis (SAST): High: REDACTED
REDACTED2.9SlopStatic Analysis (SAST): High: REDACTED
REDACTED3.0SlopStatic Analysis (SAST): High: REDACTED
REDACTED3.6SlopStatic Analysis (SAST): High: REDACTED
REDACTED4.0MixedStatic Analysis (SAST): Medium: REDACTED
REDACTED4.4MixedStatic Analysis (SAST): High: REDACTED
REDACTED4.5MixedSecrets (history): High secret: REDACTED
REDACTED4.6MixedStatic Analysis (SAST): High: REDACTED
REDACTED5.1MixedSecrets (history): High secret: REDACTED

How the grades work

Every finding carries one of four grades. Three say how serious it is. The fourth says this survey could not settle it — and it is a grade, not a gap.

Critical — 279

A definite problem that already costs you something and drags the score down: a missing authorisation check, a dependency with a known exploit, a build that does not reproduce. Failure here tends to cause failures elsewhere.

Serious — 352

Likely wrong, but not failing yet. It degrades the codebase over a longer horizon and can cause failures elsewhere — not urgent this week, not something to carry for two years either.

Minor — 67

Recorded, with no effect on how the codebase functions. Present so the survey is complete, not because it needs doing.

Could not be resolved — 15

Something this survey could not settle from the outside, and which could be critical or serious. Either a control was required and no positive evidence of it exists in the repository — a backup job that nothing shows was ever restored from proves nothing about restores — or our own analysis could not run over that part of the tree. This is not a clean result. These are excluded from the score rather than awarded a pass, so the number on the cover neither rewards nor penalises them: if you act on this survey without resolving them, you carry that risk yourself. Each one is named under Limitations.

Methodology & how to trust this report

Watchdog is a deep, periodic assessment — run each sprint, monthly, or quarterly, taking the time to go wider and deeper than a quick check and surfacing in one coherent report what you'd otherwise piece together from a dozen separate tools. It scores deterministically: the same commit yields the same score, every run. 64 of 67 evaluated dimensions are computed purely by tools and static analysis (confidence 1.0); 3 documentation/naming judgement(s) are LLM-assisted and labelled advisory. Overall confidence is 0.8 — the weighted average across measured dimensions; it falls as more of the score leans on LLM-assisted judgement and rises when it's fully tool-backed.

Every figure here is one of three kinds, and we label which: ✓ Measured — a deterministic fact (LoC, complexity, coverage); ~ Modeled — an estimate from a stated model (cost, effort, value-at-risk), always a range with its assumptions, never a precise fact; ◐ Advisory — an LLM prose judgement. We never present a modelled estimate as if it were measured. Perfect or absent scores carry their provenance too (ADR-0011): ✓ Tool-verified means the property itself was measured across the surface; ○ Nothing flagged means the probes came back clean — a claim bounded by what a repository can show; ⊘ Not evidenced means a working control (a tested restore, an automated rollback) showed no positive evidence — absence of evidence is not evidence of a control, so it's excluded from the score rather than awarded a spurious 10; ◐ Sampled · advisory marks an LLM verdict over a bounded sample — advisory, never a deterministic measurement.

What we checked — 67 dimensions across the health lenses
D1D2D3D4D6D9D12D13D14D15D16D17D20D21D26D28D29D30D31D34D35D36D43AC2AC3AC4AC6AC7AX10AX3AX4AX6GD1IC1M1M2M3M4P1P10P2P3P4P6S1X1X12X13X14X15X16X17X18X19X20X21X22X23X24X25X26X27X28X29X3X30X5

Each chip is a dimension scored from real signals across architecture, testing, dependencies, security & compliance, documentation, git-history and code quality — in one coherent pass. A surface report typically covers a handful.

How to trust any code-health report — three questions
  1. Can you open the finding? Real findings cite a repo-relative file and line you can open at the cited line — never an absolute scratch path. Here, 668 of 698 do; the remainder are repo-wide signals — a dimension-level measurement, not a single line. (Every path in this report is repo-relative by construction: paths are normalized at the producer and the report is rejected if any rooted path leaks through.)
  2. Is there a tool behind the number? Every score below names the method that produced it — Roslyn, git, a scanner, or (for a handful of documentation/naming dimensions) an LLM labelled sampled · advisory — not a narrative.
  3. Does re-running give the same result? Run it again on the same commit and the score — and this report, byte for byte — is identical. A report whose numbers move between runs is describing the run, not the code.

This report answers yes to all three. That's the bar to hold any assessment to.

Tools & methods

The actual versions used this run (captured at analysis time) — re-run on the same commit for the identical score.

MethodBacksVersionEvaluator
Roslyn static analysisComplexity, cohesion, coupling, dead code, API surface, layering5.3.0✓ deterministic
Native secret scannerHardcoded secrets / credentials1.0.0✓ deterministic
Watchdog duplication detector (in-process)Code duplication1.0.0✓ deterministic
Coverage (coverlet / dotnet-coverage)Line & branch coverage10.0.400✓ deterministic
NuGet / dotnetOutdated, vulnerable & deprecated dependencies10.0.400✓ deterministic
git / LibGit2SharpChurn hotspots, knowledge concentration, history2.43.0 · 0.31.0✓ deterministic
gitleaks · semgrep · trivy · checkovSecrets in history, SAST, CVEs, IaC & container, PII / GDPR1.86.0 · 0.69.3 · 3.2.533✓ deterministic
LLM (sampled · advisory)Documentation quality, ADR conformance, naming — sampled over a bounded sample; advisory, never a deterministic measurementLocal LLM◐ LLM · sampled · advisory

Every finding is locatable in findings.md. Run 01a15e0e-bf24-75f2-8d97-a1e51a4b6328.

The exact command behind every deep-scan dimension — tool, version, invocation and retained raw output — is in Appendix B — Reproduction & audit trail.

Run transparency — what happened this run

What ran differently this time — a tool absent, degraded, or that fell back to an estimate. Named openly, not folded silently into the scores. A degraded run also records its exact cause in diagnostics.md.

  • D8 Code Coverage — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. Coverage NOT MEASURED: test source is present (.scala) but the built-in coverage collector has no runner for this repository's ecosystem — so this suite was never executed by it. Not scored — this is a gap in the analyzer's language coverage, not a defect in the repo. To have real coverage read, produce a coverage report in a standard format (`addSbtPlugin("org.scoverage" % "sbt-scoverage" % "<version>")` in `project/plugins.sbt`, then `sbt clean coverage test coverageReport`) and commit it — a hosted scan measures a clone of the repository, so a report that exists only in a working tree, a CI runner's or your own, never reaches it; the artefact is commonly gitignored, so `git add -f` that one file (or un-ignore its path) and commit it alongside the code it measures. You can widen what we reach: optional: produce a coverage report in a standard format (`addSbtPlugin("org.scoverage" % "sbt-scoverage" % "<version>")` in `project/plugins.sbt`, then `sbt clean coverage test coverageReport`) and commit it — a hosted scan measures a clone of the repository, so a report that exists only in a working tree, a CI runner's or your own, never reaches it; the artefact is commonly gitignored, so `git add -f` that one file (or un-ignore its path) and commit it alongside the code it measures — then the real number is read on the next scan.
  • D11 Test Reliability — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. Test source is present (.scala) and this repository declares an sbt build (repository root, 189 test files), but it was not re-run: the analyzer environment could not run it. Not scored — this is a gap in the analyzer's language coverage, not a finding about this repository.
  • D19 Documentation Quality — LLM provider failed — The model provider returned an unusable result, so this LLM-assisted dimension fell back to a measurement gap (confidence 0) rather than a penalty. Re-run with a reachable provider to score it.
  • D22 Internal API Consistency — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. D22 identifies the intentionally-exposed surface from `IsPackable` and `.Contracts` project names, MSBuild conventions read off the loaded project set. This target exposed no such projects, so the probe never ran; this says nothing about whether the repository has a public API. This repository commits no C#/VB source at all, so there was never an MSBuild project set to read these conventions off. That is OUR side and it is an ADMISSION gap, not an environment fault: no published-package marker D22 reads admitted any project here. D22 DOES collect the public API of every language in this tree, and admits a project only when its manifest makes that ecosystem's publication act in a form the marker recognises — so what is missing is not a collector but an admission rule for the publication act this repository makes, if it makes one. The remedy is a better discriminator for that marker — not a collector, and no change to the scan image can close it. The marker's rules are measured to refuse applications, so one refused repository is the rule working as calibrated; several are the evidence that it is too strict. For npm the surface is what a published package.json's entry points (exports, main, module, types) export, followed through the source; an entry that names build output the tree does not commit (dist/, build/) is not followed back to source, because only a tsconfig outDir/rootDir pair states that mapping and a bundler's input lives in executable configuration this engine does not evaluate.
  • D27 Navigability — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. Navigability is taken over call sites whose callee resolved. For C# that population includes calls into referenced assemblies; for this language only calls declared inside the repository count, so the abstention threshold reads the repository's use of its standard library as a resolution failure. That is a limit of this analyzer, not a build problem in this repository.
  • D32 Data Compliance (PII/GDPR) — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. `src/main/scala/ai/starlake/job/bootstrap/Bootstrap.scala`, `src/main/scala/ai/starlake/schema/handlers/SchemaHandler.scala`, `src/main/scala/ai/starlake/schema/model/Sink.scala` produced a parse error, so every rule in this engine's `gdpr.yml` was absent there. That absence is NOT a clean result: these rules detect personal data crossing a boundary into a log sink, a URL or browser storage, and a file that was never parsed cannot report any of the three. The rest of the tree analysed normally and its rows above stand; only these files are unaccounted for. You can widen what we reach: fix the syntax error (or exclude the file deliberately) and re-scan to cover it.
  • D44 Platform End-of-Life — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. This dimension reads a project's own statement about the platform it runs on: a TargetFramework in a .NET project file, a .nvmrc or .python-version, a capped requires-python, a Rust toolchain file or Cargo.toml rust-version, a .go-version, .java-version, .ruby-version, .tool-versions or .sdkmanrc, a go.mod go directive, a Maven or Gradle Java level or toolchain, a Gemfile's ruby directive, a mix.exs elixir requirement, a rebar.config minimum_otp_vsn, a pubspec.yaml SDK constraint, a build.sbt scalaVersion, or a framework major pinned by a dependency constraint. This repository carries none of them, so nothing about its platform was established. That is a gap in this analyzer's coverage, NOT a finding that the platform is supported — a language whose runtime is declared elsewhere (Package.swift, a REDACTED) is simply not read here yet.
  • AC1 Text alternatives — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. This repository renders user interface from a TEMPLATE LANGUAGE — 2 Jinja template file(s) carrying 258 markup element(s) (e.g. src/main/resources/templates/site/standalone/table.j2, src/main/resources/templates/site/standalone/task.j2) — and the accessibility cards on this run were measured over the 3 parsed markup document(s) ONLY. The markup model reads .html/.htm/.cshtml/.razor/.vue/.svelte/.jsx/.tsx, html`…` templates and DOM-assigned HTML strings in .ts/.js, the .erb/.ejs/.eex/.heex/.leex/.twig/.jinja/.j2/.liquid/.njk/.hbs/.mustache/.blade.php/.gohtml template languages, .astro/.templ/.pug/.jade/.haml/.slim, and the kotlinx.html/Compose HTML/kotlin-react, Giraffe/Falco/Fable.React/Feliz/Bolero and Jaspr builder DSLs; the file(s) above were REFUSED rather than unsupported — a template whose element NAME comes from an expression, or an Astro component whose frontmatter fence is never closed, leaves the element's identity unknown, and every accessibility rule is a statement about a NAMED element — so those views were not merely unparsed but unenumerated: not one of their elements entered the population any card scored. Their absence from the counts above is a gap in this analyzer's frontend coverage — not a finding that they carry text alternatives, labels, keyboard semantics or correct ARIA.
  • AC2 Forms & labels — measured, with a gap in what it reached — Watchdog measured this, but not all of it. What it did not reach is a gap on our side — a collector, parser or image we have not built yet — so the numbers on that dimension cover less than the repository, and the part left out is not evidence that it would have passed. This repository renders user interface from a TEMPLATE LANGUAGE — 2 Jinja template file(s) carrying 258 markup element(s) (e.g. src/main/resources/templates/site/standalone/table.j2, src/main/resources/templates/site/standalone/task.j2) — and the accessibility cards on this run were measured over the 3 parsed markup document(s) ONLY. The markup model reads .html/.htm/.cshtml/.razor/.vue/.svelte/.jsx/.tsx, html`…` templates and DOM-assigned HTML strings in .ts/.js, the .erb/.ejs/.eex/.heex/.leex/.twig/.jinja/.j2/.liquid/.njk/.hbs/.mustache/.blade.php/.gohtml template languages, .astro/.templ/.pug/.jade/.haml/.slim, and the kotlinx.html/Compose HTML/kotlin-react, Giraffe/Falco/Fable.React/Feliz/Bolero and Jaspr builder DSLs; the file(s) above were REFUSED rather than unsupported — a template whose element NAME comes from an expression, or an Astro component whose frontmatter fence is never closed, leaves the element's identity unknown, and every accessibility rule is a statement about a NAMED element — so those views were not merely unparsed but unenumerated: not one of their elements entered the population any card scored. Their absence from the counts above is a gap in this analyzer's frontend coverage — not a finding that they carry text alternatives, labels, keyboard semantics or correct ARIA.
  • AC3 Page structure — measured, with a gap in what it reached — Watchdog measured this, but not all of it. What it did not reach is a gap on our side — a collector, parser or image we have not built yet — so the numbers on that dimension cover less than the repository, and the part left out is not evidence that it would have passed. This repository renders user interface from a TEMPLATE LANGUAGE — 2 Jinja template file(s) carrying 258 markup element(s) (e.g. src/main/resources/templates/site/standalone/table.j2, src/main/resources/templates/site/standalone/task.j2) — and the accessibility cards on this run were measured over the 3 parsed markup document(s) ONLY. The markup model reads .html/.htm/.cshtml/.razor/.vue/.svelte/.jsx/.tsx, html`…` templates and DOM-assigned HTML strings in .ts/.js, the .erb/.ejs/.eex/.heex/.leex/.twig/.jinja/.j2/.liquid/.njk/.hbs/.mustache/.blade.php/.gohtml template languages, .astro/.templ/.pug/.jade/.haml/.slim, and the kotlinx.html/Compose HTML/kotlin-react, Giraffe/Falco/Fable.React/Feliz/Bolero and Jaspr builder DSLs; the file(s) above were REFUSED rather than unsupported — a template whose element NAME comes from an expression, or an Astro component whose frontmatter fence is never closed, leaves the element's identity unknown, and every accessibility rule is a statement about a NAMED element — so those views were not merely unparsed but unenumerated: not one of their elements entered the population any card scored. Their absence from the counts above is a gap in this analyzer's frontend coverage — not a finding that they carry text alternatives, labels, keyboard semantics or correct ARIA.
  • AC4 Keyboard semantics — measured, with a gap in what it reached — Watchdog measured this, but not all of it. What it did not reach is a gap on our side — a collector, parser or image we have not built yet — so the numbers on that dimension cover less than the repository, and the part left out is not evidence that it would have passed. This repository renders user interface from a TEMPLATE LANGUAGE — 2 Jinja template file(s) carrying 258 markup element(s) (e.g. src/main/resources/templates/site/standalone/table.j2, src/main/resources/templates/site/standalone/task.j2) — and the accessibility cards on this run were measured over the 3 parsed markup document(s) ONLY. The markup model reads .html/.htm/.cshtml/.razor/.vue/.svelte/.jsx/.tsx, html`…` templates and DOM-assigned HTML strings in .ts/.js, the .erb/.ejs/.eex/.heex/.leex/.twig/.jinja/.j2/.liquid/.njk/.hbs/.mustache/.blade.php/.gohtml template languages, .astro/.templ/.pug/.jade/.haml/.slim, and the kotlinx.html/Compose HTML/kotlin-react, Giraffe/Falco/Fable.React/Feliz/Bolero and Jaspr builder DSLs; the file(s) above were REFUSED rather than unsupported — a template whose element NAME comes from an expression, or an Astro component whose frontmatter fence is never closed, leaves the element's identity unknown, and every accessibility rule is a statement about a NAMED element — so those views were not merely unparsed but unenumerated: not one of their elements entered the population any card scored. Their absence from the counts above is a gap in this analyzer's frontend coverage — not a finding that they carry text alternatives, labels, keyboard semantics or correct ARIA.
  • AC5 ARIA correctness — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. This repository renders user interface from a TEMPLATE LANGUAGE — 2 Jinja template file(s) carrying 258 markup element(s) (e.g. src/main/resources/templates/site/standalone/table.j2, src/main/resources/templates/site/standalone/task.j2) — and the accessibility cards on this run were measured over the 3 parsed markup document(s) ONLY. The markup model reads .html/.htm/.cshtml/.razor/.vue/.svelte/.jsx/.tsx, html`…` templates and DOM-assigned HTML strings in .ts/.js, the .erb/.ejs/.eex/.heex/.leex/.twig/.jinja/.j2/.liquid/.njk/.hbs/.mustache/.blade.php/.gohtml template languages, .astro/.templ/.pug/.jade/.haml/.slim, and the kotlinx.html/Compose HTML/kotlin-react, Giraffe/Falco/Fable.React/Feliz/Bolero and Jaspr builder DSLs; the file(s) above were REFUSED rather than unsupported — a template whose element NAME comes from an expression, or an Astro component whose frontmatter fence is never closed, leaves the element's identity unknown, and every accessibility rule is a statement about a NAMED element — so those views were not merely unparsed but unenumerated: not one of their elements entered the population any card scored. Their absence from the counts above is a gap in this analyzer's frontend coverage — not a finding that they carry text alternatives, labels, keyboard semantics or correct ARIA.
  • AC6 Visual & motion safety — measured, with a gap in what it reached — Watchdog measured this, but not all of it. What it did not reach is a gap on our side — a collector, parser or image we have not built yet — so the numbers on that dimension cover less than the repository, and the part left out is not evidence that it would have passed. This repository renders user interface from a TEMPLATE LANGUAGE — 2 Jinja template file(s) carrying 258 markup element(s) (e.g. src/main/resources/templates/site/standalone/table.j2, src/main/resources/templates/site/standalone/task.j2) — and the accessibility cards on this run were measured over the 3 parsed markup document(s) ONLY. The markup model reads .html/.htm/.cshtml/.razor/.vue/.svelte/.jsx/.tsx, html`…` templates and DOM-assigned HTML strings in .ts/.js, the .erb/.ejs/.eex/.heex/.leex/.twig/.jinja/.j2/.liquid/.njk/.hbs/.mustache/.blade.php/.gohtml template languages, .astro/.templ/.pug/.jade/.haml/.slim, and the kotlinx.html/Compose HTML/kotlin-react, Giraffe/Falco/Fable.React/Feliz/Bolero and Jaspr builder DSLs; the file(s) above were REFUSED rather than unsupported — a template whose element NAME comes from an expression, or an Astro component whose frontmatter fence is never closed, leaves the element's identity unknown, and every accessibility rule is a statement about a NAMED element — so those views were not merely unparsed but unenumerated: not one of their elements entered the population any card scored. Their absence from the counts above is a gap in this analyzer's frontend coverage — not a finding that they carry text alternatives, labels, keyboard semantics or correct ARIA.
  • AXR1 Runtime accessibility — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. Render strategy: boot via REDACTED (REDACTED). docker build failed (exit 1) — DEPRECATED: The legacy builder is deprecated and will be removed in a future release. Install the buildx component to build images with BuildKit: https://docs.docker.com/go/bui…; runtime evidence skipped.
  • C1 Data Protection — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. These personal data controls are read from declarative annotations, request middleware, entity/column names and guard methods in a C# source model, and none was loaded on this run, so there was nothing to gather. That is a gap in this analyzer's language reach — not a finding that the repository lacks personal data controls.
  • C2 Access Controls — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. These authorization controls are read from declarative annotations, request middleware, entity/column names and guard methods in a C# source model, and none was loaded on this run, so there was nothing to gather. That is a gap in this analyzer's language reach — not a finding that the repository lacks authorization controls.
  • C3 Audit Trail — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. 'java.sql.DriverManager' is imported (src/main/scala/ai/starlake/config/Settings.scala:53), but no entity, schema, table or model declaration this check reads was found beside it, so whether personal data is stored could not be decided.
  • C4 Data Retention — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. 'java.sql.DriverManager' is imported (src/main/scala/ai/starlake/config/Settings.scala:53), but no entity, schema, table or model declaration this check reads was found beside it, so whether personal data is stored could not be decided.
  • C5 Data-Subject Rights — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. 'java.sql.DriverManager' is imported (src/main/scala/ai/starlake/config/Settings.scala:53), but no entity, schema, table or model declaration this check reads was found beside it, so whether personal data is stored could not be decided.

Repo exclusion declarations (.gitattributes linguist-generated/vendored, .editorconfig generated_code): none declared — every source file was scored.

Limitations & what we did not check

Watchdog assesses the repository exactly as committed, and only the repository. By design it does not reach outside the source tree: the live cloud account, the running CI/CD pipeline, the host's branch-protection and approval rules, the production configuration, or a restore actually exercised against a backup are all out of scope. That boundary is a feature, not a gap — a repo-relative, deterministic scan re-runs identically on any commit and every finding opens at a real file and line, where a live audit can neither be reproduced nor traced. The visible consequence is that controls which leave no in-repo evidence are reported as "not evidenced" and excluded from the score rather than awarded a number a static scan cannot justify.

Per-dimension blind spots

For each dimension that was measured, what a static, repo-only scan structurally cannot see — the honest edge of the measurement, not a failure of it.

  • D1 Cyclomatic Complexity: Cyclomatic complexity counts branches statically — it cannot tell an essential decision tree from accidental tangle, nor see complexity that lives in data or configuration (large switch-case token tables, DSL lexers/parsers, data-as-code rule tables) rather than control flow: a tokenizer's many single-character cases read as high complexity though each branch is trivial.
  • D2 Cognitive Complexity: Cognitive-complexity heuristics approximate how hard code is to follow; genuine domain difficulty and well-named intent that eases reading are not captured.
  • D3 God Classes: "God class" is sized by members and responsibilities visible in the type — a deliberately broad facade over a coherent subsystem can read the same as an accidental grab-bag. For front-end JS the file-length check is cohesion-aware (a single-responsibility module — one class/IIFE — earns a 3× threshold), but cohesion is approximated from top-level declarations, not true dependency structure.
  • D4 Code Duplication: Duplication is token-similarity — an in-process token-stream comparison over sliding windows, with type-aware normalization — so it finds copy-paste, not semantic duplication expressed differently. Committed machine-written code (scaffolded migrations, designer/codegen output, protobuf/OpenAPI stubs, model snapshots) is EXCLUDED — its repetition is the tool's, not the team's — so the score reflects hand-written duplication only.
  • D6 Cohesion (LCOM4): LCOM4 cohesion is syntactic — it infers connectivity from which methods touch which fields/methods by name, not from real runtime behaviour or intent.
  • D9 Test Distribution: The test-pyramid shape is inferred from project/folder naming and references, with a single test host bucketed per-file by its path tier and content signals — a suite that names tiers unconventionally and gives no per-file signal can still be mis-bucketed.
  • D12 Dependency Hygiene: Dependency health reads manifests and lockfiles — a vulnerability in a vendored/copied dependency, or risk from how a dependency is actually used, is outside this view.
  • D13 REDACTED Scanning: REDACTED detection is signature- and entropy-based on the current tree — a secret that does not match a known pattern, or one already rotated, will not be flagged (a clean scan is "nothing matched", not "no secrets exist").
  • D14 License Compliance: License compatibility is checked against declared package metadata and a policy — mislabelled or missing license metadata, and obligations that depend on how you distribute, are not resolved here.
  • D15 Churn × Complexity Hotspots: Churn hotspots come from git history — a freshly imported or squashed repository has no churn signal, and recent rewrites can mask a historically risky file.
  • D16 Bus Factor: Bus-factor is a time-decayed model of commit attribution (who has recently, repeatedly worked a file), not comprehension — pairing, review and reading-without-committing spread knowledge it can't see; bot commits and shared accounts still distort it.
  • D17 Explicit Debt: Acknowledged-debt signals (TODO/FIXME, suppressions, dead code) are textual — undocumented debt that nobody marked, and debt that lives in design rather than annotations, is invisible. Committed machine-written code (scaffolded migrations, designer/codegen output, generated stubs) is excluded — it is never the team's dead code to delete.
  • D20 ADR Quality: ADR quality is an LLM read of the decision records present — it cannot know about decisions made and never recorded, and its verdict is sampled and advisory.
  • D21 Naming Consistency: Naming quality is an LLM judgement over a bounded sample — it assesses clarity/consistency of the names it sees, not domain-correctness, and is advisory.
  • D26 Project Cohesion: Project focus is sized from members/namespaces per project — a project that is broad by deliberate design reads the same as one that has sprawled.
  • D28 Secrets (history): Secret-history scanning sweeps the git log for known patterns — a secret that predates the available history, or never matched a signature, is not found (clean means "nothing matched in the history we can see").
  • D29 Static Analysis (SAST): SAST findings are pattern-based (semgrep) — it finds classes of bug it has rules for; logic flaws, auth/authorization gaps and issues needing runtime context are out of reach (and clean means "no rule matched").
  • D30 Dependency Vulnerabilities: CVE matching depends on accurate package/version metadata and on the advisory databases — a vulnerability with no published advisory, or in code not declared as a dependency, is not seen. Coverage needs a RESOLVED graph: an unpinned requirements.txt, or a pom without a resolved build, yields partial coverage rather than a clean verdict. An ecosystem the analyzer cannot scan is reported as unmeasured, never as clean.
  • D31 IaC & Container Security: IaC scanning checks Dockerfiles/Terraform/Kubernetes against best-practice rules — it cannot see the live cloud account, runtime configuration, or drift between the committed config and what is actually deployed.
  • D34 Knowledge Freshness: Freshness is decayed commit RECENCY, not comprehension — code read often but rarely committed reads as orphaned, and stable code that genuinely needs no changes is penalised the same as forgotten code; bot/squash commits distort it like the bus factor.
  • D35 Change Coupling: Change coupling is co-change in COMMITS — files split across separate commits, or coupled only through a shared config/build step, read as uncoupled, and a sweeping commit (rename/format) is excluded so it doesn't couple everything. It shows that files change together, not WHY: a high coupling can be a healthy cohesive pair as readily as a hidden leak.
  • D43 Malicious Dependencies: Only packages some vulnerability database has already NAMED as malicious are seen — a compromise published in the last hours, or never reported at all, is invisible here, and this dimension reading 10 is not evidence that a dependency is trustworthy. There is no typosquat or dependency-confusion analysis: a package nobody has reported is simply absent from the feeds. Coverage is the dependency scan's: an ecosystem that could not be scanned is disclosed as unmeasured, never as clean.
  • AC2 Forms & labels: Label association is read from static markup — a label wired up at runtime (JS-set aria-labelledby, framework-injected ids) reads as missing, a present label says nothing about whether its text is correct. A known UI-library field component (e.g. a JSX <TextField>) is now checked conservatively — flagged only when it carries NO label/aria-label/aria-labelledby/id/name — but wrapper/context-labelled libraries (Chakra/Radix FormControl+FormLabel) aren't statically visible (possible false positive) and non-JSX lowercased components are still skipped. A click handler on a plain element is now asked for a name too (it is a control the author declared), but the subtree test that answers it is deliberately generous: any DYNAMIC text expression in the subtree counts as a name, so an icon chosen by a ternary ({cond ? <IconA/> : <IconB/>}) reads as named, and a glyph component from a library the icon-import list does not know still names its parent. A clean result is "no unlabelled control found", not a labelling proof.
  • AC3 Page structure: Page structure is read from the static markup tree — landmarks, headings and lang injected at runtime aren't seen, heading ORDER is checked structurally (not against the rendered visual hierarchy), and lang/title/main fire only on full documents, never partials, and the data-table check sees header-cell presence (a <th> exists), not whether each header correctly associates with its cells. Static readiness, not conformance.
  • AC4 Keyboard semantics: Keyboard semantics are inferred from markup attributes — interactivity wired purely in script, focus managed at runtime, and component-level handlers are invisible. A clean result means "no static keyboard-trap shape", not a keyboard-operability proof.
  • AC6 Visual & motion safety: Contrast and motion safety are PARTIAL by construction — literal colours (hex/rgb/hsl/named) in inline styles, in-repo <style> blocks, in-repo .css files, var() tokens, Tailwind neutral utilities and CSS-in-JS top-level declarations are read (same-rule/same-element colour+background pairs only); computed/runtime/theme colour, external-CDN stylesheets, CSS-in-JS dynamic (${…}) and nested-selector colours, cross-element pairs and image contrast stay out of reach, so a clean result is bounded by what the static CSS itself shows.
  • AC7 A11y enforcement: Enforcement is scored from in-repo config/CI evidence only — an a11y gate enforced in external tooling with no in-repo trace can't be credited, and a configured linter is presence, not proof the rules actually run or block a merge.
  • AX10 Code composition: Role is inferred from namespace/folder convention, not semantics — a domain concept living in a folder named "Services" reads as application, and the split is lines-of-code, not business value. The business-logic-share score is a SOFT, FLOORED signal: it contributes to the Architecture lens but is floored at the Critical gate, so an infrastructure-heavy design (a gateway, an ETL, a driver) is legitimately low without being nuked to zero.
  • M4 Documentation accuracy: Onboarding quality is an LLM read of the docs/setup present — it cannot run the onboarding or measure how long a real new joiner takes; the verdict is sampled and advisory.
  • P4 Deployment & Rollback: Approval/branch-protection rules live in repository settings the scan cannot see — only their in-repo evidence (config files, workflows) is checked, so a control enforced purely in the host's settings reads as "not evidenced".
  • P6 Release Hygiene: Rollback/observability controls are inferred from repo artefacts (pipelines, dashboards-as-code) — controls configured in external tooling, with no in-repo trace, cannot be credited.

The LLM boundary

LLM-set scores this run (3): D21, D26, M4 (model: Local LLM). For these, a model reads a bounded sample and sets the numeric score; each names its own sample and method on its card. They are sampled and advisory by design: they vary at the margins between runs and are never a deterministic measurement. Every other score in this report is tool-computed at confidence 1.0.

Dimensions

D1 · Cyclomatic Complexity6.9 / 10Adequate✓ Tool-verified

What it measures: How tangled the control flow is — methods with many branches are hard to test and change.

Method: Cyclomatic complexity per method (1 + decision points), computed exhaustively across production source; test projects separated by convention. Deterministic.

Maturity: Documented → Verified → Prevented · effective 6.9 / 10 · rule-coverage 100% · ceiling Prevented

41 method(s) exceeded the cyclomatic complexity threshold of 15; the worst was BigQueryAutoTask.runBQ at 41. A further 5 method(s) were over the threshold but excluded as flat dispatchers (a long switch/match over independent cases: many branches, almost no nesting), the largest being IndexMapping.fromString at 26 — they are counted neither in the figure above nor in this dimension's score. 4 files carry no cyclomatic complexity row at all for this reason — every one of their over-threshold methods was excluded, so the exclusion is disclosed nowhere in the file itself: src/main/scala/ai/starlake/schema/model/IndexMapping.scala (IndexMapping.fromString at 26), src/main/scala/ai/starlake/schema/handlers/InferSchemaHandler.scala (DataTypesToInt.typeIntToTypeStr at 20), REDACTED (JdbcDbUtils.getCommonJDBCType at 17), src/main/scala/ai/starlake/utils/conversion/BigQueryUtils.scala (BigQueryUtils.anyRefToAny at 16). They are named here because the per-file figures other dimensions report are taken BEFORE this exclusion, so such a file can show a high maximum complexity elsewhere in this report and nothing here, with nothing to reconcile the two.

BigQueryAutoTask.runBQ (cyclomatic 41)src/main/scala/ai/starlake/job/transform/BigQueryAutoTask.scala:321
RestAPIDataExtractor.extractEndpointData (cyclomatic 39)src/main/scala/ai/starlake/extract/impl/restapi/RestAPIDataExtractor.scala:67
ConnectionInfo.checkValidity (cyclomatic 36)src/main/scala/ai/starlake/config/ConnectionInfo.scala:220
OpenAPISchemaExtractor.openAPISchemaToStarlakeSchema (cyclomatic 35)src/main/scala/ai/starlake/extract/impl/openapi/OpenAPISchemaExtractor.scala:738
JdbcAutoTask.runJDBC (cyclomatic 31)src/main/scala/ai/starlake/job/transform/JdbcAutoTask.scala:204

+ 36 more group(s) — more in Appendix A; the complete list is findings.md.

What to do

  1. Bring the 41 bodies over 15 down to 15 or less in Cyclomatic Complexity — start with BigQueryAutoTask.runBQ (cyclomatic 41), RestAPIDataExtractor.extractEndpointData (cyclomatic 39), ConnectionInfo.checkValidity (cyclomatic 36). — This score is capped by its worst body, so a finding fixed alone moves it by almost nothing — the next one down takes its place. Refactoring these 41 together lifts Cyclomatic Complexity from 6.9 to about 10.0/10, projected with the scoring formula itself and assuming each lands exactly at 15.
  2. Enforce Cyclomatic Complexity in CI to reach Verified (currently Documented). — Hardens enforcement from Documented toward Prevented — provenance only; does not change the score.

Detailed fixes: d1_recommendation.md · top locations in Appendix A, every location in findings.md.

D2 · Cognitive Complexity5.7 / 10Adequate✓ Tool-verified

What it measures: How hard the code is for a person to follow, beyond raw branching.

Method: Cognitive complexity per method (Sonar-style nesting-penalized score), computed exhaustively over production code, excluding test projects. Deterministic.

Maturity: Documented → Verified → Prevented · effective 5.7 / 10 · rule-coverage 100% · ceiling Prevented

86 method(s) exceeded the cognitive complexity threshold of 15; the worst was RestAPIDataExtractor.extractEndpointData at 90.

RestAPIDataExtractor.extractEndpointData (cognitive 90)src/main/scala/ai/starlake/extract/impl/restapi/RestAPIDataExtractor.scala:67
GenericIngestionJob.loadDataSet (cognitive 82)src/main/scala/ai/starlake/job/ingest/GenericIngestionJob.scala:172
BigQueryAutoTask.runBQ (cognitive 82)src/main/scala/ai/starlake/job/transform/BigQueryAutoTask.scala:321
ConnectionInfo.checkValidity (cognitive 74)src/main/scala/ai/starlake/config/ConnectionInfo.scala:220
InferSchemaJob.parseJsonSchema (cognitive 59)src/main/scala/ai/starlake/job/infer/InferSchemaJob.scala:520

+ 81 more group(s) — more in Appendix A; the complete list is findings.md.

What to do

  1. Bring the 4 bodies over 60 down to 60 or less in Cognitive Complexity — start with RestAPIDataExtractor.extractEndpointData (cognitive 90), GenericIngestionJob.loadDataSet (cognitive 82), BigQueryAutoTask.runBQ (cognitive 82). — This score is capped by its worst body, so a finding fixed alone moves it by almost nothing — the next one down takes its place. Refactoring these 4 together lifts Cognitive Complexity from 5.7 to about 6.2/10, projected with the scoring formula itself and assuming each lands exactly at 60; a cleaner split scores higher.
  2. Enforce Cognitive Complexity in CI to reach Verified (currently Documented). — Hardens enforcement from Documented toward Prevented — provenance only; does not change the score.

Detailed fixes: d2_recommendation.md · top locations in Appendix A, every location in findings.md.

D3 · God Classes8.4 / 10Strong✓ Tool-verified

What it measures: Over-large classes that try to do too much ("god classes").

Method: God-class detection by line and method-count thresholds per logical type (partial classes unified), filtered for generated code and registration/contract false positives. Deterministic.

Maturity: Documented → Verified → Prevented · effective 8.4 / 10 · rule-coverage 100% · ceiling Prevented

28 god class(es) detected.

FileTooLong: handlers/SchemaHandler.scala · ×16src/main/scala/ai/starlake/schema/handlers/SchemaHandler.scala
TooManyMethods: SchemaHandler · ×8src/main/scala/ai/starlake/schema/handlers/SchemaHandler.scala:103
ClassTooLong: Setup · ×3REDACTED:111
MethodTooLong: Yml2DDLJob.withDdlsrc/main/scala/ai/starlake/schema/generator/Yml2DDLJob.scala:63

What to do

  1. Resolve the 16 FileTooLong finding(s) in God Classes — start with SchemaHandler.scala, BigQueryJobBase.scala, REDACTED. — One of this dimension's main actionable groups (16 warning-level).
  2. Resolve the 8 TooManyMethods finding(s) in God Classes — start with SchemaHandler.scala, DatasetArea.scala, REDACTED. — One of this dimension's main actionable groups (8 warning-level).
  3. Resolve the 3 ClassTooLong finding(s) in God Classes — start with REDACTED, XlsDomainReader.scala, BigQueryNativeJob.scala. — One of this dimension's main actionable groups (3 warning-level).
  4. Enforce God Classes in CI to reach Verified (currently Documented). — Hardens enforcement from Documented toward Prevented — provenance only; does not change the score.

Detailed fixes: d3_recommendation.md · top locations in Appendix A, every location in findings.md.

D4 · Code Duplication9.6 / 10Stronggated by 60 serious findings✓ Tool-verified

What it measures: Copy-pasted code that should be shared instead.

Method: Code duplication via token-stream sliding windows with type-aware normalization (locals masked, type names preserved), density-scored per KLoC of production code. Deterministic.

Maturity: Documented → Verified → Prevented · effective 9.6 / 10 · rule-coverage 100% · ceiling Verified

56 duplicated block group(s) detected. A further 4 rows report members as variants of one another; they aggregate block groups already counted above and are not themselves counted.

Duplicated block (10 lines × 2) · ×6REDACTED:555
Duplicated block (8 lines × 2) · ×4src/main/scala/ai/starlake/job/transform/JdbcAutoTask.scala:298
Duplicated block (15 lines × 2) · ×3src/main/scala/ai/starlake/lineage/AclExport.scala:39
Duplicated block (11 lines × 2) · ×3src/main/scala/ai/starlake/job/transform/JdbcAutoTask.scala:308
Duplicated block (9 lines × 2) · ×3src/main/scala/ai/starlake/extract/BigQueryTableInfo.scala:122

+ 34 more group(s) — more in Appendix A; the complete list is findings.md.

What to do

  1. Resolve the 6 Duplicated block (10 lines × 2) finding(s) in Code Duplication — start with REDACTED, REDACTED, AclDependencies.scala. — One of this dimension's main actionable groups (6 warning-level).
  2. Resolve the 4 Duplicated block (8 lines × 2) finding(s) in Code Duplication — start with AclDependencies.scala (2), JdbcAutoTask.scala, DagInfo.scala. — One of this dimension's main actionable groups (4 warning-level).
  3. Resolve the 3 Duplicated block (15 lines × 2) finding(s) in Code Duplication — start with AclExport.scala, AdaptiveWriteStrategy.scala, ProcessRunner.scala. — One of this dimension's main actionable groups (3 warning-level).
  4. Enforce Code Duplication in CI to reach Verified (currently Documented). — Hardens enforcement from Documented toward Verified — provenance only; does not change the score.

Detailed fixes: d4_recommendation.md · top locations in Appendix A, every location in findings.md.

D6 · Cohesion (LCOM4)9.5 / 10Stronggated by 2 serious findings✓ Tool-verified

What it measures: Whether a class's methods are focused on a single responsibility.

Method: LCOM4 cohesion per production class with at least two methods: connected components of methods sharing state or calls, computed syntactically. Deterministic, not a proxy.

Coverage: Exhaustive · type-level: LCOM4 cohesion computed over every production class — the population is all types, not a name convention.

Maturity: Documented → Verified → Prevented · effective 9.5 / 10 · rule-coverage 100% · ceiling Verified

2 of 52 classes have LCOM4 above 3.

Low cohesion: SchemaHandler (LCOM4 6) · ×2src/main/scala/ai/starlake/schema/handlers/SchemaHandler.scala:103

What to do

  1. Resolve the 2 Low cohesion finding(s) in Cohesion (LCOM4) — start with SchemaHandler.scala, SLResultSetHelperService.scala. — One of this dimension's main actionable groups (2 warning-level).
  2. Enforce Cohesion (LCOM4) in CI to reach Verified (currently Documented). — Hardens enforcement from Documented toward Verified — provenance only; does not change the score.

Detailed fixes: d6_recommendation.md · top locations in Appendix A, every location in findings.md.

D9 · Test Distribution10.0 / 10Exemplary✓ Tool-verified

What it measures: Whether the test suite has a healthy mix of unit / integration / end-to-end tests.

Method: Test projects classified (Unit/Integration/BDD/E2E) from compiled metadata; test methods counted exhaustively across projects with placement-agnostic disk fallback. Deterministic.

Maturity: Documented → Verified → Prevented · effective 10.0 / 10 · rule-coverage 100% · ceiling Documented

946 test methods: 861 unit, 85 integration, 0 BDD, 0 e2e. The Scala suite contributes 946 test case(s) across 174 file(s) in a test source set declaring at least one — every `test("…")`, `it("…")`, `property("…")` and `"…" in {…}` registration and every JUnit `@Test`, which is how munit, ScalaTest, utest, ZIO Test and specs2 declare a case; a case registered under a computed name is not counted, so this is a floor. Its tier split is INFERRED from paths and file names, except sbt's src/it source set, which the build declares.

✓ On the Gold path — maintain.

Detailed fixes: d9_recommendation.md.

D12 · Dependency Hygiene9.2 8.9 / 10Strong✓ Tool-verified

What it measures: Whether dependencies are current, secure, and not bloated.

Method: Manifest scan via dotnet list package across all projects; worst-signal-per-package deduction (saturating for vulnerabilities, capped-linear for deprecation/outdated) per KLoC. Exhaustive, deterministic.

Maturity: Documented → Verified → Prevented · effective 8.9 / 10 · rule-coverage 97% · ceiling Verified

15 outdated direct sbt declarations. 29 of 30 direct declarations were graded against Maven Central (0 not published there, which is what a private Nexus or Artifactory artifact looks like, 1 declaring a version range rather than an exact version). An sbt declaration names an EXACT version, so the remedy is an edit to the build file — there is no lockfile refresh that would move it. Test-, Provided- and it-scoped declarations are excluded, because a consumer of this project never receives them. A version held in a Scala val is read only when the val is one string literal: sbt is a program rather than a manifest, and evaluating a computed version would invent the measurement rather than take it. Whether any artifact is DEPRECATED or ABANDONED is not graded and cannot be — Maven Central publishes no such marker, and release age does not stand in for one: the most widely depended-upon libraries are routinely the stalest, being finished rather than abandoned. Known CVEs in this dependency graph are D30's question.

Outdated: ai.starlake:starlake-airflow · ×15

What to do

  1. Enforce Dependency Hygiene in CI to reach Verified (currently Documented). — Hardens enforcement from Documented toward Verified — provenance only; does not change the score.

Detailed fixes: d12_recommendation.md · top locations in Appendix A, every location in findings.md.

D13 · REDACTED Scanning0.0 / 10Critical✓ Tool-verified

What it measures: Whether any secrets (keys, tokens, passwords) have leaked into the code.

Method: In-process native secret scanner (entropy plus signature patterns) across all tracked files; no external tool. A clean result is a measured 10, not no-data zero. Deterministic.

Maturity: Documented → Verified → Prevented · effective 0.0 / 10 · rule-coverage 100% · ceiling Prevented

2 secret(s) detected.

REDACTED

What to do

  1. Resolve the 4 Leaked secret finding(s) in REDACTED Scanning — start with REDACTED (4). — One of this dimension's main actionable groups (4 issue-level).
  2. Enforce REDACTED Scanning in CI to reach Verified (currently Documented). — Hardens enforcement from Documented toward Prevented — provenance only; does not change the score.

Detailed fixes: d13_recommendation.md · top locations in Appendix A, every location in findings.md.

D14 · License Compliance10.0 / 10Exemplary○ Nothing flagged

What it measures: Whether the licenses of third-party packages are compatible with your policy.

Method: Third-party package licenses resolved from declared package metadata and checked against the configured policy (allow/deny/copyleft). Deterministic; clean = no incompatible license found at metadata depth.

Maturity: Documented → Verified → Prevented · effective 10.0 / 10 · rule-coverage 100% · ceiling Verified

0 of 29 declared sbt dependency artifact(s) use a banned license. Licences were resolved from the POMs Maven Central publishes, over the production coordinates this repository's 5 sbt build file(s) (build.sbt and project/*.scala) declare with a literal version, directly or through a string-literal val — `%%` resolved to the Scala binary suffix Central publishes under, test-scoped declarations excluded — the same declaration-site closure this analysis reads for dependency currency. sbt has no lockfile a checkout carries, so this is deliberately NOT the transitive closure. 5 further production coordinate(s) are declared with a version the build computes rather than writes as a literal — sbt is a program, and this pass does not run it — and are NOT graded: this verdict covers what the build pins literally or through a string-literal val, not the full graph. 1 of them resolve to no licence at all on Central, even through their parent POMs; that is missing data, not a violation, and none of them is charged. A further 1 coordinate(s) are not published to Maven Central — another repository (Google's Maven hosts androidx), a SNAPSHOT, or a private artifact — so their licences are not readable from here and they are outside this verdict.

✓ On the Gold path — maintain.

Detailed fixes: d14_recommendation.md.

D15 · Churn × Complexity Hotspots9.7 / 10Stronggated by 11 serious findings✓ Tool-verified

What it measures: Files that change often and are also complex — the riskiest hotspots.

Method: Per production file churn times cyclomatic complexity over a rolling window, computed from git and Roslyn/JS/Razor analysis. Exhaustive, deterministic per commit date.

Maturity: Documented → Verified → Prevented · effective 9.7 / 10 · rule-coverage 100% · ceiling Documented

Top hotspots: REDACTED (23×23=529); REDACTED (19×19=361); src/main/scala/ai/starlake/job/transform/BigQueryAutoTask.scala (4×41=164) Repeated repair below the complexity floor: src/main/scala/ai/starlake/job/ingest/AuditTaskBuilder.scala (5 of 6 changes were fixes); src/main/scala/ai/starlake/job/ingest/AuditLog.scala (4 of 5 changes were fixes); src/main/scala/ai/starlake/serve/catalog/CatalogScrubber.scala (3 of 5 changes were fixes)

Hotspot: REDACTED · ×6REDACTED:609
Repeated repair: src/main/scala/ai/starlake/job/ingest/AuditTaskBuilder.scala · ×5src/main/scala/ai/starlake/job/ingest/AuditTaskBuilder.scala:95

What to do

  1. Resolve the 6 Hotspot finding(s) in Churn × Complexity Hotspots — start with REDACTED, REDACTED, BigQueryAutoTask.scala. — One of this dimension's main actionable groups (6 warning-level).
  2. Resolve the 5 Repeated repair finding(s) in Churn × Complexity Hotspots — start with AuditTaskBuilder.scala, AuditLog.scala, CatalogScrubber.scala. — One of this dimension's main actionable groups (5 warning-level).

Detailed fixes: d15_recommendation.md · top locations in Appendix A, every location in findings.md.

D16 · Bus Factor3.9 / 10Weak✓ Tool-verified

What it measures: Whether knowledge is concentrated in too few people (the "bus factor").

Method: Living knowledge per author via time-decayed commit attribution (6-month half-life, focus weighting) across largest source files. Deterministic, avoids blame's mechanical-refactor false positives.

Maturity: Documented → Verified → Prevented · effective 3.9 / 10 · rule-coverage 100% · ceiling Documented

137 source file(s) have their living knowledge concentrated in one author (≥90% of recent, decayed contribution). The largest is src/main/scala/ai/starlake/schema/handlers/SchemaHandler.scala. Counted over 225 of the 387 production source files in this repository: the rest are under the ~2,400-byte size floor this dimension measures over.

Off-boarding risk: anonymized user #1
Further sole-owners (lower concentration)

What to do

  1. Resolve the 1 Off-boarding risk finding(s) in Bus Factor. — One of this dimension's main actionable groups (1 recommendation-level).
  2. Resolve the 1 Further sole-owners (lower concentration) finding(s) in Bus Factor. — One of this dimension's main actionable groups (1 recommendation-level).

Detailed fixes: d16_recommendation.md · top locations in Appendix A, every location in findings.md.

D17 · Explicit Debt9.9 / 10Stronggated by 44 serious findings✓ Tool-verified

What it measures: Acknowledged debt left in the code — TODOs, dead code, suppressed warnings.

Method: Roslyn syntactic debt markers (suppressions/TODO/FIXME/HACK/empty-catch/commented-code/Obsolete) plus SymbolFinder dead-code analysis; weighted-debt-per-KLoC density deducted 2.0x per unit. Deterministic, exhaustive.

Maturity: Documented → Verified → Prevented · effective 9.9 / 10 · rule-coverage 100% · ceiling Prevented

44 deducted task-comment markers across 65215 LoC (0.1/KLoC) → score 9.9. Task comments only: this repository's language is read without a compiler, so D17's suppression, dead-code and commented-out-code arms did not run and this score counts fewer marker kinds than a .NET repository's would.

TodoComment · ×43src/main/java/ai/starlake/utils/repackaged/BigQuerySchemaConverters.java:396
FixmeCommentsrc/test/scala/ai/starlake/schema/handlers/SchemaInfo2HandlerSpec.scala:464

What to do

  1. Resolve the 43 TodoComment finding(s) in Explicit Debt — start with TaskViewDependency.scala (3), SchemaInfo2HandlerSpec.scala (3), ExtractSchema.scala (2). — One of this dimension's main actionable groups (43 warning-level).
  2. Resolve the 1 FixmeComment finding(s) in Explicit Debt — start with SchemaInfo2HandlerSpec.scala. — One of this dimension's main actionable groups (1 warning-level).
  3. Enforce Explicit Debt in CI to reach Verified (currently Documented). — Hardens enforcement from Documented toward Prevented — provenance only; does not change the score.

Detailed fixes: d17_recommendation.md · top locations in Appendix A, every location in findings.md.

D20 · ADR Quality0.0 / 10Critical✓ Tool-verified

What it measures: Whether architecture decisions are recorded well (context, decision, consequences).

Method: Per-ADR judgment by language model at low temperature with two-pass stability; confidence is share of ADRs evaluated; enforcement-field presence detected deterministically. Advisory.

Maturity: Documented → Verified → Prevented · effective 0.0 / 10 · rule-coverage 100% · ceiling Documented

No architecture decision records were found.

No ADRs found

What to do

  1. Resolve the 1 No ADRs found finding(s) in ADR Quality. — One of this dimension's main actionable groups (1 recommendation-level).

Detailed fixes: d20_recommendation.md · top locations in Appendix A, every location in findings.md.

D21 · Naming ConsistencyExemplary◐ Sampled · advisory

What it measures: Whether names — types, methods, variables — are clear and consistent.

Method: Judged by language model at low temperature (0.0-0.1) on a deterministic random symbol sample (fixed size, not exhaustive), with disclosed confidence band. Advisory, sampled.

Maturity: Documented → Verified → Prevented · effective Exemplary / 10 · rule-coverage 100% · ceiling Verified

0 naming inconsistencies across 0 sampled symbols.

✓ On the Gold path — maintain.

Detailed fixes: d21_recommendation.md.

D26 · Project Cohesion0.0 / 10Critical✓ Tool-verified

What it measures: Whether each project is a focused, coherent unit rather than an oversized grab-bag.

Method: Project size overshoot penalties (LoC / public-type count / namespace count, 2-of-3 flag) weighted by log magnitude. Exhaustive across projects, deterministic, LLM-independent.

Maturity: Documented → Verified → Prevented · effective 0.0 / 10 · rule-coverage 100% · ceiling Documented

1 of 1 build units (sbt) flagged as possibly oversized/incoherent.

Projects may be oversized for their cohesion

What to do

  1. Resolve the 1 Projects may be oversized for their cohesion finding(s) in Project Cohesion. — One of this dimension's main actionable groups (1 recommendation-level).

Detailed fixes: d26_recommendation.md · top locations in Appendix A, every location in findings.md.

D28 · Secrets (history)0.0 / 10Critical✓ Tool-verified

What it measures: Whether any secrets were ever committed — scanned across the full git history, not just now.

Method: REDACTED scan via TWO gitleaks detect passes in an isolated checkout — the full git history, then a second --no-git pass over the working tree as it stands — merged and de-duplicated by (rule, file, line); each match flagged High. Both invocations are recorded in the audit trail. Exhaustive; when the tool is absent, or when its output cannot be parsed into the expected shape, the dimension is WITHHELD as an explicit measurement gap on our side — unscored and excluded from the lens, never a hedged middling score.

Maturity: Documented → Verified → Prevented · effective 0.0 / 10 · rule-coverage 100% · ceiling Documented

35 finding(s): 0 critical, 35 high, 0 medium, 0 low. Remediation for historically-committed secrets is credential rotation — they remain in history regardless of later deletion.

REDACTED
REDACTED
REDACTED

What to do

  1. Resolve the 30 High secret finding(s) in Secrets (history) — start with REDACTED (6), REDACTED (6), REDACTED (5). — One of this dimension's main actionable groups (30 issue-level).
  2. Resolve the 5 REDACTED finding(s) in Secrets (history) — start with REDACTED, REDACTED, REDACTED. — One of this dimension's main actionable groups (5 issue-level).
  3. Resolve the 1 Rotate the exposed credentials finding(s) in Secrets (history). — One of this dimension's main actionable groups (1 recommendation-level).

Detailed fixes: d28_recommendation.md · top locations in Appendix A, every location in findings.md.

D29 · Static Analysis (SAST)0.0 / 10Critical✓ Tool-verified

What it measures: Real static-analysis (SAST) findings — likely security bugs in the code, any language.

Method: Polyglot static analysis via semgrep across the repo using the pinned, image-baked p/security-audit + p/owasp-top-ten rulesets (no scan-time registry fetch); severity rules (ERROR/WARNING/INFO) map to a full-band severity-weighted score. Exhaustive, deterministic; degrades on parse failure.

Coverage: semgrep pattern rules over all files — exhaustive for the rule set, blind to classes of bug without a rule (clean = no rule matched).

Maturity: Documented → Verified → Prevented · effective 0.0 / 10 · rule-coverage 100% · ceiling Documented

241 finding(s): 0 critical, 233 high, 6 medium, 2 low. 99 unpinned-GitHub-Actions row(s) are reported here but scored by D36 (supply-chain provenance), which measures that posture as `pinned_actions` — one pinning decision is charged once, not once per lens. semgrep hit a parse error in 26 file(s) — `samples/any-engine/sample-data/hr/flat_locations-2018-01-01.json`, `samples/any-engine/sample-data/hr/locations-2018-01-01.json`, `samples/any-engine/sample-data/hr/locations-2019-01-01.json`, `samples/any-engine/sample-data2/hr/flat_locations-2019-01-01.json`, `samples/bigquery-spark-loader/sample-data/hr/locations-2018-01-01.json`, … (+21 more) — so no absence of findings in the named regions is evidence of anything; rows reported elsewhere in those files are real. Fix the syntax error (or exclude the file deliberately) and re-scan to cover them. Separately, one or more rules could not re-parse an embedded snippet in 8 file(s) (e.g. a workflow `run:` block read as shell). Those files WERE scanned and their other rows are unaffected; only those rules' view of those snippets is missing.

REDACTED
REDACTED
REDACTED
REDACTED
REDACTED

+ 7 more group(s) — more in Appendix A; the complete list is findings.md.

What to do

  1. Resolve the 85 REDACTED finding(s) in Static Analysis (SAST) — start with REDACTED (22), REDACTED (22), REDACTED (16). — One of this dimension's main actionable groups (85 issue-level).
  2. Resolve the 38 REDACTED finding(s) in Static Analysis (SAST) — start with REDACTED (5), REDACTED (5), REDACTED (4). — One of this dimension's main actionable groups (38 issue-level).
  3. No action in Static Analysis (SAST) — all 99 REDACTED finding(s) are reported here at file:line but scored by D36 (supply-chain provenance), so none is charged to this dimension. — One of this dimension's main actionable groups (99 issue-level, 0 of them charged here).

Detailed fixes: d29_recommendation.md · top locations in Appendix A, every location in findings.md.

D30 · Dependency Vulnerabilities8.6 / 10Adequategated by 1 critical finding✓ Tool-verified

What it measures: Whether any dependency has a known published vulnerability (CVE), direct or transitive, in ANY ecosystem the repository declares — Dart pub, Elixir and Erlang via Hex, Go modules, Java and Kotlin via Maven/Gradle, JavaScript/npm, .NET/NuGet, PHP/Composer, Python/PyPI, RubyGems, Rust/Cargo and Swift.

Method: Dependency-CVE scan across every ecosystem the repository declares, scored ONCE. Three sources are unioned and deduplicated by advisory identity (rule id + alias closure, CVE<->GHSA) scoped to package+version, keeping the worst severity: `osv-scanner --recursive` over osv.dev for Dart pub, Elixir/Hex (and Erlang, whose `rebar.lock` syft first converts to a CycloneDX SBOM the scanner reads, with rows attributed back to the lock), Go, Java and Kotlin via Maven/Gradle (and Scala, whose sbt build's pinned direct declarations are written into a CycloneDX SBOM the scanner reads, with rows attributed back to the build file), npm, PHP/Composer, Python/PyPI, RubyGems, Rust/Cargo and Swift; `trivy fs --scanners vuln` for npm lockfiles; and `dotnet list package --vulnerable --include-transitive` for NuGet (with per-advisory collapse of the project x target-framework fan-out), plus a DECLARED-dependency arm that resolves a published gem's gemspec against rubygems.org where no Gemfile.lock is committed. `SeverityScore(c,h,m,l, normalizer 8.0)`. NotApplicable only when NO ecosystem is readable; if any applicable ecosystem could not be scanned the findings are REPORTED and the score is withheld. Supersedes the npm and OSV arms, retired 2026-09-05.

Maturity: Documented → Verified → Prevented · effective 8.6 / 10 · rule-coverage 100% · ceiling Documented

2 finding(s): 1 critical, 0 high, 1 medium, 0 low.

REDACTED
REDACTED

What to do

  1. Resolve the 1 Critical CVE finding(s) in Dependency Vulnerabilities — start with REDACTED. — One of this dimension's main actionable groups (1 issue-level).
  2. Resolve the 1 Medium CVE finding(s) in Dependency Vulnerabilities — start with REDACTED. — One of this dimension's main actionable groups (1 warning-level).

Detailed fixes: d30_recommendation.md · top locations in Appendix A, every location in findings.md.

D31 · IaC & Container Security9.6 / 10Adequategated by 1 critical finding✓ Tool-verified

What it measures: Whether Dockerfiles / Terraform / Kubernetes config follow security best practices.

Method: IaC/container misconfiguration scan via trivy config (REDACTED/Terraform/K8s/Helm/CloudFormation); severity rules to 0-10 moderate normalizer. NotApplicable without manifests. Exhaustive, deterministic.

Maturity: Documented → Verified → Prevented · effective 9.6 / 10 · rule-coverage 100% · ceiling Documented

2 finding(s): 0 critical, 1 high, 1 medium, 0 low.

REDACTED
REDACTED

What to do

  1. Resolve the 1 High IaC finding(s) in IaC & Container Security — start with REDACTED. — One of this dimension's main actionable groups (1 issue-level).
  2. Resolve the 1 Medium IaC finding(s) in IaC & Container Security — start with REDACTED. — One of this dimension's main actionable groups (1 warning-level).

Detailed fixes: d31_recommendation.md · top locations in Appendix A, every location in findings.md.

D34 · Knowledge Freshness9.6 / 10Adequategated by 1 critical finding✓ Tool-verified

What it measures: Whether anyone still has living knowledge of each file, or it has been orphaned — last understood long ago by someone now gone quiet. The sibling of the bus factor: D16 asks who owns it, D34 asks whether anyone still knows it.

Method: File orphaning as total living-knowledge decay below one focused-commit's worth within a year, computed per-file from the D16 decay model. Exhaustive, deterministic over fixed history.

Maturity: Documented → Verified → Prevented · effective 9.6 / 10 · rule-coverage 100% · ceiling Documented

10 of 225 significant source file(s) are orphaned — their living knowledge has decayed to nothing, so no one currently understands them. The largest is src/main/scala/ai/starlake/utils/YamlMigrator.scala. Counted over 225 of the 387 production source files in this repository: the rest are under the ~2,400-byte size floor this dimension measures over.

Orphaned knowledgesrc/main/scala/ai/starlake/utils/YamlMigrator.scala:1
Orphaned file · ×9src/main/scala/ai/starlake/core/utils/LoadConfigMelder.scala:1
Further orphaned files (smaller)

What to do

  1. Resolve the 1 Orphaned knowledge finding(s) in Knowledge Freshness — start with YamlMigrator.scala. — One of this dimension's main actionable groups (1 issue-level).
  2. Resolve the 9 Orphaned file finding(s) in Knowledge Freshness — start with LoadConfigMelder.scala, AdaptiveWriteStrategy.scala, ProjectMigrator.scala. — One of this dimension's main actionable groups (9 recommendation-level).
  3. Resolve the 1 Further orphaned files (smaller) finding(s) in Knowledge Freshness. — One of this dimension's main actionable groups (1 recommendation-level).

Detailed fixes: d34_recommendation.md · top locations in Appendix A, every location in findings.md.

D35 · Change Coupling8.2 / 10Strong✓ Tool-verified

What it measures: Whether files that change together actually belong together — pairs that repeatedly co-change in git history despite having no explicit code dependency, surfacing the hidden/logical coupling (and boundaries in the wrong place) a static scan can't see.

Method: Pairwise co-occurrence over the per-commit file sets in git history (production source only — tests and generated dropped): Degree-of-Coupling = shared ÷ min individual revisions, reported above noise floors (each file ≥10 revisions, ≥5 shared commits, ≥50% strength); sweeping commits excluded. Deterministic over fixed history.

Coverage: Population: PRODUCTION source files only — test and generated files are dropped before pairing, so a class co-changing with its own test (trivially ~100%) can't drown the real production↔production coupling. Pairs ranked by Degree-of-Coupling. A non-source file is never a coupling PARTICIPANT either: documentation, schemas, config and data files are dropped with the rest, so a code↔docs pair — a command and the reference page that restates it — is not reported however strongly the two co-change; nor is coupling that runs THROUGH a build step or config file.

Maturity: Documented → Verified → Prevented · effective 8.2 / 10 · rule-coverage 100% · ceiling Documented

Strongest change-coupling: IngestionJob.scala↔MetricsJob.scala 97%; BigQueryTableInfo.scala↔REDACTED 89%; Xls2Yml.scala↔Yml2XlsIamPolicyTags.scala 80%

Change coupling: BigQueryTableInfo.scala ↔ REDACTED · ×11src/main/scala/ai/starlake/extract/BigQueryTableInfo.scala:1
Change-coupling hub: IngestionJob.scala → MetricsJob.scala, BigQuerySparkWriter.scala, JdbcConnectionLoadConfig.scala · ×9src/main/scala/ai/starlake/job/ingest/IngestionJob.scala

What to do

  1. Resolve the 11 Change coupling finding(s) in Change Coupling — start with BigQueryTableInfo.scala (2), JdbcConnectionLoadCmd.scala (2), BigQueryAutoTask.scala. — One of this dimension's main actionable groups (11 warning-level).
  2. Resolve the 9 Change-coupling hub finding(s) in Change Coupling — start with IngestionJob.scala, Yml2XlsIamPolicyTags.scala, Parquet2CSV.scala. — One of this dimension's main actionable groups (9 warning-level).

Detailed fixes: d35_recommendation.md · top locations in Appendix A, every location in findings.md.

D36 · Supply-chain Provenance & Signing5.0 / 10Adequate✓ Tool-verified

What it measures: Whether the build pipeline provides supply-chain integrity — generated provenance/attestation, signed artifacts (cosign/sigstore), an SBOM, and pinned build actions. Presence of the configuration, not a runtime guarantee.

Method: Supply-chain provenance/signing read deterministically from CI/build config (.github/workflows, .gitlab-ci.yml, azure-pipelines, Jenkinsfile, .circleci) + the release surface: four signals — generated provenance/attestation (SLSA/in-toto/actions-attest), artifact signing (cosign/sigstore/gitsign), an SBOM (syft/sbom-action/*.spdx.json/*.cdx.json), and SHA-pinned build actions — scored 10·present/denom. NotApplicable without a build pipeline. Detects configuration presence, not runtime enforcement.

Maturity: Documented → Verified → Prevented · effective 5.0 / 10 · rule-coverage 100% · ceiling Documented

2/4 supply-chain integrity signals present (provenance, signing, SBOM, pinned actions).

REDACTED
REDACTED
REDACTED
REDACTED

What to do

  1. Resolve the 6 REDACTED finding(s) in Supply-chain Provenance & Signing — start with REDACTED, REDACTED, REDACTED. — One of this dimension's main actionable groups (6 warning-level).
  2. Resolve the 1 REDACTED finding(s) in Supply-chain Provenance & Signing. — One of this dimension's main actionable groups (1 warning-level).
  3. Resolve the 1 REDACTED finding(s) in Supply-chain Provenance & Signing — start with REDACTED. — One of this dimension's main actionable groups (1 warning-level).

Detailed fixes: d36_recommendation.md · top locations in Appendix A, every location in findings.md.

D43 · Malicious Dependencies10.0 / 10Exemplary✓ Tool-verified

What it measures: Whether any dependency the repository declares is published as MALICIOUS rather than merely vulnerable — a package that is an attacker's work, in any ecosystem osv-scanner reads. Scored apart from D30 because the answer is binary: there is no safe version to upgrade to, and the fix is to remove the package and rotate every credential it could have read.

Method: The same dependency scan D30 reads, partitioned on the scanner's own classification rather than rescanned: a row is MALICIOUS when its id is in the `MAL-` space (the ossf/malicious-packages feed) OR its `database_specific.cwe_ids` carries `CWE-506` ("Embedded Malicious Code"). Both channels are structural; the summary text is deliberately NOT read, because a malicious-package record whose summary says only "Critical severity vulnerability" is a real shape ([GHSA redacted]) and a text matcher misses it. Scored BINARY: any surviving row is 0, whatever its severity and however many CVEs sit beside it — a hostile dependency is not a quantity. Applicability and degradation are D30's: NotApplicable only when no ecosystem is readable, and an unscannable ecosystem degrades rather than reading clean. SCORED, not informational.

Maturity: Documented → Verified → Prevented · effective 10.0 / 10 · rule-coverage 100% · ceiling Documented

No dependency in any ecosystem this repository declares is published as malicious.

✓ On the Gold path — maintain.

Detailed fixes: d43_recommendation.md.

Frontend & cross-cutting dimensions

R = React/JS · M = Maturity · P = Readiness.

AC2 · Forms & labels10.0 / 10Exemplary○ Nothing flagged

Other · Accessibility — Whether form controls have a programmatic label (an associated label, aria-label or aria-labelledby), buttons have text, links have an accessible name, a click handler on a plain element names the control it declares, fieldsets have a non-empty legend, known UI-library field components carry a label prop, and a placeholder isn't used as the only label. Static markup readiness, not a WCAG conformance claim.

Method: Static markup-model scan: inputs/selects/textareas checked for an associated label[for]/wrapping label/aria-label/aria-labelledby (per document), buttons for accessible text, fieldsets for a legend; placeholder-only labelling flagged. Deterministic, hard fact per control.

Coverage: Population: form controls, buttons, links, fieldsets and known UI-library field components in the PARSED MARKUP files only (.html/.htm/.cshtml/.razor/.vue/.svelte/.jsx/.tsx); components, hidden subtrees and spread/dynamic-attribute elements are skipped, so a control whose label arrives through a spread or a runtime expression is deliberately not judged. Markup built in script — tagged-template (html`…`) UIs and hyperscript DOM factories — is not read at all.

AC3 · Page structure6.6 / 10Adequate✓ Tool-verified

Other · Accessibility — Whether pages declare a language (well-formed BCP-47) and a non-empty title, expose exactly one main landmark and a sane heading order with non-empty headings, keep zoom enabled, title their iframes, give data tables header cells, and avoid meta-refresh. Static markup readiness, not a WCAG conformance claim.

Method: Static markup-model scan: html lang, document <title>, a main landmark and heading order on full documents only, plus zoom-disabling viewports, untitled iframes and meta-refresh anywhere. Deterministic, per structural checkpoint.

Coverage: Population: the PARSED MARKUP documents (.html/.htm/.cshtml/.razor/.vue/.svelte/.jsx/.tsx). The page-level checks — lang, title, single main landmark — fire ONCE PER FULL DOCUMENT (an <html> root) and never on a partial or component fragment, so a repo of fragments is assessed only on the per-element checks (heading order, table headers, iframe titles, meta-refresh, zoom). Markup built in script — tagged-template (html`…`) UIs and hyperscript DOM factories — is not read at all.

  • No <main> (or role="main") means no "skip to content" target and a weaker landmark map. Wrap the primary content in <main>. — src/main/resources/templates/compare/index.html.j2:2

What to do

  • Declare <html lang>, a document <title> and a <main> landmark, keep headings in order, leave zoom enabled, title iframes and drop meta-refresh.
AC4 · Keyboard semantics1.0 / 10Critical✓ Tool-verified

Other · Accessibility — Whether interactive behaviour is keyboard-reachable — no click handler on a non-interactive element lacking a role, tabindex and key handler, no element the repo's own CSS styles `cursor: pointer` without giving it any of the three, no unfocusable element whose only binding is a mouse enter/leave pair or a double-click, no positive tabindex, no href-less anchor, no placeholder-href (#/javascript) link acting as a button. Static markup readiness, not a WCAG conformance claim.

Method: Static markup-model scan: click handlers on non-interactive elements lacking role+tabindex+key handler, positive tabindex values, and href-less anchors. Components skipped, spreads suppressed. Deterministic, hard fact per element.

Coverage: Population: elements that pose a keyboard-semantics question, in the PARSED MARKUP files only (.html/.htm/.cshtml/.razor/.vue/.svelte/.jsx/.tsx) — a non-natively-interactive element carrying a click handler, a double-click or hover enter/leave binding, a pointer-only gesture on a tabindex="0" element, or `cursor: pointer` from the repo's own CSS; an href-less or placeholder-href (#, javascript:) anchor; and any element with a positive tabindex. Natively interactive elements used correctly (<button>, <a href>, form controls) are NOT in the population — there is nothing to judge — so a page of nothing but correct controls gives AC4 nothing to measure. Keyboard reachability is judged from the markup, never from a rendered page. ★ An interactive element declared in a tagged-template (html`…`) or hyperscript frontend is NOT in this population — no producer reads either — so an empty population is reported as an analyzer gap, never as "this repository has no interactive elements".

  • A click handler on a plain element isn't keyboard-operable. Use a <button>, or add role + tabindex="0" + a key handler. (×3) — src/main/resources/templates/compare/index.html.j2:99, src/main/resources/templates/compare/index.html.j2:136, src/main/resources/templates/compare/index.html.j2:252

What to do

  • Make custom controls keyboard-operable (role + tabindex + key handler), drop positive tabindex, and give anchors a real href.
AC6 · Visual & motion safety4.3 / 10Weak✓ Tool-verified

Other · Accessibility — Whether focus outlines aren't removed without a replacement, motion respects prefers-reduced-motion, and literal CSS colour pairs meet contrast — PARTIAL: inline styles, in-repo <style> blocks, in-repo .css files, var() tokens, Tailwind neutral utilities and CSS-in-JS literals are read (hex/rgb/hsl/named), never computed/runtime/external-CDN colour. Static markup readiness, not a WCAG conformance claim.

Method: Static markup/CSS scan: inline outline:none/0, literal inline colour/background contrast against the 4.5:1 AA floor, and <style>-block animation without a prefers-reduced-motion guard. Deterministic but PARTIAL — only inline styles and in-repo CSS literals are visible.

Coverage: Population: styled elements in the PARSED MARKUP files (.html/.htm/.cshtml/.razor/.vue/.svelte/.jsx/.tsx), plus in-repo <style> blocks, in-repo .css files and CSS-in-JS literals. Colour contrast is computed from LITERAL colour pairs only (hex/rgb/hsl/named, including var() tokens and Tailwind neutral utilities) — computed, runtime-themed and external-CDN colour is never resolved, so this is a partial read of contrast by construction. Markup built in script — tagged-template (html`…`) UIs and hyperscript DOM factories — is not read at all.

  • `.collapsible::before` moves (`transition: transform 0.2s`) and nothing reads prefers-reduced-motion for it — no @media (prefers-reduced-motion) rule for this element, no reset this project loads — so a motion-sensitive user cannot turn it off (WCAG 2.3.3). Declare it under @media (prefers-reduced-motion: no-preference), or neutralise it under reduce. — src/main/resources/templates/compare/index.html.j2:39
  • `.nav-list details>summary::before` moves (`transition: transform .2s`) and nothing reads prefers-reduced-motion for it — no @media (prefers-reduced-motion) rule for this element, no reset this project loads — so a motion-sensitive user cannot turn it off (WCAG 2.3.3). Declare it under @media (prefers-reduced-motion: no-preference), or neutralise it under reduce. — src/main/resources/templates/site/standalone/style.css:21
  • `.item-card` moves (`transition: box-shadow .2s,transform .2s`) and nothing reads prefers-reduced-motion for it — no @media (prefers-reduced-motion) rule for this element, no reset this project loads — so a motion-sensitive user cannot turn it off (WCAG 2.3.3). Declare it under @media (prefers-reduced-motion: no-preference), or neutralise it under reduce. — src/main/resources/templates/site/standalone/style.css:81

What to do

  • Keep a visible focus style (don't remove the outline without a replacement), guard animation with prefers-reduced-motion, and raise low-contrast colour pairs to at least 4.5:1.
AC7 · A11y enforcement4.0 / 10Weak✓ Tool-verified

Other · Accessibility — Whether accessibility is ENFORCED in the toolchain — an accessibility checker configured over the markup (an a11y lint rule set, e.g. eslint-plugin-jsx-a11y or vuejs-accessibility where the project lints JavaScript) and an automated accessibility assertion wired into tests or CI (axe/pa11y/Lighthouse or an equivalent) — on the Documented→Verified→Prevented ladder.

Method: Repo config/CI scan: an accessibility checker configured over the markup (an a11y lint rule set such as eslint-plugin-jsx-a11y / vuejs-accessibility where JavaScript is linted) and an automated accessibility assertion in tests or CI (axe/pa11y/Lighthouse or equivalent), graded on the Documented→Verified→Prevented rungs. Deterministic, presence/rung detection.

Coverage: Population: the repository's own tooling configuration — lint config, test and CI files — NOT the markup. It is read for a configured accessibility checker and an automated accessibility assertion (axe/pa11y/Lighthouse, or a native-toolkit equivalent), and it credits an INVOCATION, never a mention: a licence filename, an import comment or a doc reference earns no rung. Enforcement configured entirely outside the repository leaves no evidence here and cannot be credited.

  • No accessibility enforcement found — no automated accessibility check runs over the HTML your app renders. Assert the accessibility invariants over that HTML in the test suite you already have (parse the output and assert, or drive a browser), and gate that test in CI so a regression blocks the merge. What was searched, so you can tell an absence from a miss: the 3 markup file(s) this pass actually assessed, the linter configuration checked in beside them, and this repository's test and CI files — matched by name against the accessibility checkers this dimension carries. An audit run outside the repository, a hosted scanner, or a check whose name is not one of those, is not seen here.

What to do

  • Enforce accessibility in the test suite you already have: assert the accessibility invariants over the HTML your app renders — parse the rendered output in an existing test, or drive a real browser from one — and gate that test in CI so a regression blocks the merge.
AX10 · Code composition9.6 / 10Exemplary✓ Tool-verified

Other · Architecture — How the codebase splits by code ROLE — domain, application, infrastructure, test, generated. The significance map behind the knowledge/coupling weighting, and a DDD signal in its own right: a thin domain core under fat infrastructure is the anemic-domain smell, quantified. How each file's role is decided, because the split is only as good as that: a generated name or a build-output tree makes it Generated, a test project makes it Test, and otherwise the file's NAMESPACE and PATH words are matched against fixed vocabularies in a fixed ORDER — domain, then infrastructure, then application — so a file whose words hit two layers is counted under the earlier one. A production file matching none of them counts as application, so that share reads 'application or unclassified' rather than a measured application layer. Roles come from naming convention, never from what the code does. On this repository the split was taken from the source tree on disk rather than from a loaded .NET workspace, so a file's role is decided by its PATH segments alone — no declared namespace was available to add to the evidence — and generated output is excluded from the census entirely rather than counted as a generated share.

Method: Roslyn line-count by code ROLE: every source file classified Domain/Application/Infrastructure/Test/Generated by namespace + path convention (the shared CodeRoleClassifier), then significant lines summed per role. Deterministic; the advisory score is the business-logic (domain+application) share of production code.

Coverage: Population: ALL source files, each bucketed into ONE of five roles (Domain/Application/Infrastructure/Test/Generated) by namespace + path convention — a file whose layer isn't named in the convention falls to Application (the neutral default), and the split is line-count, not semantic depth or business value.

What to do

  • The domain core is a small share of production code, but most of the rest matched no layer vocabulary at all — so this is not yet an anemic-domain finding. The namespace/path convention could not place that code, which makes the composition above a statement about the naming, not about the design. Name the layers (or check that the repository's conventions differ from the ones this check knows) before reading a thin domain into it.
AX3 · Project dependency cycles10.0 / 10Exemplary✓ Tool-verified

Other · Architecture — Whether the project-reference graph is acyclic (cycles block independent build/deploy and signal eroding boundaries).

Method: Project reference cycles via elementary-DFS over real .csproj references, using the engine shared with D5/D7; cyclic versus acyclic. Exhaustive, deterministic.

AX4 · Dependency direction10.0 / 10Exemplary✓ Tool-verified

Other · Architecture — Whether dependencies point inward (Domain ← Application ← Infrastructure/Web) — the clean-architecture dependency rule, checked across the project graph.

Method: Layer violations by name-segment inference (Domain/Core to Application to Infrastructure/Web) over the project-reference graph. Exhaustive over all projects, deterministic.

AX6 · Interface segregation9.8 / 10Exemplary✓ Tool-verified

Other · Architecture — Whether interfaces stay focused rather than fat — the Interface-Segregation principle (SOLID 'I').

Method: Roslyn scan: public interface declared-member counts (accessors fold into their property/event); fat-interface threshold (over 15 declared members) flagged per type. Each finding also reports the distinct-OPERATION count — members counted by name, so an overload group counts once — which decides whether it states the caller-side ISP harm or the implementer-side burden of an overload set. TypeScript: exported interfaces that declare behaviour, counted over their REQUIRED operations (method signatures and function-typed properties with no `?`, once per signature) — data-shape interfaces and ambient .d.ts declarations are outside the population; JavaScript declares no interfaces and is not applicable. Deterministic, type-level.

  • `StorageHandler` declares 28 abstract members: `move`, `copy`, `delete`, `exists`, `mkdirs`, `copyFromLocal`, `copyToLocal`, `moveFromLocal`, `read`, `readAndExecute`, `readAndExecuteIS`, `write`, `writeBinary`, `listDirectories`, `list`, `stat`, `blockSize`, `lastModified`, `spaceConsumed`, `touchz`, `touch`, `lockAcquisitionPollTime`, `lockRefreshPollTime`, `getScheme`, `copyMerge`, `open`, `output`, `initFS`. Counted over its ABSTRACT members — a `def` with no body or a `val`/`var` with no initializer, once per signature; concrete members, private and protected members, and members of a companion `object` are not counted, because no implementer is forced to write them. A wide interface forces every implementer and caller to depend on methods they don't use (the Interface-Segregation 'I' in SOLID). Split it into focused role-interfaces. — src/main/scala/ai/starlake/schema/handlers/StorageHandler.scala:38

What to do

  • Split fat interfaces into focused role-interfaces so clients depend only on what they use.
GD1 · Unfinished & placeholder code9.9 / 10Exemplary✓ Tool-verified

Other · Code Health — Unreviewed-generation residue in this repository's Scala: methods whose whole body is still `???` or a "not implemented" throw where nothing overrides them, and placeholder text (lorem ipsum, changeme/replace_me…) left in strings of non-test, non-generated code. Scored as a quality signature, never as a claim about authorship.

Method: Roslyn syntax scan: NotImplementedException throws and placeholder string literals in non-test, non-generated shipped code. Deterministic, code-shape signature.

  • `buildRLSQueries` is a shipped member whose whole body is still `???`, and nothing in the repository overrides it — generated scaffolding that was never completed. Implement it or remove the dead surface. — src/main/scala/ai/starlake/job/transform/JdbcAutoTask.scala:663
  • `upsertAttribute` is a shipped member whose whole body is still `???`, and nothing in the repository overrides it — generated scaffolding that was never completed. Implement it or remove the dead surface. — src/main/scala/ai/starlake/schema/handlers/SchemaHandler.scala:2446
  • `sparkType` is a shipped member whose whole body is still a "not implemented" error, and nothing in the repository overrides it — generated scaffolding that was never completed. Implement it or remove the dead surface. — src/main/scala/ai/starlake/schema/model/Rejection.scala:131

What to do

  • Finish or delete the members whose body is still `???` or a "not implemented" throw where nothing overrides them, and replace placeholder text before shipping.
IC1 · Incompleteness & stubs8.2 / 10Strong✓ Tool-verified

Other · Code Health — Unfinished work detected by code SHAPE, not keywords, in this repository's Scala: methods whose body is `???` or only throws a "not implemented" error, methods that take inputs and return a constant or do nothing, dead `if (false)` branches, and skeleton classes most of whose methods are holes.

Method: Roslyn syntax scan: incompleteness by code shape (constant-returning methods, async-never-await, #if false branches, guards that return what the code already falls through to, tests an earlier guard already decided, comparisons against NaN, skeleton types), not keyword-gated. Deterministic, code-shape heuristic.

  • `buildRLSQueries` is a shipped method whose whole body is `???` (or throws NotImplementedError), and nothing in the repository overrides it — scaffolding that was never completed. Implement it or remove the dead surface. — src/main/scala/ai/starlake/job/transform/JdbcAutoTask.scala:663
  • `upsertAttribute` is a shipped method whose whole body is `???` (or throws NotImplementedError), and nothing in the repository overrides it — scaffolding that was never completed. Implement it or remove the dead surface. — src/main/scala/ai/starlake/schema/handlers/SchemaHandler.scala:2446
  • `sparkType` throws an exception whose message says it isn't finished — a not-implemented stub wearing an ordinary exception. Implement it or remove it. — src/main/scala/ai/starlake/schema/model/Rejection.scala:131
  • A bare `// scalastyle:off`, `// scalafix:off` or `@nowarn` (or Wartremover's `warts.All`) switches every rule or warning off — hidden debt. Suppress the specific rule on the specific line, with a justification, instead. (×11) — src/main/scala/ai/starlake/config/ConnectionInfo.scala:406, src/main/scala/ai/starlake/extract/BigQueryTableInfo.scala:196, src/main/scala/ai/starlake/extract/ExtractDataJob.scala:29, …
  • `validate` has an empty body — confirm it's an intentional no-op and not an unfinished method. — src/main/scala/ai/starlake/schema/model/WriteStrategy.scala:152
  • A test is skipped/ignored — coverage that looks present but never runs. Re-enable it or delete it so the green run means something. (×7) — src/test/scala/ai/starlake/extract/ExtractDataJobSpec.scala:377, src/test/scala/ai/starlake/integration/freshness/FreshnessSnowflakeIntegrationSpec.scala:7, src/test/scala/ai/starlake/integration/load/LoadShardBQIntegrationSpec.scala:10, …

What to do

  • Finish or delete the unfinished stubs (functions that throw "not implemented", or whose body is empty or a bare constant) — they are dead surface that looks live.
  • Clear the softer debt: re-enable or delete `ignore`d and `pending` tests, decide whether each empty method or UnsupportedOperationException is deliberate (and say so in a comment), and replace a bare `// scalastyle:off` or `@nowarn` with targeted, justified suppressions.
M1 · Documentation (README)8.0 / 10Strong✓ Tool-verified

Maturity · Maturity — Whether the repo and its projects have a README, and whether it's substantive and current.

Method: Filesystem scan: README presence, word count, and headings for depth; git history for staleness. Exhaustive across root and project dirs, deterministic.

M2 · Architecture documentation5.0 / 10Adequate✓ Tool-verified

Maturity · Maturity — Whether key decisions (ADRs) and the high-level shape (C4/diagrams) are written down.

Method: Filesystem scan: ADR folder/naming conventions or content, plus Mermaid/PlantUML/C4/architecture.md discovery. Exhaustive, deterministic.

  • No Architecture Decision Records found — no conventional ADR directory, no numbered `NNNN-title` documents in any markup this check reads, and nothing ADR-shaped by content. Design rationale recorded elsewhere (a design-notes tree, a mailing list, pull-request discussion) is not visible to this check and is not re-findable per decision, so a future maintainer cannot ask why one choice was made and get an answer.

What to do

  • Record your significant design decisions. Write one short document per decision, giving its date, the situation, the decision and its consequences, and keep them together with your other design documents. A docs/adr folder with each file named by number and title is the easiest place to find them.
M3 · Folder & project structure10.0 / 10Exemplary✓ Tool-verified

Maturity · Maturity — Whether the repo is organised deliberately — src/test separation and consistent project naming.

Method: Filesystem scan: src/test folder separation and namespace-prefix consistency (majority RootNamespace agreement). Exhaustive across projects, deterministic.

M4 · Documentation accuracy10.0 / 10Exemplary◐ Sampled · advisory

Maturity · Maturity — Whether the README actually describes the code that exists (LLM-judged, advisory).

Method: Judged by language model at low temperature: README accuracy versus actual projects, within a disclosed tolerance. Advisory, not a measured number.

P1 · CI/CD gates10.0 / 10Exemplary○ Nothing flagged

Readiness · Readiness — Whether an automated pipeline builds and tests every change.

Method: Filesystem scan: CI workflow files (.github/workflows, .gitlab-ci.yml, etc.) for build and test stages. Exhaustive, deterministic.

P10 · Library API & versioning6.0 / 10Adequate✓ Tool-verified

Readiness · Readiness — For a library: a deliberate (small) public API surface and explicit semantic versioning so consumers can depend on it safely.

Method: Roslyn scan: public API surface area and semantic-versioning markers (SemVer attributes, changelog entries) for libraries; off .NET, a library is the ecosystem's publication act (an npm package that is not private and names an entry point, a PyPI distribution with a build system, a Rust library crate, a Maven/Gradle module that publishes, a Go module with no package main, a gemspec, a Composer library, a SwiftPM library product, a pub.dev or Hex package), its surface is the share of types the language model records as public (Rust, Swift, Java, Kotlin, Go, Dart; not measured where the model records no type visibility or, as in TypeScript, only module-level export), and its version is read from the manifest, a semver CHANGELOG, release tooling or semver git tags. Exhaustive, deterministic.

  • 8/10 types (80%) declared in the published library are public. For a library, every public type is a stability contract — keep implementation types off the surface and expose only the intended API.

What to do

  • Keep implementation types off the public surface (Java: package-private (no modifier), or leave its package out of `module-info` exports) so internals can change without breaking consumers.
P2 · Observability7.0 / 10Strong✓ Tool-verified

Readiness · Readiness — Whether the code is diagnosable in production — structured logging, tracing/metrics, health checks.

Method: Filesystem/Roslyn scan: structured-logging frameworks (Serilog, NLog), OpenTelemetry, and health-check endpoint patterns. Exhaustive, deterministic.

What to do

  • Consider OpenTelemetry tracing/metrics (opentelemetry-java or Micrometer) and a health-check endpoint (Spring Boot Actuator's /actuator/health, or a /health route) for operability.
P3 · Security & performance tooling0.0 / 10Critical✓ Tool-verified

Readiness · Readiness — Whether SAST, secret/dependency scanning and performance benchmarking are wired in (presence, not runtime).

Method: Filesystem scan: SAST configuration, dependency-update automation, secret scanning, and a benchmark harness or benchmark step — in this repository's own ecosystem. Exhaustive, deterministic.

  • No static application security testing detected. For this repository's stack, add scalafix or scapegoat (or `semgrep --config=auto`, which runs on any language) as a CI step. What was searched, so you can tell an absence from a miss: the 49336 CI workflow file(s) in this repository, and the scanner and linter configuration checked in beside them. A scan that runs outside CI, one configured in your forge's web UI rather than in a committed file, or a tool whose name is none of those this check carries, is not seen — if that is your case the row is wrong, and saying so is more useful than adding a second scanner.

What to do

  • Add a SAST step to CI running what this repository's stack ships: scalafix or scapegoat — or `semgrep --config=auto`, which runs on any language — so a security regression fails the build instead of landing.
  • Add gitleaks/trufflehog in CI to block PRs that introduce committed secrets.
P4 · Deployment & Rollback7.0 / 10Strong✓ Tool-verified

Readiness · Readiness — Whether releases are automated and safely reversible (probes, rolling updates, approval gates) — from manifests/pipeline files, not the live environment.

Method: Filesystem scan: deployment manifests/IaC (K8s YAML, Helm, Terraform) for rolling updates, probes, approval gates, migration hooks. Exhaustive, deterministic.

P6 · Release Hygiene10.0 / 10Exemplary✓ Tool-verified

Readiness · Readiness — Whether releases are traceable — a maintained changelog and explicit version stamping.

Method: Filesystem scan: changelog file presence and version tags in csproj or git tags. Exhaustive, deterministic.

S1 · Web-Security Posture2.0 / 10Critical✓ Tool-verified

Other · Security — Read from this repository's Scala source and the Java beside it: cryptographic hygiene (weak hash or cipher, key material fixed in the source) and outbound TLS (certificate validation, protocol version) — facts about code that hold whether or not it serves anything itself. This repository serves HTTP itself (src/main/scala/ai/starlake/serve/SingleUserRequestHandler.scala:10 — `HttpServlet`), so its Scala source was also read for the server controls: TLS served or HSTS set, security response headers, cookie flags and inbound validation — each credited where its Scala spelling appears in production source, not proven on every route. A Play application is credited the security headers Play's default `SecurityHeadersFilter` sends unless its build or configuration switches that filter off, and its cookies the HttpOnly flag Play sets by default.

Method: Roslyn plus filesystem scan: HSTS/security headers, secure cookies, input validation, middleware order, weak crypto (MD5/SHA1/DES); HTTPS-metadata context-aware. On a repository with no .NET source it reads JavaScript/TypeScript, Dart, Ruby, Scala, Rust or Elixir source itself (test, vendored, generated and minified paths not) for the arms that hold whether or not the code serves HTTP: a weak hash or cipher, key material fixed in the source, a TLS client that accepts any certificate, and (JavaScript/TypeScript, Ruby, Scala, Rust and Elixir) an obsolete TLS version; the server controls are not assessed there, and a Dart, Ruby, Scala, Rust or Elixir repository that serves no HTTP of its own is N/A on them. Deterministic.

  • an `X509TrustManager` whose `checkServerTrusted` accepts every chain is set on a TLS client, so every peer certificate is accepted — including one an attacker presents. Chain, expiry, revocation and hostname are all skipped, so an attacker on the network path can present any certificate and read or rewrite the traffic — the connection is encrypted to the attacker, not to the intended host. Remove the override so the JVM's default trust manager and hostname check apply — that is what `SSLContext.getDefault`, Netty's `SslContextBuilder.forClient()`, sttp, Play WS and ssl-config use when nothing is set. If the server presents a self-signed or privately-issued certificate, trust THAT issuer rather than every certificate: load its CA into a `KeyStore` and build a `TrustManagerFactory` from it (ssl-config: `trustManager.stores`), which keeps the chain and hostname checks on. — REDACTED:721
  • This repository runs an HTTP server and nothing in its Scala source or committed configuration secures the transport: no HTTPS served (akka/pekko-http `ConnectionContext.httpsServer`/`enableHttps`, an http4s builder's `withTLS`, Jetty's `SslContextFactory`, zio-http's `SSLConfig`, Play's `https.port`), no `Strict-Transport-Security` header, HSTS middleware or Play `RedirectHttpsFilter`, no `X-Forwarded-Proto` read or Play `play.http.forwarded.trustedProxies` for a TLS-terminating proxy — so its traffic may cross the network in clear text. (−2.0 on this card.) The server: src/main/scala/ai/starlake/serve/SingleUserRequestHandler.scala:10 — `HttpServlet`.
  • None of the 11 security response headers the OWASP Secure Headers Project recommends (Content-Security-Policy, Strict-Transport-Security, X-Content-Type-Options, X-Frame-Options, …) is set on a response this repository's Scala server sends — defense in depth, even when a proxy could set them. (−2.0 on this card.) The server: src/main/scala/ai/starlake/serve/SingleUserRequestHandler.scala:10 — `HttpServlet`.

What to do

  • Stop accepting every TLS certificate: delete the trust manager whose `checkServerTrusted` is empty, the `HostnameVerifier` that returns `true`, `InsecureTrustManagerFactory` / `NoopHostnameVerifier` and `acceptAnyCertificate`, and trust a self-signed or private server by giving the client a trust store holding its CA — never by switching verification off.
  • Secure the server's transport: serve TLS itself (akka/pekko-http `ConnectionContext.httpsServer(sslContext)` with `newServerAt(…).enableHttps(…)`, an http4s server builder's `withTLS(…)`, Play's `https.port`), or run it behind a TLS-terminating proxy and trust it (`X-Forwarded-Proto`; Play `play.http.forwarded.trustedProxies`); send `Strict-Transport-Security` once it is HTTPS-only (http4s `HSTS(…)`, Play `RedirectHttpsFilter`).
  • Set security response headers on what the server sends — keep Play's `SecurityHeadersFilter` enabled, wrap akka/pekko-http routes in `respondWithHeaders(RawHeader("X-Content-Type-Options", "nosniff"), …)`, or add an http4s middleware that sets `Content-Security-Policy`, `X-Content-Type-Options` and a frame policy on every response.
X1 · Async correctness10.0 / 10Exemplary○ Nothing flagged

Other · Code Health — Whether the code avoids sync-over-async (deadlock-prone blocking on tasks) and async void.

Method: Roslyn syntax scan: async methods scanned for .Wait()/.GetAwaiter().GetResult() and async-void outside event handlers. Deterministic, hard fact per invocation.

X12 · Unreachable branch7.7 / 10Strong✓ Tool-verified

Other · Code Health — Whether any branch is dead by construction — a switch arm whose label can never equal a case-normalised subject, or an `else if` whose predicate the arm above has already swallowed.

Method: Roslyn syntax + semantics: switch labels compared against the subject's own case normaliser, and if/else-if chains checked for a literal an earlier arm's containment test already swallows. Deterministic, provable per finding. Advisory.

  • The match subject is `value.toUpperCase`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 16). The pattern `"continuous"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"CONTINUOUS"`), or drop the normaliser and compare with `equalsIgnoreCase`. — src/main/scala/ai/starlake/job/metrics/Metrics.scala:17
  • The match subject is `value.toUpperCase`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 16). The pattern `"discrete"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"DISCRETE"`), or drop the normaliser and compare with `equalsIgnoreCase`. — src/main/scala/ai/starlake/job/metrics/Metrics.scala:18
  • The match subject is `value.toUpperCase`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 16). The pattern `"frequencies"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"FREQUENCIES"`), or drop the normaliser and compare with `equalsIgnoreCase`. — src/main/scala/ai/starlake/job/metrics/Metrics.scala:19
  • The match subject is `value.toUpperCase()`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 43). The pattern `"text"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"TEXT"`), or drop the normaliser and compare with `equalsIgnoreCase`. — src/main/scala/ai/starlake/schema/model/IndexMapping.scala:44
  • The match subject is `value.toUpperCase()`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 43). The pattern `"keyword"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"KEYWORD"`), or drop the normaliser and compare with `equalsIgnoreCase`. — src/main/scala/ai/starlake/schema/model/IndexMapping.scala:45
  • The match subject is `value.toUpperCase()`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 43). The pattern `"long"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"LONG"`), or drop the normaliser and compare with `equalsIgnoreCase`. — src/main/scala/ai/starlake/schema/model/IndexMapping.scala:47
  • The match subject is `value.toUpperCase()`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 43). The pattern `"integer"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"INTEGER"`), or drop the normaliser and compare with `equalsIgnoreCase`. — src/main/scala/ai/starlake/schema/model/IndexMapping.scala:48
  • The match subject is `value.toUpperCase()`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 43). The pattern `"short"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"SHORT"`), or drop the normaliser and compare with `equalsIgnoreCase`. — src/main/scala/ai/starlake/schema/model/IndexMapping.scala:49
  • The match subject is `value.toUpperCase()`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 43). The pattern `"byte"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"BYTE"`), or drop the normaliser and compare with `equalsIgnoreCase`. — src/main/scala/ai/starlake/schema/model/IndexMapping.scala:50
  • The match subject is `value.toUpperCase()`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 43). The pattern `"double"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"DOUBLE"`), or drop the normaliser and compare with `equalsIgnoreCase`. — src/main/scala/ai/starlake/schema/model/IndexMapping.scala:51
  • The match subject is `value.toUpperCase()`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 43). The pattern `"float"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"FLOAT"`), or drop the normaliser and compare with `equalsIgnoreCase`. — src/main/scala/ai/starlake/schema/model/IndexMapping.scala:52
  • The match subject is `value.toUpperCase()`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 43). The pattern `"half_float"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"HALF_FLOAT"`), or drop the normaliser and compare with `equalsIgnoreCase`. — src/main/scala/ai/starlake/schema/model/IndexMapping.scala:53
  • The match subject is `value.toUpperCase()`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 43). The pattern `"scaled_float"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"SCALED_FLOAT"`), or drop the normaliser and compare with `equalsIgnoreCase`. — src/main/scala/ai/starlake/schema/model/IndexMapping.scala:54
  • The match subject is `value.toUpperCase()`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 43). The pattern `"date"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"DATE"`), or drop the normaliser and compare with `equalsIgnoreCase`. — src/main/scala/ai/starlake/schema/model/IndexMapping.scala:56
  • The match subject is `value.toUpperCase()`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 43). The pattern `"boolean"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"BOOLEAN"`), or drop the normaliser and compare with `equalsIgnoreCase`. — src/main/scala/ai/starlake/schema/model/IndexMapping.scala:57
  • The match subject is `value.toUpperCase()`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 43). The pattern `"binary"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"BINARY"`), or drop the normaliser and compare with `equalsIgnoreCase`. — src/main/scala/ai/starlake/schema/model/IndexMapping.scala:59
  • The match subject is `value.toUpperCase()`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 43). The pattern `"integer_range"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"INTEGER_RANGE"`), or drop the normaliser and compare with `equalsIgnoreCase`. — src/main/scala/ai/starlake/schema/model/IndexMapping.scala:61
  • The match subject is `value.toUpperCase()`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 43). The pattern `"float_range"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"FLOAT_RANGE"`), or drop the normaliser and compare with `equalsIgnoreCase`. — src/main/scala/ai/starlake/schema/model/IndexMapping.scala:62
  • The match subject is `value.toUpperCase()`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 43). The pattern `"long_range"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"LONG_RANGE"`), or drop the normaliser and compare with `equalsIgnoreCase`. — src/main/scala/ai/starlake/schema/model/IndexMapping.scala:63
  • The match subject is `value.toUpperCase()`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 43). The pattern `"double_range"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"DOUBLE_RANGE"`), or drop the normaliser and compare with `equalsIgnoreCase`. — src/main/scala/ai/starlake/schema/model/IndexMapping.scala:64
  • The match subject is `value.toUpperCase()`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 43). The pattern `"date_range"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"DATE_RANGE"`), or drop the normaliser and compare with `equalsIgnoreCase`. — src/main/scala/ai/starlake/schema/model/IndexMapping.scala:65
  • The match subject is `value.toUpperCase()`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 43). The pattern `"geo_point"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"GEO_POINT"`), or drop the normaliser and compare with `equalsIgnoreCase`. — src/main/scala/ai/starlake/schema/model/IndexMapping.scala:67
  • The match subject is `value.toUpperCase()`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 43). The pattern `"geo_shape"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"GEO_SHAPE"`), or drop the normaliser and compare with `equalsIgnoreCase`. — src/main/scala/ai/starlake/schema/model/IndexMapping.scala:68
  • The match subject is `value.toUpperCase()`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 43). The pattern `"ip"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"IP"`), or drop the normaliser and compare with `equalsIgnoreCase`. — src/main/scala/ai/starlake/schema/model/IndexMapping.scala:69
  • The match subject is `value.toUpperCase()`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 43). The pattern `"completion"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"COMPLETION"`), or drop the normaliser and compare with `equalsIgnoreCase`. — src/main/scala/ai/starlake/schema/model/IndexMapping.scala:70
  • The match subject is `value.toUpperCase()`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 43). The pattern `"token_count"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"TOKEN_COUNT"`), or drop the normaliser and compare with `equalsIgnoreCase`. — src/main/scala/ai/starlake/schema/model/IndexMapping.scala:71
  • The match subject is `value.toUpperCase()`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 43). The pattern `"object"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"OBJECT"`), or drop the normaliser and compare with `equalsIgnoreCase`. — src/main/scala/ai/starlake/schema/model/IndexMapping.scala:73
  • The match subject is `value.toUpperCase()`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 43). The pattern `"array"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"ARRAY"`), or drop the normaliser and compare with `equalsIgnoreCase`. — src/main/scala/ai/starlake/schema/model/IndexMapping.scala:74

What to do

  • Each finding names the span that makes the branch dead and the span it is dead because of. Confirm from the two, then delete the dead arm or fix the predicate that was meant to be reachable.
X13 · Undrained process stream10.0 / 10Exemplary○ Nothing flagged

Other · Code Health — Whether a child process that has BOTH standard streams redirected drains both — reading one to the end while the other is never read deadlocks once the child fills the unread pipe.

Method: Roslyn syntax + semantics: ProcessStartInfo launches with both streams redirected, checked for a drain of each stream across the enclosing type. On a repository with no .NET source it reads JavaScript/TypeScript off the engine’s own token stream (test, vendored and minified paths not) with the same rule: a `spawn` imported from `child_process` whose stdout and stderr are both pipes (no options, no `stdio`, or `stdio` of `'pipe'`), bound to a local that never leaves its scope, where exactly one of the two streams is read, the other never, and the child’s `close`/`exit` (or the read stream’s end) is awaited; a shell redirect in the call’s arguments or a `kill` of the child suppresses it. The same rule reads Python’s `subprocess.Popen`, Dart’s `Process.start`, Ruby’s `Open3.popen3`, and Scala’s JDK process starts (`new ProcessBuilder(…).start()` with no `redirectErrorStream`/`redirectError`/`redirectOutput`/`inheritIO`, `Runtime.getRuntime.exec`) where one of `getInputStream`/`getErrorStream` is read and the other never while `waitFor()` is called or the read stream is read to its end. Deterministic, provable per finding. Advisory.

X14 · Bypassable address classification10.0 / 10Exemplary○ Nothing flagged

Other · Security — Whether a hand-rolled public/private IP check can be walked past — a method that unwraps IPv4-mapped IPv6 but returns the opposite verdict for the same host written as IPv4-compatible, 6to4 or NAT64.

Method: Roslyn syntax + semantics: methods that unwrap IPv4-mapped IPv6 and hand-roll IPv4 range carve-outs, checked for whether the IPv6 branch also accounts for the IPv4-compatible, 6to4 and NAT64 embeddings. Deterministic, provable per finding. Advisory.

X15 · Unvalidated length from an untrusted reader10.0 / 10Exemplary○ Nothing flagged

Other · Security — Whether a length read out of the stream being parsed is bounded before it is allocated or read — an unchecked count taken from the input lets the input choose the allocation.

Method: Roslyn syntax + semantics: integer lengths read from a BinaryReader and spent on a bulk read or an array allocation, checked for any comparison or bounding call on the value anywhere in the method. Deterministic, provable per finding. Advisory.

X16 · Unfloored truncation loop10.0 / 10Exemplary○ Nothing flagged

Other · Code Health — Whether a loop that shortens a string until it fits a length budget has a floor — one with none grinds the value down to the empty string, or past it into a negative-length `Substring`.

Method: Roslyn syntax + semantics: while/do loops whose body's only effect on a string is to drop its last character, checked for whether anything — a direct comparison on the length, a body guard, a break — bounds that length below. On a repository with no .NET source it reads JavaScript/TypeScript off the engine’s own token stream (test, vendored and minified paths not) with the same rule: a `while`/`do` loop whose body’s one assignment to a value is `x = x.slice(0, -1)` or `x = x.slice|substring|substr(0, x.length - 1)`, driven by a condition that reads `x.length` only as a term of a larger expression — never compared directly, never tested for truthiness, and with no other read of `x` — and whose body has no `break`, `return`, `throw` or `if` naming `x`. Deterministic, provable per finding. Advisory.

X17 · Uncapped recursion over a caller-supplied document10.0 / 10Exemplary○ Nothing flagged

Other · Security — Whether a walk that recurses through a JSON/XML tree handed in by its caller bounds how deep it will go — an uncapped walk lets the document's nesting choose the stack depth, and the resulting StackOverflowException cannot be caught.

Method: Roslyn syntax + semantics: methods that take a JSON/XML document node and call themselves with a child of it, reachable from an externally-callable member of the same type that accepts a document, checked for any depth parameter, descent counter or threaded arithmetic anywhere in the walk. On a repository with no .NET source it reads TypeScript off the engine’s own token stream (test, vendored, generated and minified paths and `.d.ts` not) with the same rule: a function or method with a parameter typed as a document value — `unknown`, `any`, `object`, `Record<string, unknown|any>`, a JSON alias (`JsonValue`, `JSONObject`, …) or a DOM node global the file does not rebind — that calls itself (bare, or through `this` for a method, or hands itself to a call over the value as in `value.map(walk)`) with something it took out of that value and never through an ancestor accessor such as `closest()` or `parentElement`, that is exported or reached from an exported function (a public method of the same exported class) taking such a value, and that names no depth, level, nesting, recursion, remaining or budget anywhere and threads no `+`/`-` arithmetic through a self-call. Plain JavaScript is not read: with no annotation nothing tells a parsed document from a tree the code built itself. Deterministic, provable per finding. Advisory.

X18 · Disposal-pattern correctness4.8 / 10Weak✓ Tool-verified

Other · Code Health — Whether a type's disposal matches what it OWNS — releasing what it created, leaving alone what it was handed, and not declaring a finalizer for state that has nothing unmanaged to finalize.

Method: Roslyn syntax + semantics: every assignment to a disposable field is read to decide whether the type CREATED the value or was handed it, and the type's disposal is checked against that answer — an injected interface it disposes, a value it constructed and never releases, a finalizer on a type holding nothing unmanaged, and a disposable local whose every reference is a plain member read. A value handed to a container that disposes its contents (a parent control's `Controls` collection, a component `IContainer`) is released by that container and is not reported; generated code is out of population. On a repository with no .NET source the same ownership questions are read in JavaScript/TypeScript off the engine’s own token stream (test, vendored, generated and minified paths not): a class declaring `dispose()`, `[Symbol.dispose]()` or `[Symbol.asyncDispose]()` that disposes a field it was handed through a constructor parameter typed as a repository interface or resolved by a dependency-injection container; that assigns a field only ever from `new X(…)` of a disposable class and neither releases it anywhere in the class nor names it in its disposal member or a method that member calls, nor hands it to anything else; and a `const`/`let` local built from literals only whose every reference opens a statement operating on a non-release member of it. A class is disposable when every repository declaration of its name declares or inherits a disposal member, or when it is a documented library disposable (`vscode` EventEmitter, CancellationTokenSource and Disposable; three.js geometries, materials, textures, render targets, renderers, controls and composers). The finalizer arm has no JavaScript counterpart: a class cannot declare one. Dart is read for the owned-and-never-released arm: a Flutter `State` (or a class declaring `dispose()`/`close()`) whose library-private field is written only with direct creations of a framework controller, `FocusNode`, `StreamController` or periodic `Timer` (or a `.listen(…)` into a field declared `StreamSubscription`), which nothing in the class or its library releases (`dispose`/`close`/`cancel`) and which is never handed on except as a named argument. Ruby is read for the owned-and-never-released arm and the abandoned-local arm: a class declaring an instance `close`, `dispose`, `shutdown`, `stop`, `disconnect` or `terminate` whose instance variable is written only with a blockless core descriptor creation (`File.open`/`new`, `Tempfile`, `Dir.open`, `IO.popen`, the `socket` and `openssl` socket classes) that no method of the class closes and that is never handed on (passed, returned, stored, exposed by `attr_reader`, delegated) — a class whose superclass or mixin lies outside the repository is not judged — and a blockless creation read through and dropped (`File.open(path).read`) or held in a local that is never closed, returned, passed or stored (a `&:read` block-pass, an `autoclose: false` borrow and a `fileno` hand-over are not abandonment). PHP is read for the same two arms: a class (not a trait, and not one using a trait) declaring an instance `close`, `dispose`, `shutdown`, `stop`, `disconnect` or `terminate` whose `private` property (or a `final` class's `protected` one) is written only with a direct core `fopen`, `tmpfile`, `popen`, `proc_open`, `fsockopen`, `stream_socket_client`/`server` or `opendir` that nothing in the class closes (`fclose`, `pclose`, `closedir`, `unset`, a reset to `null`) and that is never handed on, and a handle held in a function's local, or read through as the argument of a reader, that is never closed, returned, passed, captured or stored; a `php://` stream, `curl_init`, `SplFileObject`, `PDO` and a function the repository declares under the core name are not read. Deterministic, provable per finding. Advisory.

  • `scala.io.Source.fromFile(…)` opens something that has to be given back — a file descriptor, a socket, a directory handle — and its only use reads `.getLines` through it and drops it, so this expression both creates the value and is the last place that can release it, and does not. Every call leaks one more open handle until the collector happens to reach it: `Too many open files` under load, and on Windows a file that cannot be replaced or deleted while it is held. Open it with `scala.util.Using.resource(scala.io.Source.fromFile(…)) { r => … }`, which releases it on every path including a throw, or hold it and call `.close()` in a `finally`. — src/main/scala/ai/starlake/schema/model/AutoTaskInfo.scala:538
  • `Source.fromResource(…)` opens something that has to be given back — a file descriptor, a socket, a directory handle — and its only use reads `.mkString` through it and drops it, so this expression both creates the value and is the last place that can release it, and does not. Every call leaks one more open handle until the collector happens to reach it: `Too many open files` under load, and on Windows a file that cannot be replaced or deleted while it is held. Open it with `scala.util.Using.resource(Source.fromResource(…)) { r => … }`, which releases it on every path including a throw, or hold it and call `.close()` in a `finally`. — src/main/scala/ai/starlake/utils/CliConfig.scala:124

What to do

  • Each finding names the value and the span that decides who owns it — the `new` that created it, or the constructor parameter that handed it over. Confirm ownership from that span, then make the disposal match it: release what this type created, leave what it was injected with to whoever created THAT, and drop a finalizer whose type holds nothing unmanaged to release.
X19 · Unrestored process-global state10.0 / 10Exemplary○ Nothing flagged

Other · Code Health — Whether a method that temporarily changes state belonging to the whole process — the working directory, an environment variable — puts it back on EVERY path: a restore reached only when nothing throws leaks the change to the rest of the process.

Method: Roslyn syntax + semantics: method bodies that write the process working directory or an environment variable and write it back in the same body, checked for whether that restore sits in a `finally`/`catch` or only on the straight-line path. On a repository with no .NET source the same rule reads production JavaScript/TypeScript off the engine’s own token stream (test, vendored and minified paths not): `process.chdir`, `process.env.NAME =`/`["NAME"] =` and `delete process.env.NAME`, and Deno’s `Deno.chdir`/`Deno.env.set`/`Deno.env.delete`, paired per function body (a nested function or arrow is its own body, and module top-level code is none), where the last write puts back a local the body captured from the same global or deletes a variable the first write set, with at least one statement between them; a write in a `catch`/`finally` of that body silences it. Deterministic, provable per finding. Advisory.

X20 · Mistyped argument guard10.0 / 10Exemplary○ Nothing flagged

Other · Code Health — Whether an argument guard throws the exception its own condition describes — a guard that rejects a value for being EMPTY and reports it as `NullPointerException` (Java, Kotlin, Scala), `ArgumentError.notNull` (Dart) or `ArgumentNullException` (F#'s `nullArg`) tells the caller a parameter was null when it provably was not.

Method: Roslyn syntax: `throw new ArgumentNullException(nameof(p))` statements controlled by an `if`, whose condition is read for a test that is true of a NON-null `p` — an emptiness test that dereferences it (`p.Count == 0`, `!p.Any()`) or a BCL predicate documented true of the empty value (`string.IsNullOrEmpty(p)`). Deterministic, provable per finding. Advisory.

X21 · Side-effecting pattern guard10.0 / 10Exemplary○ Nothing flagged

Other · Code Health — Whether a `when` guard is free of side effects — a guard that increments a counter or assigns while deciding whether its arm matches applies that change during PATTERN MATCHING, on an arm that may not be selected, and skips it entirely when a short-circuit to its left answers first.

Method: Roslyn syntax: `when` guards on case labels and switch-expression arms, read for a mutation (`++`/`--`/assignment) sitting in a position the guard's own `&&`/`||`/`??`/`?:`/`?.` can skip. Deterministic, provable per finding. Advisory.

X22 · Contradicted release guard10.0 / 10Exemplary○ Nothing flagged

Other · Code Health — Whether a method that TAKES a lock or semaphore and gives it back from a flag-guarded `finally` returns the value that flag implies — reporting success while the guard hands the primitive back admits a second caller the exclusion was there to keep out, and reporting failure while the guard keeps it leaves nothing to ever give it back.

Method: Roslyn syntax: `try` statements whose `finally` releases a synchronisation primitive under a bare local-bool guard, where the method also TOOK that same primitive before the `try`, checked for a `return` of a bool literal whose value disagrees with the flag state the method's own straight-line assignments put it in. Deterministic, provable per finding. Advisory.

X23 · Unguarded diagnostic materialisation10.0 / 10Exemplary○ Nothing flagged

Other · Code Health — Whether the work a diagnostic log line costs is paid only when that line is wanted — C# evaluates a call's arguments BEFORE the call, so a trace/debug message joined or projected out of a collection is built in full on every pass, and then discarded by a sink the shipped configuration leaves switched off.

Method: Roslyn syntax: log calls at a diagnostic level (a `Log`-prefixed method naming Trace/Debug/Verbose, or a bare `Debug`/`Trace`/`Verbose` on a receiver named for a logger), whose argument list is read for a call whose cost scales with a sequence — a LINQ operator, a materialisation, `string.Join`, a serializer — with no enclosing level check or conditional-compilation region. On a repository with no .NET source it reads JavaScript/TypeScript off the engine’s own token stream (test, vendored and minified paths not) with the same rule: a `debug`/`trace`/`verbose` call on a receiver named for a logger, or a `log`-prefixed method naming the level, whose argument calls an array operator (`map`/`filter`/`reduce`/`sort`/…), `Array.from`, `Object.keys/values/entries`, `JSON.stringify`/`util.inspect` over anything but a literal, or an array `join` — outside any arrow or function passed as an argument, which the logger calls only when the level is on — with no enclosing `if`, `&&` or `?:` whose condition names a level, a level string, or the `NODE_ENV`/`__DEV__`/`DEV` build switch. Deterministic, provable per finding. Advisory.

X24 · Document value interpolated into markup unescaped10.0 / 10Exemplary○ Nothing flagged

Other · Security — Whether text read out of the document being converted is escaped before it is written into generated markup — a value the document's author chose, interpolated into an attribute the surrounding literal delimits, can close that attribute and open another.

Method: Roslyn semantic model over the whole compilation: a string-typed `Value`/`InnerText`/`InnerXml`/`Text` member declared inside `DocumentFormat.OpenXml` or `System.Xml` is a taint SOURCE, propagated through assignments, returns, arguments, tuple elements and string composition to its transitive closure, then read at interpolated-string holes that sit in a markup position the surrounding literal itself delimits. Escaper/encoder calls and enclosing validator conditions cut the flow. Flow- and container-insensitive by construction. A second arm needs no provenance at all and reports a type that CONTRADICTS ITSELF — the same expression escaped at one delimited markup hole and interpolated raw at another hole in the same markup position of the same type, which the type's own escaping proves is a defect without knowing where the value came from. On a repository with no .NET source it reads JavaScript/TypeScript off the token stream with the same rule: a DOM read of raw document text (`getAttribute`, `textContent`, `innerText`, `nodeValue`) is the source, propagated through local bindings and string composition, and judged at template-literal and concatenation holes in the same two delimited markup positions; escapers and validating conditions cut it, and documentation-site, test, vendored and minified scripts are not read. Deterministic, provable per finding. Advisory.

X25 · Inert configuration knob10.0 / 10Exemplary○ Nothing flagged

Other · Code Health — Whether a value the caller is invited to supply is the value the type actually uses — a constructor parameter stored in a private field that nothing ever reads while the default it was given is spelled out a second time at the site that should have read it, a keyed lookup that falls back to a different setting than the one its key names while the same type falls back to the matching one for that same key, or a culture-sensitive parse given no format provider by a type that feeds its own settable culture to the same kind of parse elsewhere, or a number an entry point writes to standard output under the host's culture while the same entry point formats with the invariant culture elsewhere. Either way, every caller who supplies a value silently gets something else.

Method: Roslyn syntax: private instance fields of a non-partial type assigned in a constructor from one of its own parameters with a `??` fallback, checked for whether anything in the type body reads the field and whether that same fallback expression is spelled out again outside the constructor; and `??` fallbacks onto a member access from a lookup call carrying exactly one string literal, grouped by that key across the type and checked for a fallback member whose folded name disagrees with the key while a sibling site for the same key agrees with it. On a repository with no .NET source the first two arms read JavaScript/TypeScript off the engine’s own token stream (tests included, bundles and vendored paths not): a `#x`, `private` or `private` parameter-property instance field filled in the constructor from a parameter (or one member of one) through `??`/`||` or a parameter default, never read anywhere in the file by name, whose constructed default is spelled again in the class body; and `lookup("key") ?? s.member` grouped by key per class, or per module outside every class. The culture arm has no JavaScript counterpart: its parses take no locale. Deterministic, provable per finding. Advisory.

X26 · Unsynchronised callback handoff10.0 / 10Exemplary○ Nothing flagged

Other · Code Health — Whether a value handed from a callback to the body that waits on it crosses on something built to be crossed — a `Queue<T>`/`List<T>`/`Dictionary<K,V>` written inside an event handler and read back outside it is mutated by two flows at once, and the semaphore or completion source beside it orders how MANY items exist while leaving the collection's own head, tail and backing array unprotected.

Method: Roslyn syntax: method, accessor, local-function and lambda bodies that declare BOTH a non-thread-safe generic collection (`Queue`/`Stack`/`List`/`Dictionary`/`HashSet`/`Sorted*`/`LinkedList`) and a synchronisation primitive (`SemaphoreSlim`/`TaskCompletionSource`/`ManualResetEvent(Slim)`/`AutoResetEvent`/`CountdownEvent`) as locals, then read for a `+=`-registered lambda that raises that primitive while the body outside every lambda waits on it — and, in that scope, a mutating call on the collection inside the lambda paired with a mention of it outside. Any `lock` in the scope abstains it. On a repository with no .NET source the same handoff is read in Java off the engine’s own token stream (test source sets, vendored and demonstration paths not): an `ArrayList`/`LinkedList`/`ArrayDeque`/`PriorityQueue`/`Hash*`/`LinkedHash*`/`Tree*` local and a `CountDownLatch`/`Semaphore`/`CompletableFuture` local, a lambda or anonymous class that raises the primitive and mutates the collection, and a wait outside it; any `synchronized` or `lock()` abstains the body. Because each of those primitives orders what the callback wrote before raising it, only a touch that provably overlaps the callback is convicted: one after the registration and before the next wait, or one in a loop registered-before, waiting on every pass and not declaring the collection. Deterministic, provable per finding. Advisory.

X27 · Collection changed while being enumerated10.0 / 10Exemplary○ Nothing flagged

Other · Code Health — Whether an enhanced `for` leaves the collection it is walking alone — a body that adds to or removes from the very collection the loop is iterating invalidates the fail-fast iterator it is holding, so the next `next()` throws `ConcurrentModificationException` and the remaining elements are never seen. Whether a `for` loop leaves the `dict`, `set` or `deque` it is walking alone — CPython's iterators over those check that the collection has not changed size since the loop began, so a body that removes from (or empties) the very collection the loop is walking makes the next step raise `RuntimeError: dictionary changed size during iteration` and the remaining items are never seen. Whether a `for` comprehension or `foreach` leaves the mutable collection it is walking alone — an `ArrayBuffer` or `ListBuffer` counts its changes and its iterator checks the count, an `ArrayDeque` (and `mutable.Queue`/`mutable.Stack`) is walked by index up to its starting length, and a `java.util` collection walked through `asScala` is fail-fast, so a body that adds to or removes from the very collection the loop is walking makes a later step throw `ConcurrentModificationException` (`IndexOutOfBoundsException` for a deque) and the remaining elements are never seen.

Method: Roslyn syntax + semantics: `foreach` statements whose body calls a structural mutator (`Add`/`Remove`/`Clear`/`Insert`/…) on the very expression the loop is enumerating. Two arms. ARM A — the source is a concrete fragile BCL collection, or a live `Keys`/`Values` view over one, and the mutator resolves to that same collection's own member; concurrent and immutable collections and arrays are outside the population by construction, since their enumerators survive a structural change. ARM B — the source is an argument-less accessor CALL on a receiver whose body is in source: the accessor must return a stored field VERBATIM and a sibling member must structurally change that same field, both read off the implementations rather than from the members' names. A mutation the loop provably exits immediately after (`break`/`return`/`throw`/`goto`), or one written inside a nested loop or a lambda, is counted and never reported. Deterministic, provable per finding. Advisory.

X28 · Index access outside its own emptiness guard10.0 / 10Exemplary○ Nothing flagged

Other · Code Health — Whether a condition that tests a value for emptiness indexes that same value only where the test holds — an `||` written one parenthesis too far to the left leaves an index access outside the guard beside it, so the empty case the guard exists to anticipate reaches the index and throws.

Method: Roslyn syntax only, no semantic model: the OUTERMOST `&&`/`||` of every boolean condition, read for a symbol the condition tests for emptiness (`string.IsNullOrEmpty`/`IsNullOrWhiteSpace`, a `Length`/`Count` comparison against a literal, `Any()`, a `Length`/`Count` pattern, or a comparison against `""`) and ALSO indexes. Each `symbol[...]` access is placed by a boolean-reachability walk from the access up to the outermost connective: an access is COVERED when some enclosing step has it in the right operand and the left operand, under the truth value that step forces, proves the symbol non-empty — a recursion over `&&`/`||` whose true- and false-directions are asymmetric. A finding needs BOTH an uncovered access and a covered one on the same symbol in the same condition, which is the agreeing twin that separates a misplaced parenthesis from an unrelated length test. Bare index accesses with no emptiness test in the condition are neither counted nor reported; a non-identifier receiver and a lambda nested inside the condition are outside the population. Deterministic, provable per finding. Advisory.

X29 · Per-element action decided by a fixed element10.0 / 10Exemplary○ Nothing flagged

Other · Code Health — Whether a decision taken once per element is taken ABOUT that element — a test inside a counted loop that reads a fixed subscript of the very collection its guarded statement indexes by the loop variable applies element zero's answer to all of them, so the elements that differ from it are all handled wrongly, and in the same direction.

Method: Roslyn syntax only, no semantic model: every `for` statement declaring exactly ONE loop variable, and every `if` inside its body that is not under a nested loop or a lambda. A site enters the population when the `if`’s condition never mentions the loop variable while the statement it guards indexes some collection by that variable ALONE (`c[i]`; `c[i + 1]` and `c[i, j]` are outside it). A finding additionally needs the AGREEING TWIN at the same-collection grain: the condition must read THAT SAME collection at a subscript that does not move — written into the condition, or reached through a local declared BEFORE the loop, so an alias bound inside the body is not followed. Both collection expressions must be simple identifiers. On a repository with no .NET source the same rule reads JavaScript/TypeScript off the engine’s own token stream (tests included, bundles and vendored paths not): a `for (let|var|const x = …; …; …)` with one declarator and a braced body, an alias followed only when it is declared before the loop in a block that encloses it and never assigned inside the loop. Deterministic, provable per finding. Advisory.

X3 · Exception handling6.6 / 10Adequate✓ Tool-verified

Other · Code Health — Whether exceptions are handled rather than silently swallowed or rethrown with lost stack traces.

Method: Roslyn syntax scan: every catch clause counted; empty catches and bare rethrows flagged. Population is all catch clauses, not estimated. Deterministic, hard fact.

  • `catch (Exception)` takes every exception and records none of it — the body neither logs it, rethrows it, nor even names it, so the failure is discarded as completely as by an empty catch and only the substituted value survives. Log it through whatever this codebase already uses to report problems, narrow the catch to the exception this call can actually raise, or say in a comment on the catch why the failure genuinely cannot matter. — REDACTED:1415
  • `case NonFatal(_)` takes every exception and records none of it — the body neither logs it, rethrows it, nor even names it, so the failure is discarded as completely as by an empty catch and only the substituted value survives. Log it through whatever this codebase already uses to report problems, narrow the catch to the exception this call can actually raise, or say in a comment on the catch why the failure genuinely cannot matter. — REDACTED:187
  • `case _: Exception` takes every exception and records none of it — the body neither logs it, rethrows it, nor even names it, so the failure is discarded as completely as by an empty catch and only the substituted value survives. Log it through whatever this codebase already uses to report problems, narrow the catch to the exception this call can actually raise, or say in a comment on the catch why the failure genuinely cannot matter. — src/main/scala/ai/starlake/utils/Utils.scala:240

What to do

  • Swallowed exception (caught, then discarded)
X30 · Support guard that admits what it rejects10.0 / 10Exemplary○ Nothing flagged

Other · Code Health — Whether a guard written as a NEGATED `||` says what its author meant — `!(a || b || x != k)` is `!a && !b && x == k` by De Morgan, so a bail-out that mixes capabilities the code needs with a fault it refuses turns inside out: it fires only where the capabilities are ABSENT, and lets every value the fault term names walk straight into the body that cannot handle it.

Method: Roslyn syntax only, no semantic model: every logical-not whose operand is a parenthesised `||` chain of two or more disjuncts, flattened (a left-nested `a || b || c` read once would see `(a || b)` as one disjunct). A site enters the population on that shape alone. A finding additionally needs the disjuncts to DISAGREE in polarity: at least one bare boolean read — an identifier or member access, never an invocation, which is a predicate rather than a capability flag — and at least one `x != <constant>`, the only form that negates into an exact-value pin (`== null` negates into a looser requirement and is outside the fault set). Consistently-polarised disjunctions, all-fault or all-capability, are counted and never reported; a negated `&&` is outside the population entirely. No same-receiver gate: it was measured to cost a real defect and remove no false positive. Deterministic, provable per finding. Advisory.

X5 · Nullable reference types2.0 / 10Critical✓ Tool-verified

Other · Code Health — Whether Scala's null is held to the type system and not undermined by assertions — a file compiled with `-Yexplicit-nulls` is one where `String` excludes null and `String | Null` admits it, a file under WartRemover's `Null` wart or Scalafix's `DisableSyntax.noNulls` is one whose code cannot write `null` and must say `Option`, and each `.nn`, `import scala.language.unsafeNulls` or null-lint suppression is the author overruling that.

Method: Roslyn compiler-options scan: NullableContextOptions per project; null-forgiving (!) suppression density per 1k syntax nodes. Deterministic, adoption plus suppression penalty.

  • No build over this repository's 380 Scala product file(s) passes `-Yexplicit-nulls`, enables WartRemover's `Null` wart or runs Scalafix's `DisableSyntax.noNulls`, so every reference type admits null and nothing reports a `null` written into the code or returned from a Java API — the first one to reach a method call is a `NullPointerException`. `Option` by convention is a habit no tool here holds the code to.

What to do

  • On Scala 3, add `-Yexplicit-nulls` to `scalacOptions` so `String` and `String | Null` mean different things, and type each Java API's result as nullable where it can return null; on Scala 2 (or as a first step), enable WartRemover's `Null` wart (`wartremoverErrors += Wart.Null`) or Scalafix's `DisableSyntax.noNulls = true` so the code holds absence in `Option`. Then pay the assertions down rather than adding to them: each `.nn` is a null the compiler could not rule out — wrap the Java value in `Option(…)` at the boundary, pattern-match on `null` once there, or change the API so the null cannot arrive; an `unsafeNulls` import switches the checking off for everything after it.

WCAG coverage — what static analysis assessed

Statically assessed 12 of 55 WCAG 2.2 Level A/AA success criteria (22%; ≈24% of the 50 WCAG 2.1 AA criteria for EN 301 549). The other 43 require runtime or manual evaluation. Partial signal only (a clean result is necessary, not sufficient; static analysis fully verifies none). This is accessibility readiness, not a conformance claim — a WCAG conformance claim requires manual evaluation (WCAG-EM 1.0).

DimensionWCAG 2.2 A/AA criteriaCoverage
AC2 · Forms & labels1.3.1, 3.3.2, 4.1.2Partial signal
AC3 · Page structure1.4.4, 2.2.1, 2.4.1, 2.4.2, 3.1.1, 4.1.2Partial signal
AC4 · Keyboard semantics2.1.1, 2.4.3Partial signal
AC6 · Visual & motion safety1.4.3, 2.4.7Partial — literal CSS only
AC7 · A11y enforcementenforcement — no page criterionEnforcement posture (process)

Not statically assessed — these 43 Level A/AA criteria need runtime or manual evaluation (WCAG-EM): 1.1.1, 1.2.1, 1.2.2, 1.2.3, 1.2.4, 1.2.5, 1.3.2, 1.3.3, 1.3.4, 1.3.5, 1.4.1, 1.4.2, 1.4.5, 1.4.10, 1.4.11, 1.4.12, 1.4.13, 2.1.2, 2.1.4, 2.2.2, 2.3.1, 2.4.4, 2.4.5, 2.4.6, 2.4.11, 2.5.1, 2.5.2, 2.5.3, 2.5.4, 2.5.7, 2.5.8, 3.1.2, 3.2.1, 3.2.2, 3.2.3, 3.2.4, 3.2.6, 3.3.1, 3.3.3, 3.3.4, 3.3.7, 3.3.8, 4.1.3.

Reference — by lens

The score is the rank-weighted fold of these lenses (worst-heaviest), each including its meta-dimensions; a lens with a Critical contributor is capped at Fair (its band reads "gated by …") and is never the strongest area however high its average.

LensScoreRatingImpact
Code Health61%Adequate — gated by X5Capped at Fair by a Critical contributor — resolve it before relying on this lens.
Architecture85%Adequate — gated by D26Capped at Fair by a Critical contributor — resolve it before relying on this lens.
Maturity71%Adequate — gated by D16Capped at Fair by a Critical contributor — resolve it before relying on this lens.
Readiness50%Adequate — gated by D13, P3Capped at Fair by a Critical contributor — resolve it before relying on this lens.
Security35%Weak — gated by D28, D29, S1Capped at Fair by a Critical contributor — resolve it before relying on this lens.
Accessibility41%Weak — gated by AC4Capped at Fair by a Critical contributor — resolve it before relying on this lens.
Unscored — 3 check(s) recorded observations but carry no score

These checks ran and found something, but they do not carry a score — either by design (an advisory check reports evidence rather than grading it) or because they could not be scored here. They are excluded from the score for that reason, not because there was nothing to see.

  • X10 Duplicated predicate — 6 observation(s) recorded · Advisory — this card reports evidence and never carries a score.
  • X6 Hand-rolled structured-format parsing — 1 observation(s) recorded · Advisory — this card reports evidence and never carries a score.
  • X7 Silent fallback defaults — 1 observation(s) recorded · Advisory — this card reports evidence and never carries a score.
Not evidenced — 4 control(s) we could not find positive evidence for

These checks grade a working control, and the repository shows no evidence of one. That is deliberately not scored as a zero: a repository cannot show an ops runbook, a database TTL or an infrastructure-side audit log, so absence of evidence here is not evidence the control is missing. It is also not a statement that the check is irrelevant to this codebase — the thing it grades applies; we just could not see it. Excluded from the score either way.

  • C3 Audit Trail — Not assessed: this repository stores data through 'java.sql.DriverManager' (src/main/scala/ai/starlake/config/Settings.scala:53) but declares no persisted model this check reads (Slick table columns, CREATE TABLE and ALTER TABLE … ADD columns in SQL migrations). Personal data may be written through raw SQL or a hosted backend, so whether its changes need an audit trail could not be established, and the trail's absence is not scored.
  • C4 Data Retention — Not assessed: this repository stores data through 'java.sql.DriverManager' (src/main/scala/ai/starlake/config/Settings.scala:53) but declares no persisted model this check reads (Slick table columns, CREATE TABLE and ALTER TABLE … ADD columns in SQL migrations). Personal data may be written through raw SQL or a hosted backend, so whether a retention limit applies here could not be established, and its absence is not scored.
  • C5 Data-Subject Rights — Not assessed: this repository stores data through 'java.sql.DriverManager' (src/main/scala/ai/starlake/config/Settings.scala:53) but declares no persisted model this check reads (Slick table columns, CREATE TABLE and ALTER TABLE … ADD columns in SQL migrations). Personal data may be written through raw SQL or a hosted backend, so whether data-subject rights apply here could not be established, and their absence is not scored.
  • P5 DR & Backup — not evidenced — repo shows no backup/RTO/RPO controls; absence of evidence is not evidence of a working control
Not included — 46 check(s) not relevant to this codebase

These checks had nothing to measure here (no tests, no git history, the codebase is small, or the architecture style doesn't apply), so they're omitted above rather than scored low.

  • AC1 Text alternatives — AC1 could not be measured here. This repository renders user interface from a template language — 2 Jinja template file(s) carrying 258 markup element(s) (e.g. src/main/resources/templates/site/standalone/table.j2, src/main/resources/templates/site/standalone/task.j2) — which this analyzer's markup model does not read (it reads .html/.htm/.cshtml/.razor/.vue/.svelte/.jsx/.tsx, html`…` templates and DOM-assigned HTML strings in .ts/.js, the .erb/.ejs/.eex/.heex/.leex/.twig/.jinja/.j2/.liquid/.njk/.hbs/.mustache/.blade.php/.gohtml template languages, .astro/.templ/.pug/.jade/.haml/.slim, and the kotlinx.html/Compose HTML/kotlin-react, Giraffe/Falco/Fable.React/Feliz/Bolero and Jaspr builder DSLs), so no image/media element from it could enter the population. The parsed markup carried none either, but that is a gap in our frontend coverage, NOT a statement that this repository has no image/media element.
  • AC5 ARIA correctness — AC5 could not be measured here. This repository renders user interface from a template language — 2 Jinja template file(s) carrying 258 markup element(s) (e.g. src/main/resources/templates/site/standalone/table.j2, src/main/resources/templates/site/standalone/task.j2) — which this analyzer's markup model does not read (it reads .html/.htm/.cshtml/.razor/.vue/.svelte/.jsx/.tsx, html`…` templates and DOM-assigned HTML strings in .ts/.js, the .erb/.ejs/.eex/.heex/.leex/.twig/.jinja/.j2/.liquid/.njk/.hbs/.mustache/.blade.php/.gohtml template languages, .astro/.templ/.pug/.jade/.haml/.slim, and the kotlinx.html/Compose HTML/kotlin-react, Giraffe/Falco/Fable.React/Feliz/Bolero and Jaspr builder DSLs), so no ARIA usage from it could enter the population. The parsed markup carried none either, but that is a gap in our frontend coverage, NOT a statement that this repository has no ARIA usage.
  • AX1 Captive dependencies — Not applicable: this repository's Java imports no dependency-injection container and defines no container of its own — nothing that both registers and resolves bindings, and nothing that names two lifetimes — so nothing holds one lifetime's instance while handing out another's; this repository's Python imports no dependency-injection container and defines no container of its own — nothing that both registers and resolves bindings, and nothing that names two lifetimes — so nothing holds one lifetime's instance while handing out another's; this repository's Scala imports no dependency-injection container and defines no container of its own — nothing that both registers and resolves bindings, and nothing that names two lifetimes — so nothing holds one lifetime's instance while handing out another's.
  • AX2 Stateful singletons — No container singleton was found, so there is no shared instance for concurrent requests to race on. No class in this Java code is a Spring, JSR-330 or CDI singleton bean. No function in this Python code is served by a threaded web framework (a Flask, Bottle or FastAPI route, a Django or Pyramid view), so no module is shared between request threads. No class in this Scala code is a Play controller, a Cask routes object, a Scalatra servlet, a Finatra controller, an Akka/Pekko HTTP routes holder or a JSR-330/Spring singleton, so no instance is shared between request threads. TypeScript/JavaScript runs each process's requests on one event loop, so no two requests write a shared object at the same instant (interleaving across an await is a different defect).
  • AX5 Architecture & structure — not assessed — architecture style/structure is computed from a project graph (projects, types, module namespaces) that was not loaded for this repository, because the repository is written in a language this check does not yet model, or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository
  • AX7 Slice cohesion — not applicable — not a vertical-slice architecture
  • AX8 Test isolation — no test/production split to check
  • AX9 CQS / query purity — no CQRS query handlers detected — query purity is not applicable to this codebase
  • AXB2 Runtime readiness — Advisory — this card reports evidence and never carries a score, so there is nothing missing here.
  • AXR1 Runtime accessibility — docker build failed (exit 1) — DEPRECATED: The legacy builder is deprecated and will be removed in a future release. Install the buildx component to build images with BuildKit: https://docs.docker.com/go/bui…; runtime evidence skipped This is a statement about this run, not a statement about your application: nothing here says the surface is inaccessible, only that it was never rendered.
  • C1 Data Protection — Not assessed: these personal data controls are read from a source model (declarative annotations, request middleware, entity/column names, guard methods) that was not loaded for this repository — because the repository is written in a language this check does not yet model, or because its projects failed to load. Absence of an idiom this check recognises is NOT evidence that this repository lacks personal data controls: it may implement them entirely in its own ecosystem. This is a gap in the analyzer's language coverage, not a finding about this repository.
  • C2 Access Controls — Not assessed: these authorization controls are read from a source model (declarative annotations, request middleware, entity/column names, guard methods) that was not loaded for this repository — because the repository is written in a language this check does not yet model, or because its projects failed to load. Absence of an idiom this check recognises is NOT evidence that this repository lacks authorization controls: it may implement them entirely in its own ecosystem. This is a gap in the analyzer's language coverage, not a finding about this repository.
  • D10 Test Quality — ~34380 lines of test source are present (.scala) but no test cases reached the test census for this repository — no test root we could resolve declared them, or the files we read declare no cases in a test framework we recognise — so skipped/assertion-free tests couldn't be counted. Not scored — this is a gap in the analyzer, not a finding about this repository.
  • D11 Test Reliability — Test reliability not included — the .scala suite was found but not re-run
  • D18 Solution Shape — D18 scores the shape of a .NET solution; this repository has no .NET solution or project files, so the dimension does not apply.
  • D19 Documentation Quality — LLM evaluation failed
  • D22 Internal API Consistency — The exposed public-API surface could not be collected — no C#/VB projects loaded.
  • D23 Boundary Type-Coupling — No bounded-context organisation was detected either — neither a context-shaped layout nor 2+ sibling source directories each declaring an aggregate root. Declaring this codebase's bounded contexts (≥2) would let cross-boundary type coupling be assessed. Declare them in `.codehealth/config.yaml` at the repository root (create it if absent), mapping each context name to the module-path or namespace prefixes that belong to it — e.g. `architecture:` → `contexts:` → `Billing: ["src/billing", "Acme.Billing"]`, `Catalog: ["src/catalog", "Acme.Catalog"]`.
  • D24 Comment Value — No inline comments to assess — comment value is not applicable here.
  • D25 ADR Conformance — no ADRs to check
  • D27 Navigability — D27 counts a call into the standard library or a dependency as unresolved for this repository's language (.scala), so too few call sites counted as resolved to assess navigability. Not scored: this is a gap in the analyzer, not a verdict about this repository.
  • D32 Data Compliance (PII/GDPR) — 3 file(s) were not parsed by semgrep — the PII/GDPR ruleset never ran over them
  • D37 Vulnerability-disclosure Policy — No vulnerability-disclosure policy file found (SECURITY.md/.markdown/.rst/.txt at root or under .github/.forgejo/.gitea/docs, .well-known/security.txt). A coordinated-disclosure policy may live off-repo, so this is not evidenced rather than failed.
  • D39 IL Efficiency — D39 measures the IL emitted by a .NET build; this repository has no .NET solution or project files, so the dimension does not apply.
  • D40 Network Egress Confinement — No Kubernetes/orchestration workloads found in the repository manifests; network egress policy is a cluster-native control that may live at the platform/firewall layer, so there is nothing to assess here.
  • D41 Kernel & Syscall Confinement — No Kubernetes/orchestration workloads found in the repository manifests; seccomp/AppArmor/SELinux confinement is a workload-level control, so there is nothing to assess here.
  • D42 Runtime Threat Enforcement — No Kubernetes/orchestration workloads found in the repository manifests; runtime threat-detection and admission-control policy are cluster-level controls, so there is nothing to assess here.
  • D44 Platform End-of-Life — Platform end-of-life not assessed — this repository declares no platform this pass reads
  • D5 Coupling — Not applicable — this sbt build ships 1 production module(s), so there is no coupling BETWEEN modules to measure. (Its test and non-production modules are not part of the shipped graph.)
  • D7 Architectural Integrity — no checkable ADRs, and no project-reference graph for the cycle pass to read — so this dimension makes no claim about dependency cycles in either direction (where this repository's language has an import-cycle lens, cycles are reported there). Architectural integrity not assessed
  • D8 Code Coverage — Coverage not included — suite not readable by the collector
  • DM1 Domain Modelling — applicable but not scored (2 signals for this style, 2 needed — the count is met but a required primary signal is absent): 180 value object(s); 1 domain event(s); its domain events are published by services or handlers — no domain entity raises one
  • ED1 Event-Driven — not scored — this repository shows none of the 3 signals this lens looks for
  • ED5 Idempotency — no mutating command handlers or message consumers detected — idempotency check not applicable
  • ES1 Event Sourcing — not scored — this repository shows none of the 3 signals this lens looks for
  • P12 CI test-gate honesty — Reported, not scored — and nothing was matched here. The coverage check applies to any stack, and the automatic-re-run check to any GitHub-Actions workflow, but the checks for excluded tests, skipped tests and sleep-based synchronisation currently recognise only some ecosystems' test-runner idioms, so on a repository built with another stack the zeros below mean 'not checked', not 'clean'.
  • P7 Outbound HTTP resilience — not applicable — no HTTP server, API framework or worker entry point was found in the JVM, JavaScript/TypeScript, Python source, so there is no service whose uptime a failing dependency could take down
  • P8 Schema migrations — no ORM, schema-migration tool or schema auto-create was found in this repository's dependency manifests or source, so there is no database schema for this check to judge
  • P9 Domain vs controller coverage — no coverage report found on disk — produce a coverage report in a standard format (`addSbtPlugin("org.scoverage" % "sbt-scoverage" % "<version>")` in `project/plugins.sbt`, then `sbt clean coverage test coverageReport`) and commit it — a hosted scan measures a clone of the repository, so a report that exists only in a working tree, a CI runner's or your own, never reaches it; the artefact is commonly gitignored, so `git add -f` that one file (or un-ignore its path) and commit it alongside the code it measures, or wire coverage collection into CI, to enable this cross-layer check
  • PF1 Benchmark discipline — Not applicable: no benchmark suite was found. This check searched for `@Benchmark` in a Java, Kotlin or Scala file importing JMH or kotlinx-benchmark, Jetpack Benchmark's `measureRepeated` in a file importing androidx.benchmark, or JMH/kotlinx-benchmark/androidx.benchmark in a pom.xml, Gradle or sbt build, and for a `*benchmark*` script that this repository's CI runs. Benchmarks are credited as a bonus, so their absence is neither scored nor deducted.
  • PF2 Allocation hygiene — Not applicable: allocation awareness is rated where code engineers for it — at least 400 production lines that ship benchmarks or already use allocation-aware idioms (8 or more). This repository's Java and Scala code has 71,332 production line(s), 6 such use(s) and no benchmarks, so there is no allocation work to rate. The card is reward-only: its absence costs nothing.
  • PF3 Async & latency hygiene — Not applicable: this repository declares no async functions, so there is no asynchronous code for a blocking call to stall.
  • X2 Cancellation propagation — no C# was loaded; this repository's Scala, Java and Python were read, and no request in it can be cancelled by a caller — a Scala request takes no token: a Future cannot be cancelled, and cats-effect, ZIO and Monix interrupt the fiber that made the request from outside the call, not through a parameter; a Java request off Android takes no token: it is cancelled through the handle it returns (Future.cancel, Call.cancel, a Disposable), ambiently (io.grpc.Context) or by interrupting its thread, not through a parameter; a Python request takes no token: asyncio cancels the task and trio or anyio the scope that made it, from outside the call, and requests, httpx, aiohttp and gRPC take a timeout, not a cancellation
  • X32 Type resolved by simple name across every loaded assembly — This check is about how a .NET program searches the assemblies loaded into its process for a type, and this repository contains no .NET source, so there is nothing here for it to assess. Not a gap in the analyzer and not a finding about your code.
  • X4 Structured logging — This check asks whether log calls keep a message's values apart from its text, which needs a logger that can carry them as fields. This repository's Java was read, and it logs through none that an assembled message defeats: java.util.logging's level methods, log4j 1, commons-logging, JBoss Logging and Flogger take one finished message (as log4net does in C#), or it makes no call through a named logger at all. Calls made through SLF4J or log4j 2 (`org.slf4j.Logger`, `org.apache.logging.log4j.Logger`, Lombok's `@Slf4j`) would be judged.
  • X9 Subsumed condition operand — Advisory — this card reports evidence and never carries a score, so there is nothing missing here.

Appendix A — Findings (grouped)

The findings behind the scores, grouped by severity, then by dimension and kind. The high-severity issues are enumerated in full below; items per group are capped at 25 with any overflow stated explicitly per group, never silently truncated. The complete machine-readable list of every finding (all severities) is the companion findings.md in this report's bundle.

Critical — 279 finding(s)
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D29 · Static Analysis (SAST) · REDACTED
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  • + 13 more in this group — see findings.md.
D28 · Secrets (history) · High secret · ×30
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D28 · Secrets (history) · REDACTED · ×5
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D29 · Static Analysis (SAST) · REDACTED
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D13 · REDACTED Scanning · Leaked secret · ×4
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AC4 · Keyboard semantics · Click handler on a non-interactive <h3> · ×3
  • Click handler on a non-interactive <h3> src/main/resources/templates/compare/index.html.j2:99 — A click handler on a plain element isn't keyboard-operable. Use a <button>, or add role + tabindex="0" + a key handler.
  • Click handler on a non-interactive <h3> src/main/resources/templates/compare/index.html.j2:136 — A click handler on a plain element isn't keyboard-operable. Use a <button>, or add role + tabindex="0" + a key handler.
  • Click handler on a non-interactive <h3> src/main/resources/templates/compare/index.html.j2:252 — A click handler on a plain element isn't keyboard-operable. Use a <button>, or add role + tabindex="0" + a key handler.
D29 · Static Analysis (SAST) · REDACTED
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D29 · Static Analysis (SAST) · REDACTED · ×2
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D29 · Static Analysis (SAST) · REDACTED · ×2
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D30 · Dependency Vulnerabilities · Critical CVE · ×1
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D31 · IaC & Container Security · High IaC · ×1
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D34 · Knowledge Freshness · Orphaned knowledge · ×1
  • Orphaned knowledge src/main/scala/ai/starlake/utils/YamlMigrator.scala:1 — No living knowledge remains for this large file — its last meaningful change has decayed away; if it breaks, no one currently understands it. Schedule a read-through / add characterisation tests before it bites.
S1 · Web-Security Posture · TLS certificate validation disabled · ×1
  • TLS certificate validation disabled REDACTED:721 — an `X509TrustManager` whose `checkServerTrusted` accepts every chain is set on a TLS client, so every peer certificate is accepted — including one an attacker presents. Chain, expiry, revocation and hostname are all skipped, so an attacker on the network path can present any certificate and read or rewrite the traffic — the connection is encrypted to the attacker, not to the intended host. Remove the override so the JVM's default trust manager and hostname check apply — that is what `SSLContext.getDefault`, Netty's `SslContextBuilder.forClient()`, sttp, Play WS and ssl-config use when nothing is set. If the server presents a self-signed or privately-issued certificate, trust THAT issuer rather than every certificate: load its CA into a `KeyStore` and build a `TrustManagerFactory` from it (ssl-config: `trustManager.stores`), which keeps the chain and hostname checks on.
Serious — 352 finding(s)
D17 · Explicit Debt · TodoComment · ×43
  • TodoComment src/main/java/ai/starlake/utils/repackaged/BigQuerySchemaConverters.java:396 — // TODO(#5): add a timezone to allow parsing to timestamp
  • TodoComment REDACTED:26 — // todo get it from settings — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `// REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
  • TodoComment src/main/scala/ai/starlake/extract/ExtractSchema.scala:92 — // TODO: temporarily doing switch case but plans to remove it once all others extractions implement Schema Extractor — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `// REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
  • TodoComment src/main/scala/ai/starlake/extract/ExtractSchema.scala:93 — // TODO: implement templates as in jdbcSchema — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `// REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
  • TodoComment src/main/scala/ai/starlake/job/ingest/JsonIngestionJob.scala:79 — // TODO send rejected records to rejected area — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `// REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
  • TodoComment src/main/scala/ai/starlake/job/ingest/IngestionUtil.scala:53 — // TODO: Wondering if we should remove file name in errors and rely on err.path for rejectedPathName instead — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `// REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
  • TodoComment src/main/scala/ai/starlake/job/ingest/AuditLog.scala:253 — // TODO handle gcp log when builing statements — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `// REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
  • TodoComment src/main/scala/ai/starlake/job/ingest/AuditLog.scala:257 — // TODO: if detailed load audit is set to true and sink is bigquery, we may generate a query that is too long — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `// REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
  • TodoComment src/main/scala/ai/starlake/job/ingest/loaders/NativeLoader.scala:320 — // TODO Implement Expectations — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `// REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
  • TodoComment REDACTED:679 — // TODO Implement Expectations — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `// REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
  • TodoComment src/main/scala/ai/starlake/job/ingest/loaders/BigQueryNativeLoader.scala:157 — // TODO What if type is changed by transform ? we need to use safe_cast to have the same behavior as in SPARK. — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `// REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
  • TodoComment src/main/scala/ai/starlake/job/sink/bigquery/BigQuerySparkWriter.scala:90 — // TODO Sink Audit Log to ES — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `// REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
  • TodoComment src/main/scala/ai/starlake/job/sink/bigquery/BigQuerySparkJob.scala:272 — // TODO verify if there is a difference between maybeTableDescription, schema.comment , task.desc — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `// REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
  • TodoComment src/main/scala/ai/starlake/job/sink/jdbc/JdbcConnectionLoadCmd.scala:82 — // TODO: wanted to just call this "apply" but we need to get rid of the defaults in the ctor above — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `// REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
  • TodoComment src/main/scala/ai/starlake/job/sink/jdbc/JdbcConnectionLoadCmd.scala:92 — // TODO: Once spark 3.3 is not supported anymore, switch to withColumnsRenamed(colsMap: Map[String, String]) — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `// REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
  • TodoComment src/main/scala/ai/starlake/job/transform/SparkExportTask.scala:241 — // TODO if a template has been defined, we don't want to simply create a new sheet — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `// REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
  • TodoComment REDACTED:840 — // TODO declaration is never used — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `// REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
  • TodoComment src/main/scala/ai/starlake/job/transform/BigQueryAutoTask.scala:437 — // TODO Check that we are in the second step of the load — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `// REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
  • TodoComment src/main/scala/ai/starlake/lineage/TaskViewDependency.scala:19 — // TODO migrate to enum once scala 3 is here — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `// REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
  • TodoComment src/main/scala/ai/starlake/lineage/TaskViewDependency.scala:217 — // TODO We just handle one task per job which is always the case till now. — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `// REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
  • TodoComment src/main/scala/ai/starlake/lineage/TaskViewDependency.scala:336 — // TODO We just handle one task per job which is always the case till now. — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `// REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
  • TodoComment src/main/scala/ai/starlake/schema/handlers/SchemaHandler.scala:2445 — // todo — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `// REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
  • TodoComment src/main/scala/ai/starlake/schema/model/TransformInput.scala:62 — // TODO: remove colMap — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `// REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
  • TodoComment src/main/scala/ai/starlake/schema/model/TableAttribute.scala:304 — // TODO: Support NoSQL Documents — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `// REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
  • TodoComment src/main/scala/ai/starlake/schema/model/TableAttribute.scala:393 — // Not Supported date format for ES TODO : needs to be implemented — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `// REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
  • + 18 more in this group — see findings.md.
X12 · Unreachable branch · Switch arm can never match its case-normalised subject · ×28
  • Switch arm can never match its case-normalised subject src/main/scala/ai/starlake/job/metrics/Metrics.scala:17 — The match subject is `value.toUpperCase`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 16). The pattern `"continuous"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"CONTINUOUS"`), or drop the normaliser and compare with `equalsIgnoreCase`.
  • Switch arm can never match its case-normalised subject src/main/scala/ai/starlake/job/metrics/Metrics.scala:18 — The match subject is `value.toUpperCase`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 16). The pattern `"discrete"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"DISCRETE"`), or drop the normaliser and compare with `equalsIgnoreCase`.
  • Switch arm can never match its case-normalised subject src/main/scala/ai/starlake/job/metrics/Metrics.scala:19 — The match subject is `value.toUpperCase`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 16). The pattern `"frequencies"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"FREQUENCIES"`), or drop the normaliser and compare with `equalsIgnoreCase`.
  • Switch arm can never match its case-normalised subject src/main/scala/ai/starlake/schema/model/IndexMapping.scala:44 — The match subject is `value.toUpperCase()`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 43). The pattern `"text"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"TEXT"`), or drop the normaliser and compare with `equalsIgnoreCase`.
  • Switch arm can never match its case-normalised subject src/main/scala/ai/starlake/schema/model/IndexMapping.scala:45 — The match subject is `value.toUpperCase()`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 43). The pattern `"keyword"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"KEYWORD"`), or drop the normaliser and compare with `equalsIgnoreCase`.
  • Switch arm can never match its case-normalised subject src/main/scala/ai/starlake/schema/model/IndexMapping.scala:47 — The match subject is `value.toUpperCase()`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 43). The pattern `"long"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"LONG"`), or drop the normaliser and compare with `equalsIgnoreCase`.
  • Switch arm can never match its case-normalised subject src/main/scala/ai/starlake/schema/model/IndexMapping.scala:48 — The match subject is `value.toUpperCase()`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 43). The pattern `"integer"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"INTEGER"`), or drop the normaliser and compare with `equalsIgnoreCase`.
  • Switch arm can never match its case-normalised subject src/main/scala/ai/starlake/schema/model/IndexMapping.scala:49 — The match subject is `value.toUpperCase()`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 43). The pattern `"short"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"SHORT"`), or drop the normaliser and compare with `equalsIgnoreCase`.
  • Switch arm can never match its case-normalised subject src/main/scala/ai/starlake/schema/model/IndexMapping.scala:50 — The match subject is `value.toUpperCase()`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 43). The pattern `"byte"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"BYTE"`), or drop the normaliser and compare with `equalsIgnoreCase`.
  • Switch arm can never match its case-normalised subject src/main/scala/ai/starlake/schema/model/IndexMapping.scala:51 — The match subject is `value.toUpperCase()`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 43). The pattern `"double"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"DOUBLE"`), or drop the normaliser and compare with `equalsIgnoreCase`.
  • Switch arm can never match its case-normalised subject src/main/scala/ai/starlake/schema/model/IndexMapping.scala:52 — The match subject is `value.toUpperCase()`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 43). The pattern `"float"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"FLOAT"`), or drop the normaliser and compare with `equalsIgnoreCase`.
  • Switch arm can never match its case-normalised subject src/main/scala/ai/starlake/schema/model/IndexMapping.scala:53 — The match subject is `value.toUpperCase()`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 43). The pattern `"half_float"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"HALF_FLOAT"`), or drop the normaliser and compare with `equalsIgnoreCase`.
  • Switch arm can never match its case-normalised subject src/main/scala/ai/starlake/schema/model/IndexMapping.scala:54 — The match subject is `value.toUpperCase()`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 43). The pattern `"scaled_float"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"SCALED_FLOAT"`), or drop the normaliser and compare with `equalsIgnoreCase`.
  • Switch arm can never match its case-normalised subject src/main/scala/ai/starlake/schema/model/IndexMapping.scala:56 — The match subject is `value.toUpperCase()`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 43). The pattern `"date"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"DATE"`), or drop the normaliser and compare with `equalsIgnoreCase`.
  • Switch arm can never match its case-normalised subject src/main/scala/ai/starlake/schema/model/IndexMapping.scala:57 — The match subject is `value.toUpperCase()`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 43). The pattern `"boolean"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"BOOLEAN"`), or drop the normaliser and compare with `equalsIgnoreCase`.
  • Switch arm can never match its case-normalised subject src/main/scala/ai/starlake/schema/model/IndexMapping.scala:59 — The match subject is `value.toUpperCase()`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 43). The pattern `"binary"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"BINARY"`), or drop the normaliser and compare with `equalsIgnoreCase`.
  • Switch arm can never match its case-normalised subject src/main/scala/ai/starlake/schema/model/IndexMapping.scala:61 — The match subject is `value.toUpperCase()`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 43). The pattern `"integer_range"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"INTEGER_RANGE"`), or drop the normaliser and compare with `equalsIgnoreCase`.
  • Switch arm can never match its case-normalised subject src/main/scala/ai/starlake/schema/model/IndexMapping.scala:62 — The match subject is `value.toUpperCase()`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 43). The pattern `"float_range"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"FLOAT_RANGE"`), or drop the normaliser and compare with `equalsIgnoreCase`.
  • Switch arm can never match its case-normalised subject src/main/scala/ai/starlake/schema/model/IndexMapping.scala:63 — The match subject is `value.toUpperCase()`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 43). The pattern `"long_range"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"LONG_RANGE"`), or drop the normaliser and compare with `equalsIgnoreCase`.
  • Switch arm can never match its case-normalised subject src/main/scala/ai/starlake/schema/model/IndexMapping.scala:64 — The match subject is `value.toUpperCase()`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 43). The pattern `"double_range"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"DOUBLE_RANGE"`), or drop the normaliser and compare with `equalsIgnoreCase`.
  • Switch arm can never match its case-normalised subject src/main/scala/ai/starlake/schema/model/IndexMapping.scala:65 — The match subject is `value.toUpperCase()`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 43). The pattern `"date_range"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"DATE_RANGE"`), or drop the normaliser and compare with `equalsIgnoreCase`.
  • Switch arm can never match its case-normalised subject src/main/scala/ai/starlake/schema/model/IndexMapping.scala:67 — The match subject is `value.toUpperCase()`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 43). The pattern `"geo_point"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"GEO_POINT"`), or drop the normaliser and compare with `equalsIgnoreCase`.
  • Switch arm can never match its case-normalised subject src/main/scala/ai/starlake/schema/model/IndexMapping.scala:68 — The match subject is `value.toUpperCase()`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 43). The pattern `"geo_shape"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"GEO_SHAPE"`), or drop the normaliser and compare with `equalsIgnoreCase`.
  • Switch arm can never match its case-normalised subject src/main/scala/ai/starlake/schema/model/IndexMapping.scala:69 — The match subject is `value.toUpperCase()`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 43). The pattern `"ip"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"IP"`), or drop the normaliser and compare with `equalsIgnoreCase`.
  • Switch arm can never match its case-normalised subject src/main/scala/ai/starlake/schema/model/IndexMapping.scala:70 — The match subject is `value.toUpperCase()`, so every value reaching these cases has already been upper-cased by `toUpperCase` (line 43). The pattern `"completion"` carries a letter of the opposite case, and a literal pattern compares with `==`, so it cannot equal that subject for ANY input — this case is dead and whatever it was meant to handle silently falls to a later case, or throws `MatchError` when none is left. Match the normalised spelling (`"COMPLETION"`), or drop the normaliser and compare with `equalsIgnoreCase`.
  • + 3 more in this group — see findings.md.
D3 · God Classes · FileTooLong · ×16
  • FileTooLong: handlers/SchemaHandler.scala src/main/scala/ai/starlake/schema/handlers/SchemaHandler.scala — FileTooLong — 1747 significant lines (blank, comment-only and punctuation-only lines excluded). The bar is 500 significant lines; this is 1247 over it, 3.49× the bar. To reduce it, split the file along the responsibilities already in it: move each cohesive group of declarations into its own sibling file in the same module or package, so no one file has to be read whole to change one of them.
  • FileTooLong: bigquery/BigQueryJobBase.scala src/main/scala/ai/starlake/job/sink/bigquery/BigQueryJobBase.scala — FileTooLong — 968 significant lines (blank, comment-only and punctuation-only lines excluded). The bar is 500 significant lines; this is 468 over it, 1.94× the bar. To reduce it, split the file along the responsibilities already in it: move each cohesive group of declarations into its own sibling file in the same module or package, so no one file has to be read whole to change one of them.
  • FileTooLong: REDACTED REDACTED — FileTooLong — 956 significant lines (blank, comment-only and punctuation-only lines excluded), about 97% of them inside a single declaration: Setup (111-1579). The bar is 500 significant lines; this is 456 over it, 1.91× the bar. Moving the declarations that sit BESIDE it into sibling files will not shorten this file. Extract from INSIDE that declaration instead: lift each cohesive group of its body — the parts that share the same inputs and are named together — into its own unit in a sibling file, and have the original call them.
  • FileTooLong: config/Settings.scala src/main/scala/ai/starlake/config/Settings.scala — FileTooLong — 939 significant lines (blank, comment-only and punctuation-only lines excluded). The bar is 500 significant lines; this is 439 over it, 1.88× the bar. To reduce it, split the file along the responsibilities already in it: move each cohesive group of declarations into its own sibling file in the same module or package, so no one file has to be read whole to change one of them.
  • FileTooLong: REDACTED REDACTED — FileTooLong — 903 significant lines (blank, comment-only and punctuation-only lines excluded). The bar is 500 significant lines; this is 403 over it, 1.81× the bar. To reduce it, split the file along the responsibilities already in it: move each cohesive group of declarations into its own sibling file in the same module or package, so no one file has to be read whole to change one of them.
  • FileTooLong: REDACTED REDACTED — FileTooLong — 815 significant lines (blank, comment-only and punctuation-only lines excluded). The bar is 500 significant lines; this is 315 over it, 1.63× the bar. To reduce it, split the file along the responsibilities already in it: move each cohesive group of declarations into its own sibling file in the same module or package, so no one file has to be read whole to change one of them.
  • FileTooLong: extract/ExtractDataJob.scala src/main/scala/ai/starlake/extract/ExtractDataJob.scala — FileTooLong — 742 significant lines (blank, comment-only and punctuation-only lines excluded). The bar is 500 significant lines; this is 242 over it, 1.48× the bar. To reduce it, split the file along the responsibilities already in it: move each cohesive group of declarations into its own sibling file in the same module or package, so no one file has to be read whole to change one of them.
  • FileTooLong: model/SchemaInfo.scala src/main/scala/ai/starlake/schema/model/SchemaInfo.scala — FileTooLong — 694 significant lines (blank, comment-only and punctuation-only lines excluded). The bar is 500 significant lines; this is 194 over it, 1.39× the bar. To reduce it, split the file along the responsibilities already in it: move each cohesive group of declarations into its own sibling file in the same module or package, so no one file has to be read whole to change one of them.
  • FileTooLong: transform/BigQueryAutoTask.scala src/main/scala/ai/starlake/job/transform/BigQueryAutoTask.scala — FileTooLong — 681 significant lines (blank, comment-only and punctuation-only lines excluded). The bar is 500 significant lines; this is 181 over it, 1.36× the bar. To reduce it, split the file along the responsibilities already in it: move each cohesive group of declarations into its own sibling file in the same module or package, so no one file has to be read whole to change one of them.
  • FileTooLong: REDACTED REDACTED — FileTooLong — 671 significant lines (blank, comment-only and punctuation-only lines excluded), about 94% of them inside a single declaration: SparkAutoTask (57-1018). The bar is 500 significant lines; this is 171 over it, 1.34× the bar. Moving the declarations that sit BESIDE it into sibling files will not shorten this file. Extract from INSIDE that declaration instead: lift each cohesive group of its body — the parts that share the same inputs and are named together — into its own unit in a sibling file, and have the original call them.
  • FileTooLong: generator/XlsDomainReader.scala src/main/scala/ai/starlake/schema/generator/XlsDomainReader.scala — FileTooLong — 565 significant lines (blank, comment-only and punctuation-only lines excluded), about 98% of them inside a single declaration: XlsDomainReader (25-696). The bar is 500 significant lines; this is 65 over it, 1.13× the bar. Moving the declarations that sit BESIDE it into sibling files will not shorten this file. Extract from INSIDE that declaration instead: lift each cohesive group of its body — the parts that share the same inputs and are named together — into its own unit in a sibling file, and have the original call them.
  • FileTooLong: lineage/AclDependencies.scala src/main/scala/ai/starlake/lineage/AclDependencies.scala — FileTooLong — 562 significant lines (blank, comment-only and punctuation-only lines excluded). The bar is 500 significant lines; this is 62 over it, 1.12× the bar. To reduce it, split the file along the responsibilities already in it: move each cohesive group of declarations into its own sibling file in the same module or package, so no one file has to be read whole to change one of them.
  • FileTooLong: generator/DagGenerateJob.scala src/main/scala/ai/starlake/schema/generator/DagGenerateJob.scala — FileTooLong — 552 significant lines (blank, comment-only and punctuation-only lines excluded). The bar is 500 significant lines; this is 52 over it, 1.10× the bar. To reduce it, split the file along the responsibilities already in it: move each cohesive group of declarations into its own sibling file in the same module or package, so no one file has to be read whole to change one of them.
  • FileTooLong: REDACTED REDACTED — FileTooLong — 550 significant lines (blank, comment-only and punctuation-only lines excluded). The bar is 500 significant lines; this is 50 over it, 1.10× the bar. To reduce it, split the file along the responsibilities already in it: move each cohesive group of declarations into its own sibling file in the same module or package, so no one file has to be read whole to change one of them.
  • FileTooLong: openapi/OpenAPISchemaExtractor.scala src/main/scala/ai/starlake/extract/impl/openapi/OpenAPISchemaExtractor.scala — FileTooLong — 548 significant lines (blank, comment-only and punctuation-only lines excluded). The bar is 500 significant lines; this is 48 over it, 1.10× the bar. To reduce it, split the file along the responsibilities already in it: move each cohesive group of declarations into its own sibling file in the same module or package, so no one file has to be read whole to change one of them.
  • FileTooLong: workflow/IngestionWorkflow.scala src/main/scala/ai/starlake/workflow/IngestionWorkflow.scala — FileTooLong — 510 significant lines (blank, comment-only and punctuation-only lines excluded). The bar is 500 significant lines; this is 10 over it, 1.02× the bar. To reduce it, split the file along the responsibilities already in it: move each cohesive group of declarations into its own sibling file in the same module or package, so no one file has to be read whole to change one of them.
D35 · Change Coupling · Change coupling · ×11
  • Change coupling: BigQueryTableInfo.scala ↔ REDACTED src/main/scala/ai/starlake/extract/BigQueryTableInfo.scala:1 — `src/main/scala/ai/starlake/extract/BigQueryTableInfo.scala` and `REDACTED` change together 89% of the time (16 of the 18 commits that touched whichever of the two files changed less often, counting a file under its earlier names as well, and counted over this repository's 10,000 most recent commits rather than its whole history — a repo-wide or module-wide sweep is evidence about the sweep rather than about any pair inside it and is left out of BOTH sides of this ratio, while a dependency bump, a formatter/rename sweep, or a commit whose edit to one of the two files was a tool directive such as //go:generate or whitespace only is left out of the shared count ONLY, so the two sides are not taken over identical commit sets) with no explicit dependency — the edge is real but nothing declares it. Read the pair before acting: if one registers itself into the other through a hook or an initialiser, the missing dependency is DELIBERATE — the registration is the link, and it is meant not to be an import — and the thing to add is a comment on each side naming the other, not a merge; if they simply belong together, co-locate them; if neither holds, the coupling is hidden and worth breaking. You can check this without leaving the row: of the 16 shared commits counted here, the most recent 3 are `c78e1fbe` Custom datasets parameter when creating settings; `3cbc917b` Custom datasets parameter when creating settings; `79eb4a8f` agressive settings caching — run `git show` on any of them.
  • Change coupling: BigQueryAutoTask.scala ↔ StarlakeJdbcOps.scala src/main/scala/ai/starlake/job/transform/BigQueryAutoTask.scala:1 — `src/main/scala/ai/starlake/job/transform/BigQueryAutoTask.scala` and `src/main/scala/ai/starlake/utils/StarlakeJdbcOps.scala` change together 80% of the time (8 of the 10 commits that touched whichever of the two files changed less often, counting a file under its earlier names as well, and counted over this repository's 10,000 most recent commits rather than its whole history — a repo-wide or module-wide sweep is evidence about the sweep rather than about any pair inside it and is left out of BOTH sides of this ratio, while a dependency bump, a formatter/rename sweep, or a commit whose edit to one of the two files was a tool directive such as //go:generate or whitespace only is left out of the shared count ONLY, so the two sides are not taken over identical commit sets) with no explicit dependency — the edge is real but nothing declares it. Read the pair before acting: if one registers itself into the other through a hook or an initialiser, the missing dependency is DELIBERATE — the registration is the link, and it is meant not to be an import — and the thing to add is a comment on each side naming the other, not a merge; if they simply belong together, co-locate them; if neither holds, the coupling is hidden and worth breaking. You can check this without leaving the row: of the 8 shared commits counted here, the most recent 3 are `00457219` Add BigQuery native branching support via SL_DATA_BRANCH; `9309e687` Add BigQuery native branching support via SL_DATA_BRANCH; `b8d51dfd` Improve code quality: security, resource management, type safety, and… — run `git show` on any of them.
  • Change coupling: JdbcConnectionLoadCmd.scala ↔ JdbcAutoTask.scala src/main/scala/ai/starlake/job/sink/jdbc/JdbcConnectionLoadCmd.scala:1 — `src/main/scala/ai/starlake/job/sink/jdbc/JdbcConnectionLoadCmd.scala` and `src/main/scala/ai/starlake/job/transform/JdbcAutoTask.scala` change together 71% of the time (10 of the 14 commits that touched whichever of the two files changed less often, counting a file under its earlier names as well, and counted over this repository's 10,000 most recent commits rather than its whole history — a repo-wide or module-wide sweep is evidence about the sweep rather than about any pair inside it and is left out of BOTH sides of this ratio, while a dependency bump, a formatter/rename sweep, or a commit whose edit to one of the two files was a tool directive such as //go:generate or whitespace only is left out of the shared count ONLY, so the two sides are not taken over identical commit sets) with no explicit dependency — the edge is real but nothing declares it. Read the pair before acting: if one registers itself into the other through a hook or an initialiser, the missing dependency is DELIBERATE — the registration is the link, and it is meant not to be an import — and the thing to add is a comment on each side naming the other, not a merge; if they simply belong together, co-locate them; if neither holds, the coupling is hidden and worth breaking. You can check this without leaving the row: of the 10 shared commits counted here, the most recent 3 are `3e814b89` rename Connection to ConnectionInfo; `8a312ae5` rename Connection to ConnectionInfo; `1b9f9b52` Improve validity checking — run `git show` on any of them.
  • Change coupling: REDACTED ↔ StarlakeJdbcOps.scala REDACTED:1 — `REDACTED` and `src/main/scala/ai/starlake/utils/StarlakeJdbcOps.scala` change together 60% of the time (6 of the 10 commits that touched whichever of the two files changed less often, counting a file under its earlier names as well, and counted over this repository's 10,000 most recent commits rather than its whole history — a repo-wide or module-wide sweep is evidence about the sweep rather than about any pair inside it and is left out of BOTH sides of this ratio, while a dependency bump, a formatter/rename sweep, or a commit whose edit to one of the two files was a tool directive such as //go:generate or whitespace only is left out of the shared count ONLY, so the two sides are not taken over identical commit sets) with no explicit dependency — the edge is real but nothing declares it. Read the pair before acting: if one registers itself into the other through a hook or an initialiser, the missing dependency is DELIBERATE — the registration is the link, and it is meant not to be an import — and the thing to add is a comment on each side naming the other, not a merge; if they simply belong together, co-locate them; if neither holds, the coupling is hidden and worth breaking. You can check this without leaving the row: of the 6 shared commits counted here, the most recent 3 are `b8d51dfd` Improve code quality: security, resource management, type safety, and…; `01244bc9` Improve code quality: security, resource management, type safety, and…; `7d8d4d6b` Activate Sync by default — run `git show` on any of them.
  • Change coupling: DagGenerateConfig.scala ↔ Xls2YmlConfig.scala src/main/scala/ai/starlake/schema/generator/DagGenerateConfig.scala:1 — `src/main/scala/ai/starlake/schema/generator/DagGenerateConfig.scala` and `src/main/scala/ai/starlake/schema/generator/Xls2YmlConfig.scala` change together 60% of the time (6 of the 10 commits that touched whichever of the two files changed less often, counting a file under its earlier names as well, and counted over this repository's 10,000 most recent commits rather than its whole history — a repo-wide or module-wide sweep is evidence about the sweep rather than about any pair inside it and is left out of BOTH sides of this ratio, while a dependency bump, a formatter/rename sweep, or a commit whose edit to one of the two files was a tool directive such as //go:generate or whitespace only is left out of the shared count ONLY, so the two sides are not taken over identical commit sets). They sit in the same directory, and in this ecosystem sibling files there normally share one namespace/package — so a direct reference between them needs no import and this pass cannot see whether one exists. Read the pair before acting: if one file only DECLARES what the other consumes (a constants/types file beside its user), the co-change is definitional and the question is whether the split earns its keep; if they duplicate structure, extract the common part into a shared function or type they both call; if neither holds, the coupling is hidden and worth breaking. You can check this without leaving the row: of the 6 shared commits counted here, the most recent 3 are `02fbb098` Update docs; `a6b32e9c` Update docs; `ec08d753` Dag Orchestration (at that commit the file was still `src/main/scala/ai/starlake/schema/generator/Yml2DagGenerateConfig.scala`) — run `git show` on any of them.
  • Change coupling: JdbcConnectionLoadCmd.scala ↔ REDACTED src/main/scala/ai/starlake/job/sink/jdbc/JdbcConnectionLoadCmd.scala:1 — `src/main/scala/ai/starlake/job/sink/jdbc/JdbcConnectionLoadCmd.scala` and `REDACTED` change together 57% of the time (8 of the 14 commits that touched whichever of the two files changed less often, counting a file under its earlier names as well, and counted over this repository's 10,000 most recent commits rather than its whole history — a repo-wide or module-wide sweep is evidence about the sweep rather than about any pair inside it and is left out of BOTH sides of this ratio, while a dependency bump, a formatter/rename sweep, or a commit whose edit to one of the two files was a tool directive such as //go:generate or whitespace only is left out of the shared count ONLY, so the two sides are not taken over identical commit sets) with no explicit dependency — the edge is real but nothing declares it. Read the pair before acting: if one registers itself into the other through a hook or an initialiser, the missing dependency is DELIBERATE — the registration is the link, and it is meant not to be an import — and the thing to add is a comment on each side naming the other, not a merge; if they simply belong together, co-locate them; if neither holds, the coupling is hidden and worth breaking. You can check this without leaving the row: of the 8 shared commits counted here, the most recent 3 are `3e814b89` rename Connection to ConnectionInfo; `8a312ae5` rename Connection to ConnectionInfo; `1b9f9b52` Improve validity checking — run `git show` on any of them.
  • Change coupling: BigQueryTableInfo.scala ↔ Main.scala src/main/scala/ai/starlake/extract/BigQueryTableInfo.scala:1 — `src/main/scala/ai/starlake/extract/BigQueryTableInfo.scala` and `src/main/scala/ai/starlake/job/Main.scala` change together 56% of the time (10 of the 18 commits that touched whichever of the two files changed less often, counting a file under its earlier names as well, and counted over this repository's 10,000 most recent commits rather than its whole history — a repo-wide or module-wide sweep is evidence about the sweep rather than about any pair inside it and is left out of BOTH sides of this ratio, while a dependency bump, a formatter/rename sweep, or a commit whose edit to one of the two files was a tool directive such as //go:generate or whitespace only is left out of the shared count ONLY, so the two sides are not taken over identical commit sets) with no explicit dependency — the edge is real but nothing declares it. Read the pair before acting: if one registers itself into the other through a hook or an initialiser, the missing dependency is DELIBERATE — the registration is the link, and it is meant not to be an import — and the thing to add is a comment on each side naming the other, not a merge; if they simply belong together, co-locate them; if neither holds, the coupling is hidden and worth breaking. You can check this without leaving the row: of the 10 shared commits counted here, the most recent 3 are `c78e1fbe` Custom datasets parameter when creating settings; `3cbc917b` Custom datasets parameter when creating settings; `79eb4a8f` agressive settings caching — run `git show` on any of them.
  • Change coupling: ExtractDataCmd.scala ↔ REDACTED src/main/scala/ai/starlake/extract/ExtractDataCmd.scala:1 — `src/main/scala/ai/starlake/extract/ExtractDataCmd.scala` and `REDACTED` change together 55% of the time (6 of the 11 commits that touched whichever of the two files changed less often, counting a file under its earlier names as well, and counted over this repository's 10,000 most recent commits rather than its whole history — a repo-wide or module-wide sweep is evidence about the sweep rather than about any pair inside it and is left out of BOTH sides of this ratio, while a dependency bump, a formatter/rename sweep, or a commit whose edit to one of the two files was a tool directive such as //go:generate or whitespace only is left out of the shared count ONLY, so the two sides are not taken over identical commit sets). They sit in the same directory, and in this ecosystem sibling files there normally share one namespace/package — so a direct reference between them needs no import and this pass cannot see whether one exists. Read the pair before acting: if one file only DECLARES what the other consumes (a constants/types file beside its user), the co-change is definitional and the question is whether the split earns its keep; if they duplicate structure, extract the common part into a shared function or type they both call; if neither holds, the coupling is hidden and worth breaking. You can check this without leaving the row: of the 6 shared commits counted here, the most recent 3 are `2bc4efe4` fix: full export precedence; `228b272c` fix: full export precedence; `427601ca` refactor: all codes related to JDBC — run `git show` on any of them.
  • Change coupling: BigQueryJobBase.scala ↔ SingleUserRequestHandler.scala src/main/scala/ai/starlake/job/sink/bigquery/BigQueryJobBase.scala:1 — `src/main/scala/ai/starlake/job/sink/bigquery/BigQueryJobBase.scala` and `src/main/scala/ai/starlake/serve/SingleUserRequestHandler.scala` change together 53% of the time (8 of the 15 commits that touched whichever of the two files changed less often, counting a file under its earlier names as well, and counted over this repository's 10,000 most recent commits rather than its whole history — a repo-wide or module-wide sweep is evidence about the sweep rather than about any pair inside it and is left out of BOTH sides of this ratio, while a dependency bump, a formatter/rename sweep, or a commit whose edit to one of the two files was a tool directive such as //go:generate or whitespace only is left out of the shared count ONLY, so the two sides are not taken over identical commit sets) with no explicit dependency — the edge is real but nothing declares it. Read the pair before acting: if one registers itself into the other through a hook or an initialiser, the missing dependency is DELIBERATE — the registration is the link, and it is meant not to be an import — and the thing to add is a comment on each side naming the other, not a merge; if they simply belong together, co-locate them; if neither holds, the coupling is hidden and worth breaking. You can check this without leaving the row: of the 8 shared commits counted here, the most recent 3 are `b64db745` Create duckdb database on first load; `691e357c` Create duckdb database on first load; `513f491a` use audit.database for database — run `git show` on any of them.
  • Change coupling: InferSchemaConfig.scala ↔ InferSchemaJob.scala src/main/scala/ai/starlake/job/infer/InferSchemaConfig.scala:1 — `src/main/scala/ai/starlake/job/infer/InferSchemaConfig.scala` and `src/main/scala/ai/starlake/job/infer/InferSchemaJob.scala` change together 50% of the time (10 of the 20 commits that touched whichever of the two files changed less often, counting a file under its earlier names as well, and counted over this repository's 10,000 most recent commits rather than its whole history — a repo-wide or module-wide sweep is evidence about the sweep rather than about any pair inside it and is left out of BOTH sides of this ratio, while a dependency bump, a formatter/rename sweep, or a commit whose edit to one of the two files was a tool directive such as //go:generate or whitespace only is left out of the shared count ONLY, so the two sides are not taken over identical commit sets). They sit in the same directory, and in this ecosystem sibling files there normally share one namespace/package — so a direct reference between them needs no import and this pass cannot see whether one exists. Read the pair before acting: if one file only DECLARES what the other consumes (a constants/types file beside its user), the co-change is definitional and the question is whether the split earns its keep; if they duplicate structure, extract the common part into a shared function or type they both call; if neither holds, the coupling is hidden and worth breaking. You can check this without leaving the row: of the 10 shared commits counted here, the most recent 3 are `55c18e17` infer starlake schema from json-schema; `1c2bd445` infer starlake schema from json-schema; `d7b7ab92` Handle XML Load in Snowflake as variant type — run `git show` on any of them.
  • Change coupling: Settings.scala ↔ ExtractSchemaConfig.scala src/main/scala/ai/starlake/config/Settings.scala:1 — `src/main/scala/ai/starlake/config/Settings.scala` and `src/main/scala/ai/starlake/extract/ExtractSchemaConfig.scala` change together 50% of the time (8 of the 16 commits that touched whichever of the two files changed less often, counting a file under its earlier names as well, and counted over this repository's 10,000 most recent commits rather than its whole history — a repo-wide or module-wide sweep is evidence about the sweep rather than about any pair inside it and is left out of BOTH sides of this ratio, while a dependency bump, a formatter/rename sweep, or a commit whose edit to one of the two files was a tool directive such as //go:generate or whitespace only is left out of the shared count ONLY, so the two sides are not taken over identical commit sets) with no explicit dependency — the edge is real but nothing declares it. Read the pair before acting: if one registers itself into the other through a hook or an initialiser, the missing dependency is DELIBERATE — the registration is the link, and it is meant not to be an import — and the thing to add is a comment on each side naming the other, not a merge; if they simply belong together, co-locate them; if neither holds, the coupling is hidden and worth breaking. You can check this without leaving the row: of the 8 shared commits counted here, the most recent 3 are `3d8ca518` Improve schema extraction on sink; `67342395` Improve schema extraction on sink; `7ed8b524` Extract JDBC / BigQuery BD to external — run `git show` on any of them.
D35 · Change Coupling · Change-coupling hub · ×9
  • Change-coupling hub: IngestionJob.scala → MetricsJob.scala, BigQuerySparkWriter.scala, JdbcConnectionLoadConfig.scala src/main/scala/ai/starlake/job/ingest/IngestionJob.scala — `src/main/scala/ai/starlake/job/ingest/IngestionJob.scala` changes together with 3 other files — `src/main/scala/ai/starlake/job/metrics/MetricsJob.scala`, `src/main/scala/ai/starlake/job/sink/bigquery/BigQuerySparkWriter.scala`, `src/main/scala/ai/starlake/job/sink/jdbc/JdbcConnectionLoadConfig.scala` — none of which declares a dependency on it: one file is the hub of 3 separate couplings, not 3 unrelated pairs. Read the hub first: if the others each duplicate a part of what it does, the shared concern belongs in ONE unit and extracting it clears every edge at once; if the hub is a registry, dispatcher or barrel that must name each of them, the coupling is structural and the question is whether that list can be discovered instead of enumerated. Fixing the hub is one change; breaking the couplings one pair at a time is 3.
  • Change-coupling hub: Yml2XlsIamPolicyTags.scala → BigQueryTableInfo.scala, REDACTED, Main.scala, Xls2Yml.scala, Xls2YmlAutoJob.scala, SettingsManager.scala src/main/scala/ai/starlake/schema/generator/Yml2XlsIamPolicyTags.scala — `src/main/scala/ai/starlake/schema/generator/Yml2XlsIamPolicyTags.scala` changes together with 6 other files — `src/main/scala/ai/starlake/extract/BigQueryTableInfo.scala`, `REDACTED`, `src/main/scala/ai/starlake/job/Main.scala`, `src/main/scala/ai/starlake/schema/generator/Xls2Yml.scala`, `src/main/scala/ai/starlake/schema/generator/Xls2YmlAutoJob.scala`, `src/main/scala/ai/starlake/serve/SettingsManager.scala` — none of which declares a dependency on it: one file is the hub of 6 separate couplings, not 6 unrelated pairs. Read the hub first: if the others each duplicate a part of what it does, the shared concern belongs in ONE unit and extracting it clears every edge at once; if the hub is a registry, dispatcher or barrel that must name each of them, the coupling is structural and the question is whether that list can be discovered instead of enumerated. Fixing the hub is one change; breaking the couplings one pair at a time is 6.
  • Change-coupling hub: Parquet2CSV.scala → BigQueryTableInfo.scala, REDACTED, Main.scala, IngestionJob.scala, Xls2Yml.scala, Xls2YmlAutoJob.scala, Yml2XlsIamPolicyTags.scala src/main/scala/ai/starlake/job/convert/Parquet2CSV.scala — `src/main/scala/ai/starlake/job/convert/Parquet2CSV.scala` changes together with 7 other files — `src/main/scala/ai/starlake/extract/BigQueryTableInfo.scala`, `REDACTED`, `src/main/scala/ai/starlake/job/Main.scala`, `src/main/scala/ai/starlake/job/ingest/IngestionJob.scala`, `src/main/scala/ai/starlake/schema/generator/Xls2Yml.scala`, `src/main/scala/ai/starlake/schema/generator/Xls2YmlAutoJob.scala`, `src/main/scala/ai/starlake/schema/generator/Yml2XlsIamPolicyTags.scala` — none of which declares a dependency on it: one file is the hub of 7 separate couplings, not 7 unrelated pairs. Read the hub first: if the others each duplicate a part of what it does, the shared concern belongs in ONE unit and extracting it clears every edge at once; if the hub is a registry, dispatcher or barrel that must name each of them, the coupling is structural and the question is whether that list can be discovered instead of enumerated. Fixing the hub is one change; breaking the couplings one pair at a time is 7.
  • Change-coupling hub: TableInfo.scala → BigQueryLoadConfig.scala, XlsAutoJobReader.scala, AutoTaskInfo.scala src/main/scala/ai/starlake/schema/model/TableInfo.scala — `src/main/scala/ai/starlake/schema/model/TableInfo.scala` changes together with 3 other files — `src/main/scala/ai/starlake/job/sink/bigquery/BigQueryLoadConfig.scala`, `src/main/scala/ai/starlake/schema/generator/XlsAutoJobReader.scala`, `src/main/scala/ai/starlake/schema/model/AutoTaskInfo.scala` — none of which declares a dependency on it: one file is the hub of 3 separate couplings, not 3 unrelated pairs. Read the hub first: if the others each duplicate a part of what it does, the shared concern belongs in ONE unit and extracting it clears every edge at once; if the hub is a registry, dispatcher or barrel that must name each of them, the coupling is structural and the question is whether that list can be discovered instead of enumerated. Fixing the hub is one change; breaking the couplings one pair at a time is 3.
  • Change-coupling hub: IngestionWorkflow.scala → InferSchemaConfig.scala, LoadCmd.scala, BigQuerySparkWriter.scala, TransformConfig.scala src/main/scala/ai/starlake/workflow/IngestionWorkflow.scala — `src/main/scala/ai/starlake/workflow/IngestionWorkflow.scala` changes together with 4 other files — `src/main/scala/ai/starlake/job/infer/InferSchemaConfig.scala`, `src/main/scala/ai/starlake/job/ingest/LoadCmd.scala`, `src/main/scala/ai/starlake/job/sink/bigquery/BigQuerySparkWriter.scala`, `src/main/scala/ai/starlake/job/transform/TransformConfig.scala` — none of which declares a dependency on it: one file is the hub of 4 separate couplings, not 4 unrelated pairs. Read the hub first: if the others each duplicate a part of what it does, the shared concern belongs in ONE unit and extracting it clears every edge at once; if the hub is a registry, dispatcher or barrel that must name each of them, the coupling is structural and the question is whether that list can be discovered instead of enumerated. Fixing the hub is one change; breaking the couplings one pair at a time is 4.
  • Change-coupling hub: BootstrapCmd.scala → InferSchemaCmd.scala, LoadCmd.scala, TransformCmd.scala src/main/scala/ai/starlake/job/bootstrap/BootstrapCmd.scala — `src/main/scala/ai/starlake/job/bootstrap/BootstrapCmd.scala` changes together with 3 other files — `src/main/scala/ai/starlake/job/infer/InferSchemaCmd.scala`, `src/main/scala/ai/starlake/job/ingest/LoadCmd.scala`, `src/main/scala/ai/starlake/job/transform/TransformCmd.scala` — none of which declares a dependency on it: one file is the hub of 3 separate couplings, not 3 unrelated pairs. Read the hub first: if the others each duplicate a part of what it does, the shared concern belongs in ONE unit and extracting it clears every edge at once; if the hub is a registry, dispatcher or barrel that must name each of them, the coupling is structural and the question is whether that list can be discovered instead of enumerated. Fixing the hub is one change; breaking the couplings one pair at a time is 3.
  • Change-coupling hub: AuditLog.scala → MetricsJob.scala, BigQuerySparkWriter.scala, JdbcConnectionLoadConfig.scala src/main/scala/ai/starlake/job/ingest/AuditLog.scala — `src/main/scala/ai/starlake/job/ingest/AuditLog.scala` changes together with 3 other files — `src/main/scala/ai/starlake/job/metrics/MetricsJob.scala`, `src/main/scala/ai/starlake/job/sink/bigquery/BigQuerySparkWriter.scala`, `src/main/scala/ai/starlake/job/sink/jdbc/JdbcConnectionLoadConfig.scala` — none of which declares a dependency on it: one file is the hub of 3 separate couplings, not 3 unrelated pairs. Read the hub first: if the others each duplicate a part of what it does, the shared concern belongs in ONE unit and extracting it clears every edge at once; if the hub is a registry, dispatcher or barrel that must name each of them, the coupling is structural and the question is whether that list can be discovered instead of enumerated. Fixing the hub is one change; breaking the couplings one pair at a time is 3.
  • Change-coupling hub: Yml2Xls.scala → Main.scala, Xls2Yml.scala, Xls2YmlAutoJob.scala src/main/scala/ai/starlake/schema/generator/Yml2Xls.scala — `src/main/scala/ai/starlake/schema/generator/Yml2Xls.scala` changes together with 3 other files — `src/main/scala/ai/starlake/job/Main.scala`, `src/main/scala/ai/starlake/schema/generator/Xls2Yml.scala`, `src/main/scala/ai/starlake/schema/generator/Xls2YmlAutoJob.scala` — none of which declares a dependency on it: one file is the hub of 3 separate couplings, not 3 unrelated pairs. Read the hub first: if the others each duplicate a part of what it does, the shared concern belongs in ONE unit and extracting it clears every edge at once; if the hub is a registry, dispatcher or barrel that must name each of them, the coupling is structural and the question is whether that list can be discovered instead of enumerated. Fixing the hub is one change; breaking the couplings one pair at a time is 3.
  • Change-coupling hub: DataFrameTransform.scala → BigQueryTableInfo.scala, REDACTED, Main.scala, Parquet2CSV.scala, Xls2Yml.scala, Xls2YmlAutoJob.scala, Yml2Xls.scala, Yml2XlsIamPolicyTags.scala, SettingsManager.scala src/main/scala/ai/starlake/job/sink/DataFrameTransform.scala — `src/main/scala/ai/starlake/job/sink/DataFrameTransform.scala` changes together with 9 other files — `src/main/scala/ai/starlake/extract/BigQueryTableInfo.scala`, `REDACTED`, `src/main/scala/ai/starlake/job/Main.scala`, `src/main/scala/ai/starlake/job/convert/Parquet2CSV.scala`, `src/main/scala/ai/starlake/schema/generator/Xls2Yml.scala`, `src/main/scala/ai/starlake/schema/generator/Xls2YmlAutoJob.scala`, `src/main/scala/ai/starlake/schema/generator/Yml2Xls.scala`, `src/main/scala/ai/starlake/schema/generator/Yml2XlsIamPolicyTags.scala`, `src/main/scala/ai/starlake/serve/SettingsManager.scala` — none of which declares a dependency on it: one file is the hub of 9 separate couplings, not 9 unrelated pairs. Read the hub first: if the others each duplicate a part of what it does, the shared concern belongs in ONE unit and extracting it clears every edge at once; if the hub is a registry, dispatcher or barrel that must name each of them, the coupling is structural and the question is whether that list can be discovered instead of enumerated. Fixing the hub is one change; breaking the couplings one pair at a time is 9.
D3 · God Classes · TooManyMethods · ×8
  • TooManyMethods: SchemaHandler src/main/scala/ai/starlake/schema/handlers/SchemaHandler.scala:103 — TooManyMethods — 114 methods. The bar is 30 methods; this is 84 over it, 3.80× the bar. To reduce it, group the members that share the same data into a smaller type of their own and delegate to it, so no single type carries every responsibility.
  • TooManyMethods: DatasetArea src/main/scala/ai/starlake/config/DatasetArea.scala:33 — TooManyMethods — 51 methods. The bar is 30 methods; this is 21 over it, 1.70× the bar. To reduce it, group the members that share the same data into a smaller type of their own and delegate to it, so no single type carries every responsibility.
  • TooManyMethods: Setup REDACTED:111 — TooManyMethods — 46 methods. The bar is 30 methods; this is 16 over it, 1.53× the bar. To reduce it, group the members that share the same data into a smaller type of their own and delegate to it, so no single type carries every responsibility.
  • TooManyMethods: Utils src/main/scala/ai/starlake/utils/Utils.scala:45 — TooManyMethods — 42 methods. The bar is 30 methods; this is 12 over it, 1.40× the bar. To reduce it, group the members that share the same data into a smaller type of their own and delegate to it, so no single type carries every responsibility.
  • TooManyMethods: SQLUtils src/main/scala/ai/starlake/sql/SQLUtils.scala:19 — TooManyMethods — 34 methods. The bar is 30 methods; this is 4 over it, 1.13× the bar. To reduce it, group the members that share the same data into a smaller type of their own and delegate to it, so no single type carries every responsibility.
  • TooManyMethods: JdbcDbUtils REDACTED:24 — TooManyMethods — 33 methods. The bar is 30 methods; this is 3 over it, 1.10× the bar. To reduce it, group the members that share the same data into a smaller type of their own and delegate to it, so no single type carries every responsibility.
  • TooManyMethods: AutoTask REDACTED:63 — TooManyMethods — 33 methods. The bar is 30 methods; this is 3 over it, 1.10× the bar. To reduce it, group the members that share the same data into a smaller type of their own and delegate to it, so no single type carries every responsibility.
  • TooManyMethods: HdfsStorageHandler src/main/scala/ai/starlake/schema/handlers/HdfsStorageHandler.scala:41 — TooManyMethods — 33 methods. The bar is 30 methods; this is 3 over it, 1.10× the bar. To reduce it, group the members that share the same data into a smaller type of their own and delegate to it, so no single type carries every responsibility.
D15 · Churn × Complexity Hotspots · Hotspot · ×6
  • Hotspot: REDACTED REDACTED:609 — REDACTED changed 23 times in last 90 days, and the most complex body those changes touched has cyclomatic complexity 23 in DuckDbNativeLoader.singleStepLoad at line 609. 14 of those changes were fix/bug commits, so the churn is repair rather than feature work. Before the next change lands here, make sure the area it touches is under test, then split that area out of the file so the following change is smaller than this one — a file this often edited pays the complexity back every time. Counted over 2026-07-22..2026-10-20, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-22 16:06:12 +02:00' --until='2026-10-20 16:06:12 +02:00' --full-history --no-merges -- REDACTED`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
  • Hotspot: REDACTED REDACTED:637 — REDACTED changed 19 times in last 90 days, and the most complex body those changes touched has cyclomatic complexity 19 in Setup.generateVersions at line 637. 6 of those changes were fix/bug commits, and the other 13 changed it for other reasons — this file is under both repair and feature pressure. Before the next change lands here, make sure the area it touches is under test, then split that area out of the file so the following change is smaller than this one — a file this often edited pays the complexity back every time. Counted over 2026-07-22..2026-10-20, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-22 16:06:12 +02:00' --until='2026-10-20 16:06:12 +02:00' --full-history --no-merges -- REDACTED`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
  • Hotspot: src/main/scala/ai/starlake/job/transform/BigQueryAutoTask.scala src/main/scala/ai/starlake/job/transform/BigQueryAutoTask.scala:321 — src/main/scala/ai/starlake/job/transform/BigQueryAutoTask.scala changed 4 times in last 90 days, and the most complex body those changes touched has cyclomatic complexity 41 in BigQueryAutoTask.runBQ at line 321. 3 of those changes were fix/bug commits, so the churn is repair rather than feature work. Before the next change lands here, make sure the area it touches is under test, then split that area out of the file so the following change is smaller than this one — a file this often edited pays the complexity back every time. Counted over 2026-07-22..2026-10-20, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-22 16:06:12 +02:00' --until='2026-10-20 16:06:12 +02:00' --full-history --no-merges -- src/main/scala/ai/starlake/job/transform/BigQueryAutoTask.scala`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
  • Hotspot: src/main/scala/ai/starlake/extract/StarlakeConnectionPool.scala src/main/scala/ai/starlake/extract/StarlakeConnectionPool.scala:142 — src/main/scala/ai/starlake/extract/StarlakeConnectionPool.scala changed 5 times in last 90 days, and the most complex body those changes touched has cyclomatic complexity 27 in StarlakeConnectionPool.getConnection at line 142. 2 of those changes were fix/bug commits, and the other 3 changed it for other reasons — this file is under both repair and feature pressure. Before the next change lands here, make sure the area it touches is under test, then split that area out of the file so the following change is smaller than this one — a file this often edited pays the complexity back every time. Counted over 2026-07-22..2026-10-20, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-22 16:06:12 +02:00' --until='2026-10-20 16:06:12 +02:00' --full-history --no-merges -- src/main/scala/ai/starlake/extract/StarlakeConnectionPool.scala`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
  • Hotspot: src/main/scala/ai/starlake/job/transform/JdbcAutoTask.scala src/main/scala/ai/starlake/job/transform/JdbcAutoTask.scala:204 — src/main/scala/ai/starlake/job/transform/JdbcAutoTask.scala changed 4 times in last 90 days, and the most complex body those changes touched has cyclomatic complexity 31 in JdbcAutoTask.runJDBC at line 204. 1 of those changes was a fix/bug commit, and the other 3 changed it for other reasons — this file is under both repair and feature pressure. Before the next change lands here, make sure the area it touches is under test, then split that area out of the file so the following change is smaller than this one — a file this often edited pays the complexity back every time. Counted over 2026-07-22..2026-10-20, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-22 16:06:12 +02:00' --until='2026-10-20 16:06:12 +02:00' --full-history --no-merges -- src/main/scala/ai/starlake/job/transform/JdbcAutoTask.scala`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
  • Hotspot: src/main/scala/ai/starlake/job/Main.scala src/main/scala/ai/starlake/job/Main.scala:208 — src/main/scala/ai/starlake/job/Main.scala changed 7 times in last 90 days, and the most complex body those changes touched has cyclomatic complexity 15 in Main.runWithExitCode at line 208. 2 of those changes were fix/bug commits, and the other 5 changed it for other reasons — this file is under both repair and feature pressure. Before the next change lands here, make sure the area it touches is under test, then split that area out of the file so the following change is smaller than this one — a file this often edited pays the complexity back every time. Counted over 2026-07-22..2026-10-20, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-22 16:06:12 +02:00' --until='2026-10-20 16:06:12 +02:00' --full-history --no-merges -- src/main/scala/ai/starlake/job/Main.scala`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
D36 · Supply-chain Provenance & Signing REDACTED · ×6
  • REDACTED
  • REDACTED
  • REDACTED
  • REDACTED
  • REDACTED
  • REDACTED
D4 · Code Duplication · Duplicated block (10 lines × 2) · ×6
  • Duplicated block (10 lines × 2) REDACTED:555 — REDACTED:555-564 | REDACTED:568-577 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Read the line range as the matched WINDOW rather than a finished unit: at `REDACTED:555` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
  • Duplicated block (10 lines × 2) REDACTED:547 — REDACTED:547-556 | src/main/scala/ai/starlake/job/ingest/loaders/NativeLoader.scala:197-206 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from both call sites, so a change lands once. Read the line range as the matched WINDOW rather than a finished unit: at `REDACTED:547` it runs out through the closing brace of the declaration holding it — the window is that declaration's tail, not a fragment that begins part-way through something, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
  • Duplicated block (10 lines × 2) src/main/scala/ai/starlake/lineage/AclDependencies.scala:155 — src/main/scala/ai/starlake/lineage/AclDependencies.scala:155-164 | src/main/scala/ai/starlake/lineage/AclDependencies.scala:185-194 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Read the line range as the matched WINDOW rather than a finished unit: at `src/main/scala/ai/starlake/lineage/AclDependencies.scala:155` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
  • Duplicated block (10 lines × 2) src/main/scala/ai/starlake/lineage/AutoTaskDependencies.scala:172 — src/main/scala/ai/starlake/lineage/AutoTaskDependencies.scala:172-181 | src/main/scala/ai/starlake/lineage/AutoTaskDependencies.scala:253-262 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
  • Duplicated block (10 lines × 2) src/main/scala/ai/starlake/serve/SingleUserRequestHandler.scala:46 — src/main/scala/ai/starlake/serve/SingleUserRequestHandler.scala:46-55 | src/main/scala/ai/starlake/serve/SingleUserRequestHandler.scala:94-103 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Read the line range as the matched WINDOW rather than a finished unit: at `src/main/scala/ai/starlake/serve/SingleUserRequestHandler.scala:46` it runs out through the closing brace of the declaration holding it — the window is that declaration's tail, not a fragment that begins part-way through something, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
  • Duplicated block (10 lines × 2) src/main/scala/ai/starlake/utils/ProcessRunner.scala:106 — src/main/scala/ai/starlake/utils/ProcessRunner.scala:106-115 | src/main/scala/ai/starlake/utils/ProcessRunner.scala:124-133 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Read the line range as the matched WINDOW rather than a finished unit: at `src/main/scala/ai/starlake/utils/ProcessRunner.scala:106` it runs out through the closing brace of the declaration holding it — the window is that declaration's tail, not a fragment that begins part-way through something, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
D15 · Churn × Complexity Hotspots · Repeated repair · ×5
  • Repeated repair: src/main/scala/ai/starlake/job/ingest/AuditTaskBuilder.scala src/main/scala/ai/starlake/job/ingest/AuditTaskBuilder.scala:95 — src/main/scala/ai/starlake/job/ingest/AuditTaskBuilder.scala changed 6 times in last 90 days and 5 of those changes were fix/bug commits, so repair is the majority of this file's churn. Its max cyclomatic complexity is 2 (its worst body is AuditTaskBuilder.buildTask at line 95), UNDER the 15 threshold, so this is deliberately not filed as a churn × complexity hotspot — the difficulty here is in the behaviour the file has to get right, not in its control flow, and refactoring it for complexity would be the wrong move. The repairs counted were: “fix: escape SQL string literals consistently in audit and expectations sinks (#1741)”; “fix: harden the DuckDB native reject capture and the audit escaping”; “fix: make the audit literal escaping a fixpoint”; “fix: strip every jinja delimiter from the audit literals”. Each one is a case this code did not handle. Before the next change lands here, check that every one of them is pinned by a test that fails without its fix; where the same area keeps coming back, the durable fix is usually at the interface that keeps being misused rather than at the line that was last corrected. Counted over 2026-07-22..2026-10-20, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-22 16:06:12 +02:00' --until='2026-10-20 16:06:12 +02:00' --full-history --no-merges -- src/main/scala/ai/starlake/job/ingest/AuditTaskBuilder.scala`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
  • Repeated repair: src/main/scala/ai/starlake/job/ingest/AuditLog.scala src/main/scala/ai/starlake/job/ingest/AuditLog.scala:242 — src/main/scala/ai/starlake/job/ingest/AuditLog.scala changed 5 times in last 90 days and 4 of those changes were fix/bug commits, so repair is the majority of this file's churn. Its max cyclomatic complexity is 4 (its worst body is AuditLog.createTask at line 242), UNDER the 15 threshold, so this is deliberately not filed as a churn × complexity hotspot — the difficulty here is in the behaviour the file has to get right, not in its control flow, and refactoring it for complexity would be the wrong move. The repairs counted were: “fix: escape SQL string literals consistently in audit and expectations sinks (#1741)”; “fix: make the audit literal escaping a fixpoint”; “fix: fail the DuckDB native load when its second step task fails”; “fix: give the native rejected sink the audit sink contract it was missing”. Each one is a case this code did not handle. Before the next change lands here, check that every one of them is pinned by a test that fails without its fix; where the same area keeps coming back, the durable fix is usually at the interface that keeps being misused rather than at the line that was last corrected. Counted over 2026-07-22..2026-10-20, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-22 16:06:12 +02:00' --until='2026-10-20 16:06:12 +02:00' --full-history --no-merges -- src/main/scala/ai/starlake/job/ingest/AuditLog.scala`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
  • Repeated repair: src/main/scala/ai/starlake/serve/catalog/CatalogScrubber.scala src/main/scala/ai/starlake/serve/catalog/CatalogScrubber.scala:27 — src/main/scala/ai/starlake/serve/catalog/CatalogScrubber.scala changed 5 times in last 90 days and 3 of those changes were fix/bug commits, so repair is the majority of this file's churn. Its max cyclomatic complexity is 3 (its worst body is CatalogScrubber.maskOptions at line 27), UNDER the 15 threshold, so this is deliberately not filed as a churn × complexity hotspot — the difficulty here is in the behaviour the file has to get right, not in its control flow, and refactoring it for complexity would be the wrong move. The repairs counted were: “fix(serve): mask more credential option names and URL userinfo”; “fix(serve): scrub every connection's credentials in unexpected errors”; “fix(serve): scrub unexpected catalog errors”. Each one is a case this code did not handle. Before the next change lands here, check that every one of them is pinned by a test that fails without its fix; where the same area keeps coming back, the durable fix is usually at the interface that keeps being misused rather than at the line that was last corrected. Counted over 2026-07-22..2026-10-20, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-22 16:06:12 +02:00' --until='2026-10-20 16:06:12 +02:00' --full-history --no-merges -- src/main/scala/ai/starlake/serve/catalog/CatalogScrubber.scala`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
  • Repeated repair: src/main/scala/ai/starlake/job/ingest/loaders/ReplayFileWriter.scala src/main/scala/ai/starlake/job/ingest/loaders/ReplayFileWriter.scala:77 — src/main/scala/ai/starlake/job/ingest/loaders/ReplayFileWriter.scala changed 5 times in last 90 days and 3 of those changes were fix/bug commits, so repair is the majority of this file's churn. Its max cyclomatic complexity is 2 (its worst body is ReplayFileWriter.write at line 77), UNDER the 15 threshold, so this is deliberately not filed as a churn × complexity hotspot — the difficulty here is in the behaviour the file has to get right, not in its control flow, and refactoring it for complexity would be the wrong move. The repairs counted were: “fix: make the Spark replay file collision proof and a real file again (#1742)”; “fix: discriminate reject artifacts by input file, not only jobid”; “fix: make the native replay file collision proof and clean it up on a failed second step”. Each one is a case this code did not handle. Before the next change lands here, check that every one of them is pinned by a test that fails without its fix; where the same area keeps coming back, the durable fix is usually at the interface that keeps being misused rather than at the line that was last corrected. Counted over 2026-07-22..2026-10-20, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-22 16:06:12 +02:00' --until='2026-10-20 16:06:12 +02:00' --full-history --no-merges -- src/main/scala/ai/starlake/job/ingest/loaders/ReplayFileWriter.scala`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
  • Repeated repair: REDACTED REDACTED:165 — REDACTED changed 4 times in last 90 days and 3 of those changes were fix/bug commits, so repair is the majority of this file's churn. Its max cyclomatic complexity is 11 (its worst body is BigQueryCatalogBackend.jsonValue at line 165), UNDER the 15 threshold, so this is deliberately not filed as a churn × complexity hotspot — the difficulty here is in the behaviour the file has to get right, not in its control flow, and refactoring it for complexity would be the wrong move. The repairs counted were: “fix(serve): send integers beyond 2^53 as strings”; “fix(serve): cap bytes billed for BigQuery view preview”; “fix(serve): typed BigQuery preview values”. Each one is a case this code did not handle. Before the next change lands here, check that every one of them is pinned by a test that fails without its fix; where the same area keeps coming back, the durable fix is usually at the interface that keeps being misused rather than at the line that was last corrected. Counted over 2026-07-22..2026-10-20, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-22 16:06:12 +02:00' --until='2026-10-20 16:06:12 +02:00' --full-history --no-merges -- REDACTED`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
D4 · Code Duplication · Duplicated block (8 lines × 2) · ×4
  • Duplicated block (8 lines × 2) src/main/scala/ai/starlake/job/transform/JdbcAutoTask.scala:298 — src/main/scala/ai/starlake/job/transform/JdbcAutoTask.scala:298-305 | REDACTED:923-930 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from both call sites, so a change lands once. Read the line range as the matched WINDOW rather than a finished unit: at `src/main/scala/ai/starlake/job/transform/JdbcAutoTask.scala:298` it does not close everything it opens, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that. Note that the copies do not run to the end of the range shown: their LAST lines are different code, not the same code under different names — the matched region ends inside that line. Extract the lines above it, and read the last line of each site separately.
  • Duplicated block (8 lines × 2) src/main/scala/ai/starlake/lineage/AclDependencies.scala:43 — src/main/scala/ai/starlake/lineage/AclDependencies.scala:43-50 | src/main/scala/ai/starlake/lineage/AclDependencies.scala:100-107 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. ★ These copies have DRIFTED, and that is worth reading before extracting anything: just after the matched lines, `src/main/scala/ai/starlake/lineage/AclDependencies.scala:108` calls `toLowerCase` and `src/main/scala/ai/starlake/lineage/AclDependencies.scala:52` does not — after which the two agree again for 2 more lines. One of those two behaviours is the intended one and the other is what a copy-paste left behind, so decide which BEFORE unifying them: extracting the shared part will silently settle it, and if the copy that skips the call is the wrong one, that bug is already live.
  • Duplicated block (8 lines × 2) src/main/scala/ai/starlake/lineage/AclDependencies.scala:364 — src/main/scala/ai/starlake/lineage/AclDependencies.scala:364-371 | src/main/scala/ai/starlake/lineage/AclDependencies.scala:422-429 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
  • Duplicated block (8 lines × 2) src/main/scala/ai/starlake/schema/model/DagInfo.scala:219 — src/main/scala/ai/starlake/schema/model/DagInfo.scala:219-226 | src/main/scala/ai/starlake/schema/model/DagInfo.scala:336-343 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
AC6 · Visual & motion safety · Animation without a prefers-reduced-motion guard · ×3
  • Animation without a prefers-reduced-motion guard src/main/resources/templates/compare/index.html.j2:39 — `.collapsible::before` moves (`transition: transform 0.2s`) and nothing reads prefers-reduced-motion for it — no @media (prefers-reduced-motion) rule for this element, no reset this project loads — so a motion-sensitive user cannot turn it off (WCAG 2.3.3). Declare it under @media (prefers-reduced-motion: no-preference), or neutralise it under reduce.
  • Animation without a prefers-reduced-motion guard src/main/resources/templates/site/standalone/style.css:21 — `.nav-list details>summary::before` moves (`transition: transform .2s`) and nothing reads prefers-reduced-motion for it — no @media (prefers-reduced-motion) rule for this element, no reset this project loads — so a motion-sensitive user cannot turn it off (WCAG 2.3.3). Declare it under @media (prefers-reduced-motion: no-preference), or neutralise it under reduce.
  • Animation without a prefers-reduced-motion guard src/main/resources/templates/site/standalone/style.css:81 — `.item-card` moves (`transition: box-shadow .2s,transform .2s`) and nothing reads prefers-reduced-motion for it — no @media (prefers-reduced-motion) rule for this element, no reset this project loads — so a motion-sensitive user cannot turn it off (WCAG 2.3.3). Declare it under @media (prefers-reduced-motion: no-preference), or neutralise it under reduce.
D29 · Static Analysis (SAST) · REDACTED · ×3
  • REDACTED
  • REDACTED
  • REDACTED
D3 · God Classes · ClassTooLong · ×3
  • ClassTooLong: Setup REDACTED:111 — ClassTooLong — 923 significant lines (blank, comment-only and punctuation-only lines excluded), 46 methods. The bar is 400 significant lines; this is 523 over it, 2.31× the bar. To reduce it, group the members that share the same data into a smaller type of their own and delegate to it, so no single type carries every responsibility.
  • ClassTooLong: XlsDomainReader src/main/scala/ai/starlake/schema/generator/XlsDomainReader.scala:25 — ClassTooLong — 553 significant lines (blank, comment-only and punctuation-only lines excluded), 7 methods. The bar is 400 significant lines; this is 153 over it, 1.38× the bar. To reduce it, group the members that share the same data into a smaller type of their own and delegate to it, so no single type carries every responsibility.
  • ClassTooLong: BigQueryNativeJob src/main/scala/ai/starlake/job/sink/bigquery/BigQueryNativeJob.scala:31 — ClassTooLong — 470 significant lines (blank, comment-only and punctuation-only lines excluded), 15 methods. The bar is 400 significant lines; this is 70 over it, 1.18× the bar. To reduce it, group the members that share the same data into a smaller type of their own and delegate to it, so no single type carries every responsibility.
D4 · Code Duplication · Duplicated block (15 lines × 2) · ×3
  • Duplicated block (15 lines × 2) src/main/scala/ai/starlake/lineage/AclExport.scala:39 — src/main/scala/ai/starlake/lineage/AclExport.scala:39-53 | src/main/scala/ai/starlake/lineage/AclExport.scala:70-84 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Read the line range as the matched WINDOW rather than a finished unit: at `src/main/scala/ai/starlake/lineage/AclExport.scala:39` it runs out through the closing brace of the declaration holding it — the window is that declaration's tail, not a fragment that begins part-way through something, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
  • Duplicated block (15 lines × 2) src/main/scala/ai/starlake/schema/AdaptiveWriteStrategy.scala:105 — src/main/scala/ai/starlake/schema/AdaptiveWriteStrategy.scala:105-119 | src/main/scala/ai/starlake/schema/AdaptiveWriteStrategy.scala:121-135 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
  • Duplicated block (15 lines × 2) src/main/scala/ai/starlake/utils/ProcessRunner.scala:60 — src/main/scala/ai/starlake/utils/ProcessRunner.scala:60-74 | src/main/scala/ai/starlake/utils/ProcessRunner.scala:83-97 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Read the line range as the matched WINDOW rather than a finished unit: at `src/main/scala/ai/starlake/utils/ProcessRunner.scala:60` it runs out through the closing brace of the declaration holding it — the window is that declaration's tail, not a fragment that begins part-way through something, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that. ★ These copies have DRIFTED, and that is worth reading before extracting anything: just before the matched lines, `src/main/scala/ai/starlake/utils/ProcessRunner.scala:59` calls `info`, `mkString` and `src/main/scala/ai/starlake/utils/ProcessRunner.scala:82` does not — after which the two agree again for 4 more lines. One of those two behaviours is the intended one and the other is what a copy-paste left behind, so decide which BEFORE unifying them: extracting the shared part will silently settle it, and if the copy that skips the call is the wrong one, that bug is already live.
D4 · Code Duplication · Duplicated block (11 lines × 2) · ×3
  • Duplicated block (11 lines × 2) src/main/scala/ai/starlake/job/transform/JdbcAutoTask.scala:308 — src/main/scala/ai/starlake/job/transform/JdbcAutoTask.scala:308-318 | REDACTED:931-941 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from both call sites, so a change lands once. Read the line range as the matched WINDOW rather than a finished unit: at `src/main/scala/ai/starlake/job/transform/JdbcAutoTask.scala:308` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
  • Duplicated block (11 lines × 2) src/main/scala/ai/starlake/utils/ProcessRunner.scala:23 — src/main/scala/ai/starlake/utils/ProcessRunner.scala:23-33 | src/main/scala/ai/starlake/utils/ProcessRunner.scala:38-48 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
  • Duplicated block (11 lines × 2) REDACTED:267 — REDACTED:267-277 | src/main/scala/ai/starlake/utils/conversion/DuckDbUtils.scala:76-86 — the copies span different directories, so extracting a shared function means choosing where it lives: put it somewhere both call sites can already reach — a location they all depend on today, or a new shared one if there is none — and call it from each site; until then, every change has to be made twice.
D4 · Code Duplication · Duplicated block (9 lines × 2) · ×3
  • Duplicated block (9 lines × 2) src/main/scala/ai/starlake/extract/BigQueryTableInfo.scala:122 — src/main/scala/ai/starlake/extract/BigQueryTableInfo.scala:122-130 | REDACTED:328-336 — the copies span different directories, so extracting a shared function means choosing where it lives: put it somewhere both call sites can already reach — a location they all depend on today, or a new shared one if there is none — and call it from each site; until then, every change has to be made twice. Read the line range as the matched WINDOW rather than a finished unit: at `src/main/scala/ai/starlake/extract/BigQueryTableInfo.scala:122` it does not close everything it opens, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
  • Duplicated block (9 lines × 2) src/main/scala/ai/starlake/schema/model/EnvDesc.scala:208 — src/main/scala/ai/starlake/schema/model/EnvDesc.scala:208-216 | src/main/scala/ai/starlake/schema/model/EnvDesc.scala:253-261 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Read the line range as the matched WINDOW rather than a finished unit: at `src/main/scala/ai/starlake/schema/model/EnvDesc.scala:208` it does not close everything it opens, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
  • Duplicated block (9 lines × 2) src/main/scala/ai/starlake/utils/AnyTemplateLoader.scala:72 — src/main/scala/ai/starlake/utils/AnyTemplateLoader.scala:72-80 | src/main/scala/ai/starlake/utils/AnyTemplateLoader.scala:98-106 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. ★ These copies have DRIFTED, and that is worth reading before extracting anything: just after the matched lines, `src/main/scala/ai/starlake/utils/AnyTemplateLoader.scala:107` calls `fromResource`, `Exception` and `src/main/scala/ai/starlake/utils/AnyTemplateLoader.scala:81` does not — after which the two agree again for 2 more lines. One of those two behaviours is the intended one and the other is what a copy-paste left behind, so decide which BEFORE unifying them: extracting the shared part will silently settle it, and if the copy that skips the call is the wrong one, that bug is already live.
GD1 · Unfinished & placeholder code · Unfinished stub · ×3
  • Unfinished stub — body is `???` src/main/scala/ai/starlake/job/transform/JdbcAutoTask.scala:663 — `buildRLSQueries` is a shipped member whose whole body is still `???`, and nothing in the repository overrides it — generated scaffolding that was never completed. Implement it or remove the dead surface.
  • Unfinished stub — body is `???` src/main/scala/ai/starlake/schema/handlers/SchemaHandler.scala:2446 — `upsertAttribute` is a shipped member whose whole body is still `???`, and nothing in the repository overrides it — generated scaffolding that was never completed. Implement it or remove the dead surface.
  • Unfinished stub — body is a "not implemented" error src/main/scala/ai/starlake/schema/model/Rejection.scala:131 — `sparkType` is a shipped member whose whole body is still a "not implemented" error, and nothing in the repository overrides it — generated scaffolding that was never completed. Implement it or remove the dead surface.
IC1 · Incompleteness & stubs · Unfinished stub · ×3
  • Unfinished stub — body is ??? / NotImplementedError src/main/scala/ai/starlake/job/transform/JdbcAutoTask.scala:663 — `buildRLSQueries` is a shipped method whose whole body is `???` (or throws NotImplementedError), and nothing in the repository overrides it — scaffolding that was never completed. Implement it or remove the dead surface.
  • Unfinished stub — body is ??? / NotImplementedError src/main/scala/ai/starlake/schema/handlers/SchemaHandler.scala:2446 — `upsertAttribute` is a shipped method whose whole body is `???` (or throws NotImplementedError), and nothing in the repository overrides it — scaffolding that was never completed. Implement it or remove the dead surface.
  • Unfinished stub — throws a "not implemented" exception src/main/scala/ai/starlake/schema/model/Rejection.scala:131 — `sparkType` throws an exception whose message says it isn't finished — a not-implemented stub wearing an ordinary exception. Implement it or remove it.
X3 · Exception handling · Swallowed exception (caught, then discarded) · ×3
  • Swallowed exception (caught, then discarded) REDACTED:1415 — `catch (Exception)` takes every exception and records none of it — the body neither logs it, rethrows it, nor even names it, so the failure is discarded as completely as by an empty catch and only the substituted value survives. Log it through whatever this codebase already uses to report problems, narrow the catch to the exception this call can actually raise, or say in a comment on the catch why the failure genuinely cannot matter.
  • Swallowed exception (caught, then discarded) REDACTED:187 — `case NonFatal(_)` takes every exception and records none of it — the body neither logs it, rethrows it, nor even names it, so the failure is discarded as completely as by an empty catch and only the substituted value survives. Log it through whatever this codebase already uses to report problems, narrow the catch to the exception this call can actually raise, or say in a comment on the catch why the failure genuinely cannot matter.
  • Swallowed exception (caught, then discarded) src/main/scala/ai/starlake/utils/Utils.scala:240 — `case _: Exception` takes every exception and records none of it — the body neither logs it, rethrows it, nor even names it, so the failure is discarded as completely as by an empty catch and only the substituted value survives. Log it through whatever this codebase already uses to report problems, narrow the catch to the exception this call can actually raise, or say in a comment on the catch why the failure genuinely cannot matter.
D4 · Code Duplication · Duplicated block (25 lines × 2) · ×2
  • Duplicated block (25 lines × 2) src/main/scala/ai/starlake/config/SettingsCmd.scala:56 — src/main/scala/ai/starlake/config/SettingsCmd.scala:56-80 | src/main/scala/ai/starlake/schema/handlers/ValidateCmd.scala:77-101 — the copies span different directories, so extracting a shared function means choosing where it lives: put it somewhere both call sites can already reach — a location they all depend on today, or a new shared one if there is none — and call it from each site; until then, every change has to be made twice.
  • Duplicated block (25 lines × 2) REDACTED:639 — REDACTED:639-663 | src/main/scala/ai/starlake/job/ingest/loaders/SnowflakeNativeLoader.scala:432-456 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from both call sites, so a change lands once. Read the line range as the matched WINDOW rather than a finished unit: at `REDACTED:639` it does not close everything it opens, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
D4 · Code Duplication · Duplicated block (20 lines × 2) · ×2
  • Duplicated block (20 lines × 2) REDACTED:525 — REDACTED:525-544 | src/main/scala/ai/starlake/job/ingest/loaders/NativeLoader.scala:175-194 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from both call sites, so a change lands once. Read the line range as the matched WINDOW rather than a finished unit: at `REDACTED:525` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
  • Duplicated block (20 lines × 2) src/main/scala/ai/starlake/job/ingest/AuditLog.scala:155 — src/main/scala/ai/starlake/job/ingest/AuditLog.scala:155-178 | src/main/scala/ai/starlake/job/metrics/ExpectationJob.scala:383-402 — the copies span different directories, so extracting a shared function means choosing where it lives: put it somewhere both call sites can already reach — a location they all depend on today, or a new shared one if there is none — and call it from each site; until then, every change has to be made twice. Read the line range as the matched WINDOW rather than a finished unit: at `src/main/scala/ai/starlake/job/ingest/AuditLog.scala:155` it does not close everything it opens, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
D4 · Code Duplication · Duplicated block (18 lines × 2) · ×2
  • Duplicated block (18 lines × 2) REDACTED:186 — REDACTED:186-203 | src/main/scala/ai/starlake/job/ingest/loaders/SnowflakeNativeLoader.scala:92-109 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from both call sites, so a change lands once. Read the line range as the matched WINDOW rather than a finished unit: at `REDACTED:186` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
  • Duplicated block (18 lines × 2) src/main/scala/ai/starlake/schema/model/SchemaInfo.scala:509 — src/main/scala/ai/starlake/schema/model/SchemaInfo.scala:509-526 | src/main/scala/ai/starlake/schema/model/SchemaInfo.scala:544-561 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Read the line range as the matched WINDOW rather than a finished unit: at `src/main/scala/ai/starlake/schema/model/SchemaInfo.scala:509` it does not close everything it opens, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
D4 · Code Duplication · Duplicated block (17 lines × 2) · ×2
  • Duplicated block (17 lines × 2) src/main/scala/ai/starlake/job/sink/bigquery/BigQueryJobBase.scala:1268 — src/main/scala/ai/starlake/job/sink/bigquery/BigQueryJobBase.scala:1268-1284 | src/main/scala/ai/starlake/job/transform/BigQueryAutoTask.scala:830-846 — the copies span different directories, so extracting a shared function means choosing where it lives: put it somewhere both call sites can already reach — a location they all depend on today, or a new shared one if there is none — and call it from each site; until then, every change has to be made twice. Read the line range as the matched WINDOW rather than a finished unit: at `src/main/scala/ai/starlake/job/sink/bigquery/BigQueryJobBase.scala:1268` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that. ★ These copies have DRIFTED, and that is worth reading before extracting anything: just after the matched lines, `src/main/scala/ai/starlake/job/transform/BigQueryAutoTask.scala:847` calls `getBqTableForNative` and `src/main/scala/ai/starlake/job/sink/bigquery/BigQueryJobBase.scala:1285` does not — after which the two agree again for 4 more lines. One of those two behaviours is the intended one and the other is what a copy-paste left behind, so decide which BEFORE unifying them: extracting the shared part will silently settle it, and if the copy that skips the call is the wrong one, that bug is already live.
  • Duplicated block (17 lines × 2) src/main/scala/ai/starlake/lineage/TableDependencies.scala:56 — src/main/scala/ai/starlake/lineage/TableDependencies.scala:56-72 | src/main/scala/ai/starlake/lineage/TableDependencies.scala:100-116 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Read the line range as the matched WINDOW rather than a finished unit: at `src/main/scala/ai/starlake/lineage/TableDependencies.scala:56` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
D4 · Code Duplication · Duplicated block (13 lines × 2) · ×2
  • Duplicated block (13 lines × 2) src/main/scala/ai/starlake/job/sink/bigquery/BigQueryJobResult.scala:67 — src/main/scala/ai/starlake/job/sink/bigquery/BigQueryJobResult.scala:67-79 | src/main/scala/ai/starlake/job/sink/bigquery/BigQueryJobResult.scala:122-134 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
  • Duplicated block (13 lines × 2) REDACTED:663 — REDACTED:663-675 | src/main/scala/ai/starlake/job/transform/JdbcAutoTask.scala:143-155 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from both call sites, so a change lands once. Read the line range as the matched WINDOW rather than a finished unit: at `REDACTED:663` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
D4 · Code Duplication · Duplicated block (12 lines × 2) · ×2
  • Duplicated block (12 lines × 2) src/main/scala/ai/starlake/job/metrics/ExpectationAssertionHandler.scala:57 — src/main/scala/ai/starlake/job/metrics/ExpectationAssertionHandler.scala:57-68 | src/main/scala/ai/starlake/job/metrics/ExpectationAssertionHandler.scala:78-89 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Read the line range as the matched WINDOW rather than a finished unit: at `src/main/scala/ai/starlake/job/metrics/ExpectationAssertionHandler.scala:57` it runs out through the closing brace of the declaration holding it — the window is that declaration's tail, not a fragment that begins part-way through something, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that. ★ These copies have DRIFTED, and that is worth reading before extracting anything: just before the matched lines, `src/main/scala/ai/starlake/job/metrics/ExpectationAssertionHandler.scala:54` calls `withJDBCConnection`, `schemaHandler`, `dataBranch` and `src/main/scala/ai/starlake/job/metrics/ExpectationAssertionHandler.scala:76` does not — after which the two agree again for 2 more lines. One of those two behaviours is the intended one and the other is what a copy-paste left behind, so decide which BEFORE unifying them: extracting the shared part will silently settle it, and if the copy that skips the call is the wrong one, that bug is already live.
  • Duplicated block (12 lines × 2) src/main/scala/ai/starlake/schema/model/DomainInfo.scala:273 — src/main/scala/ai/starlake/schema/model/DomainInfo.scala:273-284 | src/main/scala/ai/starlake/schema/model/DomainInfo.scala:295-306 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Read the line range as the matched WINDOW rather than a finished unit: at `src/main/scala/ai/starlake/schema/model/DomainInfo.scala:273` it does not close everything it opens, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
D4 · Code Duplication · Duplicated block (6 lines × 2) · ×2
  • Duplicated block (6 lines × 2) src/main/scala/ai/starlake/job/ingest/DsvIngestionJob.scala:80 — src/main/scala/ai/starlake/job/ingest/DsvIngestionJob.scala:80-85 | src/main/scala/ai/starlake/job/ingest/DsvIngestionJob.scala:142-147 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Read the line range as the matched WINDOW rather than a finished unit: at `src/main/scala/ai/starlake/job/ingest/DsvIngestionJob.scala:80` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
  • Duplicated block (6 lines × 2) src/main/scala/ai/starlake/lineage/AclDependencies.scala:738 — src/main/scala/ai/starlake/lineage/AclDependencies.scala:738-743 | src/main/scala/ai/starlake/lineage/AclDependencies.scala:762-767 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
D6 · Cohesion (LCOM4) · Low cohesion · ×2
  • Low cohesion: SchemaHandler (LCOM4 6) src/main/scala/ai/starlake/schema/handlers/SchemaHandler.scala:103 — SchemaHandler's methods fall into 6 groups that share no field and call none of each other, against a bar of more than 3 for this run (LCOM4, configurable — your repository's bar is the one quoted here). Each group is a set of methods reachable from one another through shared fields or direct calls, so 6 groups means the type has that many internally-connected clusters with nothing tying them together. Types whose shape makes a high count expected — and which would otherwise dominate this list — are excluded before this row is raised, so this is a genuine split candidate rather than a metric reading. It is still a shape, not a defect: confirm the groups match responsibilities you can name before splitting.
  • Low cohesion: SLResultSetHelperService (LCOM4 4) src/main/scala/ai/starlake/extract/SLResultSetHelperService.scala:32 — SLResultSetHelperService's methods fall into 4 groups that share no field and call none of each other, against a bar of more than 3 for this run (LCOM4, configurable — your repository's bar is the one quoted here). Each group is a set of methods reachable from one another through shared fields or direct calls, so 4 groups means the type has that many internally-connected clusters with nothing tying them together. Types whose shape makes a high count expected — and which would otherwise dominate this list — are excluded before this row is raised, so this is a genuine split candidate rather than a metric reading. It is still a shape, not a defect: confirm the groups match responsibilities you can name before splitting.
X18 · Disposal-pattern correctness · Disposable created and abandoned in the function that made it · ×2
  • Disposable created and abandoned in the function that made it src/main/scala/ai/starlake/schema/model/AutoTaskInfo.scala:538 — `scala.io.Source.fromFile(…)` opens something that has to be given back — a file descriptor, a socket, a directory handle — and its only use reads `.getLines` through it and drops it, so this expression both creates the value and is the last place that can release it, and does not. Every call leaks one more open handle until the collector happens to reach it: `Too many open files` under load, and on Windows a file that cannot be replaced or deleted while it is held. Open it with `scala.util.Using.resource(scala.io.Source.fromFile(…)) { r => … }`, which releases it on every path including a throw, or hold it and call `.close()` in a `finally`.
  • Disposable created and abandoned in the function that made it src/main/scala/ai/starlake/utils/CliConfig.scala:124 — `Source.fromResource(…)` opens something that has to be given back — a file descriptor, a socket, a directory handle — and its only use reads `.mkString` through it and drops it, so this expression both creates the value and is the last place that can release it, and does not. Every call leaks one more open handle until the collector happens to reach it: `Too many open files` under load, and on Windows a file that cannot be replaced or deleted while it is held. Open it with `scala.util.Using.resource(Source.fromResource(…)) { r => … }`, which releases it on every path including a throw, or hold it and call `.close()` in a `finally`.
AC3 · Page structure · Page without a main landmark · ×1
  • Page without a main landmark src/main/resources/templates/compare/index.html.j2:2 — No <main> (or role="main") means no "skip to content" target and a weaker landmark map. Wrap the primary content in <main>.
AC7 · A11y enforcement · Accessibility enforcement below the top rung · ×1
  • Accessibility enforcement below the top rung — No accessibility enforcement found — no automated accessibility check runs over the HTML your app renders. Assert the accessibility invariants over that HTML in the test suite you already have (parse the output and assert, or drive a browser), and gate that test in CI so a regression blocks the merge. What was searched, so you can tell an absence from a miss: the 3 markup file(s) this pass actually assessed, the linter configuration checked in beside them, and this repository's test and CI files — matched by name against the accessibility checkers this dimension carries. An audit run outside the repository, a hosted scanner, or a check whose name is not one of those, is not seen here.
D1 · Cyclomatic Complexity · BigQueryAutoTask.runBQ (cyclomatic 41) · ×1
  • BigQueryAutoTask.runBQ (cyclomatic 41) src/main/scala/ai/starlake/job/transform/BigQueryAutoTask.scala:321 — BigQueryAutoTask.runBQ has cyclomatic complexity 41 (threshold 15). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Where every arm is uniform — the same kind of value, with no behaviour of its own — a table keyed by the case is the shorter form; wherever the arms carry different data or different behaviour, keep them as cases, because collapsing those trades an explicit, reviewable set of cases for nothing.
D1 · Cyclomatic Complexity · RestAPIDataExtractor.extractEndpointData (cyclomatic 39) · ×1
  • RestAPIDataExtractor.extractEndpointData (cyclomatic 39) src/main/scala/ai/starlake/extract/impl/restapi/RestAPIDataExtractor.scala:67 — RestAPIDataExtractor.extractEndpointData has cyclomatic complexity 39 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
D1 · Cyclomatic Complexity · ConnectionInfo.checkValidity (cyclomatic 36) · ×1
  • ConnectionInfo.checkValidity (cyclomatic 36) src/main/scala/ai/starlake/config/ConnectionInfo.scala:220 — ConnectionInfo.checkValidity has cyclomatic complexity 36 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
D1 · Cyclomatic Complexity · OpenAPISchemaExtractor.openAPISchemaToStarlakeSchema (cyclomatic 35) · ×1
  • OpenAPISchemaExtractor.openAPISchemaToStarlakeSchema (cyclomatic 35) src/main/scala/ai/starlake/extract/impl/openapi/OpenAPISchemaExtractor.scala:738 — OpenAPISchemaExtractor.openAPISchemaToStarlakeSchema has cyclomatic complexity 35 (threshold 15). To reduce it, split the body: these branches sit side by side rather than nested inside one another, so extracting each one on its own would leave a function per branch. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.
D1 · Cyclomatic Complexity · JdbcAutoTask.runJDBC (cyclomatic 31) · ×1
  • JdbcAutoTask.runJDBC (cyclomatic 31) src/main/scala/ai/starlake/job/transform/JdbcAutoTask.scala:204 — JdbcAutoTask.runJDBC has cyclomatic complexity 31 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
D1 · Cyclomatic Complexity · Setup.askUserWhichConfigToEnableWindows (cyclomatic 31) · ×1
  • Setup.askUserWhichConfigToEnableWindows (cyclomatic 31) REDACTED:936 — Setup.askUserWhichConfigToEnableWindows has cyclomatic complexity 31 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
D1 · Cyclomatic Complexity · flow.ModelNode (cyclomatic 30) · ×1
  • flow.ModelNode (cyclomatic 30) src/main/resources/templates/site/standalone/flow.js:121 — flow.ModelNode has cyclomatic complexity 30 (threshold 15). Of this number, 24 points are the body's own statements and 6 belong to one function literal inside it that branches. To reduce it, split the body: these branches sit side by side rather than nested inside one another, so extracting each one on its own would leave a function per branch. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.
D1 · Cyclomatic Complexity · StarlakeConnectionPool.getConnection (cyclomatic 27) · ×1
  • StarlakeConnectionPool.getConnection (cyclomatic 27) src/main/scala/ai/starlake/extract/StarlakeConnectionPool.scala:142 — StarlakeConnectionPool.getConnection has cyclomatic complexity 27 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
D1 · Cyclomatic Complexity · LastExportUtils.getBoundaries (cyclomatic 26) · ×1
  • LastExportUtils.getBoundaries (cyclomatic 26) REDACTED:38 — LastExportUtils.getBoundaries has cyclomatic complexity 26 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
D1 · Cyclomatic Complexity · InferSchemaJob.parseJsonSchema (cyclomatic 24) · ×1
  • InferSchemaJob.parseJsonSchema (cyclomatic 24) src/main/scala/ai/starlake/job/infer/InferSchemaJob.scala:520 — InferSchemaJob.parseJsonSchema has cyclomatic complexity 24 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
D1 · Cyclomatic Complexity · RowValidator.validate (cyclomatic 24) · ×1
  • RowValidator.validate (cyclomatic 24) src/main/scala/ai/starlake/job/validator/RowValidator.scala:152 — RowValidator.validate has cyclomatic complexity 24 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
D1 · Cyclomatic Complexity · TaskViewDependency.dependencies (cyclomatic 24) · ×1
  • TaskViewDependency.dependencies (cyclomatic 24) src/main/scala/ai/starlake/lineage/TaskViewDependency.scala:73 — TaskViewDependency.dependencies has cyclomatic complexity 24 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
D1 · Cyclomatic Complexity · WriteStrategy.checkValidity (cyclomatic 24) · ×1
  • WriteStrategy.checkValidity (cyclomatic 24) src/main/scala/ai/starlake/schema/model/WriteStrategy.scala:33 — WriteStrategy.checkValidity has cyclomatic complexity 24 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
D1 · Cyclomatic Complexity · DuckDbNativeLoader.singleStepLoad (cyclomatic 23) · ×1
  • DuckDbNativeLoader.singleStepLoad (cyclomatic 23) REDACTED:609 — DuckDbNativeLoader.singleStepLoad has cyclomatic complexity 23 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
D1 · Cyclomatic Complexity · RowValidator.prepareData (cyclomatic 23) · ×1
  • RowValidator.prepareData (cyclomatic 23) src/main/scala/ai/starlake/job/validator/RowValidator.scala:403 — RowValidator.prepareData has cyclomatic complexity 23 (threshold 15). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Where every arm is uniform — the same kind of value, with no behaviour of its own — a table keyed by the case is the shorter form; wherever the arms carry different data or different behaviour, keep them as cases, because collapsing those trades an explicit, reviewable set of cases for nothing.
D1 · Cyclomatic Complexity · Setup.askUserWhichConfigToEnableUnix (cyclomatic 23) · ×1
  • Setup.askUserWhichConfigToEnableUnix (cyclomatic 23) REDACTED:823 — Setup.askUserWhichConfigToEnableUnix has cyclomatic complexity 23 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
D1 · Cyclomatic Complexity · IngestionWorkflow.load (cyclomatic 22) · ×1
  • IngestionWorkflow.load (cyclomatic 22) src/main/scala/ai/starlake/workflow/IngestionWorkflow.scala:344 — IngestionWorkflow.load has cyclomatic complexity 22 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
D1 · Cyclomatic Complexity · AppConfig.checkValidity (cyclomatic 21) · ×1
  • AppConfig.checkValidity (cyclomatic 21) src/main/scala/ai/starlake/config/Settings.scala:489 — AppConfig.checkValidity has cyclomatic complexity 21 (threshold 15). To reduce it, split the body: these branches sit side by side rather than nested inside one another, so extracting each one on its own would leave a function per branch. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.
D1 · Cyclomatic Complexity · SparkEnv.session (cyclomatic 21) · ×1
  • SparkEnv.session (cyclomatic 21) src/main/scala/ai/starlake/config/SparkEnv.scala:66 — SparkEnv.session has cyclomatic complexity 21 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
D1 · Cyclomatic Complexity · ExtractDataJob.extractTableData (cyclomatic 21) · ×1
  • ExtractDataJob.extractTableData (cyclomatic 21) src/main/scala/ai/starlake/extract/ExtractDataJob.scala:457 — ExtractDataJob.extractTableData has cyclomatic complexity 21 (threshold 15). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Where every arm is uniform — the same kind of value, with no behaviour of its own — a table keyed by the case is the shorter form; wherever the arms carry different data or different behaviour, keep them as cases, because collapsing those trades an explicit, reviewable set of cases for nothing.
D1 · Cyclomatic Complexity · PrimitiveType.instantFromString (cyclomatic 20) · ×1
  • PrimitiveType.instantFromString (cyclomatic 20) src/main/scala/ai/starlake/schema/model/PrimitiveType.scala:284 — PrimitiveType.instantFromString has cyclomatic complexity 20 (threshold 15). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Where every arm is uniform — the same kind of value, with no behaviour of its own — a table keyed by the case is the shorter form; wherever the arms carry different data or different behaviour, keep them as cases, because collapsing those trades an explicit, reviewable set of cases for nothing.
D1 · Cyclomatic Complexity · JsonIngestionUtil.compareTypes (cyclomatic 20) · ×1
  • JsonIngestionUtil.compareTypes (cyclomatic 20) src/main/scala/org/apache/spark/sql/execution/datasources/json/JsonIngestionUtil.scala:63 — JsonIngestionUtil.compareTypes has cyclomatic complexity 20 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
D1 · Cyclomatic Complexity · SparkAutoTask.runSparkOnSpark (cyclomatic 19) · ×1
  • SparkAutoTask.runSparkOnSpark (cyclomatic 19) REDACTED:300 — SparkAutoTask.runSparkOnSpark has cyclomatic complexity 19 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
D1 · Cyclomatic Complexity · Yml2DDLJob.run (cyclomatic 19) · ×1
  • Yml2DDLJob.run (cyclomatic 19) src/main/scala/ai/starlake/schema/generator/Yml2DDLJob.scala:71 — Yml2DDLJob.run has cyclomatic complexity 19 (threshold 15). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Where every arm is uniform — the same kind of value, with no behaviour of its own — a table keyed by the case is the shorter form; wherever the arms carry different data or different behaviour, keep them as cases, because collapsing those trades an explicit, reviewable set of cases for nothing.
D1 · Cyclomatic Complexity · Metadata.keepIfDifferent (cyclomatic 19) · ×1
  • Metadata.keepIfDifferent (cyclomatic 19) src/main/scala/ai/starlake/schema/model/Metadata.scala:391 — Metadata.keepIfDifferent has cyclomatic complexity 19 (threshold 15). To reduce it, split the body: these branches sit side by side rather than nested inside one another, so extracting each one on its own would leave a function per branch. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.
D1 · Cyclomatic Complexity · SingleUserMainServer.run (cyclomatic 19) · ×1
  • SingleUserMainServer.run (cyclomatic 19) src/main/scala/ai/starlake/serve/SingleUserMainServer.scala:27 — SingleUserMainServer.run has cyclomatic complexity 19 (threshold 15). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Where every arm is uniform — the same kind of value, with no behaviour of its own — a table keyed by the case is the shorter form; wherever the arms carry different data or different behaviour, keep them as cases, because collapsing those trades an explicit, reviewable set of cases for nothing.
D1 · Cyclomatic Complexity · SqlCommentStripper.stripComments (cyclomatic 19) · ×1
  • SqlCommentStripper.stripComments (cyclomatic 19) src/main/scala/ai/starlake/sql/SqlCommentStripper.scala:13 — SqlCommentStripper.stripComments has cyclomatic complexity 19 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
D1 · Cyclomatic Complexity · Setup.generateVersions (cyclomatic 19) · ×1
  • Setup.generateVersions (cyclomatic 19) REDACTED:637 — Setup.generateVersions has cyclomatic complexity 19 (threshold 15). To reduce it, name the conditions: bind each compound test to a well-named local or a small predicate function, so the body reads as a sequence of named decisions rather than a chain of operators.
D1 · Cyclomatic Complexity · Setup.main (cyclomatic 19) · ×1
  • Setup.main (cyclomatic 19) REDACTED:1037 — Setup.main has cyclomatic complexity 19 (threshold 15). To reduce it, split the body: these branches sit side by side rather than nested inside one another, so extracting each one on its own would leave a function per branch. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.
D1 · Cyclomatic Complexity · FreshnessJob.freshness (cyclomatic 18) · ×1
  • FreshnessJob.freshness (cyclomatic 18) REDACTED:145 — FreshnessJob.freshness has cyclomatic complexity 18 (threshold 15). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Where every arm is uniform — the same kind of value, with no behaviour of its own — a table keyed by the case is the shorter form; wherever the arms carry different data or different behaviour, keep them as cases, because collapsing those trades an explicit, reviewable set of cases for nothing.
D1 · Cyclomatic Complexity · PreLoadCmd.run (cyclomatic 18) · ×1
  • PreLoadCmd.run (cyclomatic 18) src/main/scala/ai/starlake/job/ingest/PreLoadCmd.scala:95 — PreLoadCmd.run has cyclomatic complexity 18 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
D1 · Cyclomatic Complexity · Bootstrap.bootstrap (cyclomatic 17) · ×1
  • Bootstrap.bootstrap (cyclomatic 17) src/main/scala/ai/starlake/job/bootstrap/Bootstrap.scala:80 — Bootstrap.bootstrap has cyclomatic complexity 17 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
D1 · Cyclomatic Complexity · DagInfo.checkValidity (cyclomatic 17) · ×1
  • DagInfo.checkValidity (cyclomatic 17) src/main/scala/ai/starlake/schema/model/DagInfo.scala:76 — DagInfo.checkValidity has cyclomatic complexity 17 (threshold 15). To reduce it, split the body: these branches sit side by side rather than nested inside one another, so extracting each one on its own would leave a function per branch. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.
D1 · Cyclomatic Complexity · SchemaInfo.checkValidity (cyclomatic 17) · ×1
  • SchemaInfo.checkValidity (cyclomatic 17) src/main/scala/ai/starlake/schema/model/SchemaInfo.scala:319 — SchemaInfo.checkValidity has cyclomatic complexity 17 (threshold 15). To reduce it, split the body: these branches sit side by side rather than nested inside one another, so extracting each one on its own would leave a function per branch. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.
D1 · Cyclomatic Complexity · SqlParser.getSelectStatementIndex (cyclomatic 17) · ×1
  • SqlParser.getSelectStatementIndex (cyclomatic 17) src/main/scala/ai/starlake/sql/SqlParser.scala:178 — SqlParser.getSelectStatementIndex has cyclomatic complexity 17 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
D1 · Cyclomatic Complexity · JarUtil.collectFromUrl (cyclomatic 17) · ×1
  • JarUtil.collectFromUrl (cyclomatic 17) src/main/scala/ai/starlake/utils/JarUtil.scala:14 — JarUtil.collectFromUrl has cyclomatic complexity 17 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
D1 · Cyclomatic Complexity · IngestionWorkflow.listStageFiles (cyclomatic 17) · ×1
  • IngestionWorkflow.listStageFiles (cyclomatic 17) src/main/scala/ai/starlake/workflow/IngestionWorkflow.scala:179 — IngestionWorkflow.listStageFiles has cyclomatic complexity 17 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
D1 · Cyclomatic Complexity · BigQuerySchemaConverters.getStandardDataType (cyclomatic 17) · ×1
  • BigQuerySchemaConverters.getStandardDataType (cyclomatic 17) src/main/java/ai/starlake/utils/repackaged/BigQuerySchemaConverters.java:357 — BigQuerySchemaConverters.getStandardDataType has cyclomatic complexity 17 (threshold 15). To reduce it, split the body: these branches sit side by side rather than nested inside one another, so extracting each one on its own would leave a function per branch. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.
D1 · Cyclomatic Complexity · Console.handler (cyclomatic 16) · ×1
  • Console.handler (cyclomatic 16) src/main/scala/ai/starlake/console/Console.scala:64 — Console.handler has cyclomatic complexity 16 (threshold 15). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Where every arm is uniform — the same kind of value, with no behaviour of its own — a table keyed by the case is the shorter form; wherever the arms carry different data or different behaviour, keep them as cases, because collapsing those trades an explicit, reviewable set of cases for nothing.
D1 · Cyclomatic Complexity · TableAttribute.checkValidity (cyclomatic 16) · ×1
  • TableAttribute.checkValidity (cyclomatic 16) src/main/scala/ai/starlake/schema/model/TableAttribute.scala:153 — TableAttribute.checkValidity has cyclomatic complexity 16 (threshold 15). To reduce it, split the body: these branches sit side by side rather than nested inside one another, so extracting each one on its own would leave a function per branch. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.
D1 · Cyclomatic Complexity · SparkUtils.sqlSchema (cyclomatic 16) · ×1
  • SparkUtils.sqlSchema (cyclomatic 16) REDACTED:298 — SparkUtils.sqlSchema has cyclomatic complexity 16 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
D17 · Explicit Debt · FixmeComment · ×1
  • FixmeComment src/test/scala/ai/starlake/schema/handlers/SchemaInfo2HandlerSpec.scala:464 — // FIXME, check why this works on master — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `// REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
D2 · Cognitive Complexity · RestAPIDataExtractor.extractEndpointData (cognitive 90) · ×1
  • RestAPIDataExtractor.extractEndpointData (cognitive 90) src/main/scala/ai/starlake/extract/impl/restapi/RestAPIDataExtractor.scala:67 — RestAPIDataExtractor.extractEndpointData has cognitive complexity 90 (threshold 15). Drivers by points: if/else 29 (53 pts), match/switch 5 (23 pts), loops 4 (10 pts), boolean chains 4 (nesting depth added 48). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D2 · Cognitive Complexity · GenericIngestionJob.loadDataSet (cognitive 82) · ×1
  • GenericIngestionJob.loadDataSet (cognitive 82) src/main/scala/ai/starlake/job/ingest/GenericIngestionJob.scala:172 — GenericIngestionJob.loadDataSet has cognitive complexity 82 (threshold 15). Drivers by points: loops 12 (78 pts), if/else 3 (4 pts) (nesting depth added 67). To reduce it, break up the iteration: give each loop body a named function, and split a multi-phase loop into one function per phase so no single body carries the whole pipeline.
D2 · Cognitive Complexity · BigQueryAutoTask.runBQ (cognitive 82) · ×1
  • BigQueryAutoTask.runBQ (cognitive 82) src/main/scala/ai/starlake/job/transform/BigQueryAutoTask.scala:321 — BigQueryAutoTask.runBQ has cognitive complexity 82 (threshold 15). Drivers by points: match/switch 13 (42 pts), if/else 16 (38 pts), boolean chains 2 (nesting depth added 51). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
D2 · Cognitive Complexity · ConnectionInfo.checkValidity (cognitive 74) · ×1
  • ConnectionInfo.checkValidity (cognitive 74) src/main/scala/ai/starlake/config/ConnectionInfo.scala:220 — ConnectionInfo.checkValidity has cognitive complexity 74 (threshold 15). Drivers by points: if/else 22 (61 pts), boolean chains 7, match/switch 3 (6 pts) (nesting depth added 42). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D2 · Cognitive Complexity · InferSchemaJob.parseJsonSchema (cognitive 59) · ×1
  • InferSchemaJob.parseJsonSchema (cognitive 59) src/main/scala/ai/starlake/job/infer/InferSchemaJob.scala:520 — InferSchemaJob.parseJsonSchema has cognitive complexity 59 (threshold 15). Drivers by points: if/else 26 (50 pts), match/switch 2 (6 pts), boolean chains 3 (nesting depth added 28). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D2 · Cognitive Complexity · TaskViewDependency.dependencies (cognitive 54) · ×1
  • TaskViewDependency.dependencies (cognitive 54) src/main/scala/ai/starlake/lineage/TaskViewDependency.scala:73 — TaskViewDependency.dependencies has cognitive complexity 54 (threshold 15). Drivers by points: if/else 15 (29 pts), match/switch 6 (16 pts), loops 3 (6 pts), boolean chains 3 (nesting depth added 27). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D2 · Cognitive Complexity · StarlakeConnectionPool.getConnection (cognitive 53) · ×1
  • StarlakeConnectionPool.getConnection (cognitive 53) src/main/scala/ai/starlake/extract/StarlakeConnectionPool.scala:142 — StarlakeConnectionPool.getConnection has cognitive complexity 53 (threshold 15). Drivers by points: if/else 24 (36 pts), match/switch 4 (10 pts), boolean chains 7 (nesting depth added 18). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D2 · Cognitive Complexity · LastExportUtils.getBoundaries (cognitive 49) · ×1
  • LastExportUtils.getBoundaries (cognitive 49) REDACTED:38 — LastExportUtils.getBoundaries has cognitive complexity 49 (threshold 15). Drivers by points: if/else 29 (42 pts), match/switch 4 (7 pts) (nesting depth added 16). The drivers above price the dispatch low by construction — a dispatch is charged once however many cases it lists, while each branch inside an arm is charged in full — so most of this count is what the case bodies hold, and the arms are where it can be reduced. To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
D2 · Cognitive Complexity · JdbcAutoTask.runJDBC (cognitive 49) · ×1
  • JdbcAutoTask.runJDBC (cognitive 49) src/main/scala/ai/starlake/job/transform/JdbcAutoTask.scala:204 — JdbcAutoTask.runJDBC has cognitive complexity 49 (threshold 15). Drivers by points: if/else 16 (32 pts), match/switch 6 (12 pts), boolean chains 5 (nesting depth added 22). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D2 · Cognitive Complexity · DuckDbNativeLoader.singleStepLoad (cognitive 46) · ×1
  • DuckDbNativeLoader.singleStepLoad (cognitive 46) REDACTED:609 — DuckDbNativeLoader.singleStepLoad has cognitive complexity 46 (threshold 15). Drivers by points: if/else 28 (39 pts), match/switch 3 (5 pts), boolean chains 1, error handling 1 (nesting depth added 13). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D2 · Cognitive Complexity · Setup.askUserWhichConfigToEnableUnix (cognitive 44) · ×1
  • Setup.askUserWhichConfigToEnableUnix (cognitive 44) REDACTED:823 — Setup.askUserWhichConfigToEnableUnix has cognitive complexity 44 (threshold 15). Drivers by points: if/else 15 (33 pts), ternaries 3 (6 pts), boolean chains 2, error handling 2, loops 1 (nesting depth added 21). To reduce it, flatten the nesting: this score is depth rather than breadth — most of its points come from checks stacked inside one another, so the work sits several levels in. Invert each enclosing check into an early exit (a return, or the language's equivalent) so the happy path stays at one level, and where a level cannot be exited early, lift the block it encloses into its own named function.
D2 · Cognitive Complexity · SparkEnv.session (cognitive 42) · ×1
  • SparkEnv.session (cognitive 42) src/main/scala/ai/starlake/config/SparkEnv.scala:66 — SparkEnv.session has cognitive complexity 42 (threshold 15). Drivers by points: if/else 12 (38 pts), match/switch 1 (3 pts), boolean chains 1 (nesting depth added 28). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D2 · Cognitive Complexity · OpenAPISchemaExtractor.openAPISchemaToStarlakeSchema (cognitive 41) · ×1
  • OpenAPISchemaExtractor.openAPISchemaToStarlakeSchema (cognitive 41) src/main/scala/ai/starlake/extract/impl/openapi/OpenAPISchemaExtractor.scala:738 — OpenAPISchemaExtractor.openAPISchemaToStarlakeSchema has cognitive complexity 41 (threshold 15). Drivers by points: if/else 24 (29 pts), match/switch 7 (11 pts), boolean chains 1 (nesting depth added 9). To reduce it, split the body: most of this score is breadth rather than depth — checks laid out side by side rather than stacked — so group the statements between the checks into named steps and move each step into its own function. Some of it IS depth: where a check sits inside another whose only job is to reach it, merge the two into one condition, and where an else follows a branch that already returns, drop the trailing else and let the rest of the body continue at one level.
D2 · Cognitive Complexity · SparkExportTask.exportToXls (cognitive 41) · ×1
  • SparkExportTask.exportToXls (cognitive 41) src/main/scala/ai/starlake/job/transform/SparkExportTask.scala:166 — SparkExportTask.exportToXls has cognitive complexity 41 (threshold 15). Drivers by points: if/else 17 (26 pts), match/switch 7 (14 pts), boolean chains 1 (nesting depth added 16). The drivers above price the dispatch low by construction — a dispatch is charged once however many cases it lists, while each branch inside an arm is charged in full — so most of this count is what the case bodies hold, and the arms are where it can be reduced. To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
D2 · Cognitive Complexity · PrimitiveType.instantFromString (cognitive 40) · ×1
  • PrimitiveType.instantFromString (cognitive 40) src/main/scala/ai/starlake/schema/model/PrimitiveType.scala:284 — PrimitiveType.instantFromString has cognitive complexity 40 (threshold 15). Drivers by points: match/switch 7 (25 pts), if/else 6 (14 pts), boolean chains 1 (nesting depth added 26). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
D2 · Cognitive Complexity · Setup.askUserWhichConfigToEnableWindows (cognitive 40) · ×1
  • Setup.askUserWhichConfigToEnableWindows (cognitive 40) REDACTED:936 — Setup.askUserWhichConfigToEnableWindows has cognitive complexity 40 (threshold 15). Drivers by points: ternaries 11 (22 pts), if/else 7 (9 pts), loops 2 (4 pts), match/switch 1 (4 pts), error handling 1 (nesting depth added 18). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D2 · Cognitive Complexity · AutoTask.buildAllSQLQueries (cognitive 38) · ×1
  • AutoTask.buildAllSQLQueries (cognitive 38) REDACTED:374 — AutoTask.buildAllSQLQueries has cognitive complexity 38 (threshold 15). Drivers by points: if/else 18 (32 pts), match/switch 1 (4 pts), boolean chains 2 (nesting depth added 17). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D2 · Cognitive Complexity · Setup.downloadAndDisplayProgress (cognitive 38) · ×1
  • Setup.downloadAndDisplayProgress (cognitive 38) REDACTED:1447 — Setup.downloadAndDisplayProgress has cognitive complexity 38 (threshold 15). Drivers by points: if/else 8 (21 pts), loops 4 (12 pts), error handling 2 (4 pts), boolean chains 1 (nesting depth added 23). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D2 · Cognitive Complexity · JarUtil.collectFromUrl (cognitive 37) · ×1
  • JarUtil.collectFromUrl (cognitive 37) src/main/scala/ai/starlake/utils/JarUtil.scala:14 — JarUtil.collectFromUrl has cognitive complexity 37 (threshold 15). Drivers by points: if/else 8 (21 pts), loops 2 (6 pts), match/switch 2 (6 pts), boolean chains 2, error handling 1 (2 pts) (nesting depth added 22). The drivers above price the dispatch low by construction — a dispatch is charged once however many cases it lists, while each branch inside an arm is charged in full — so most of this count is what the case bodies hold, and the arms are where it can be reduced. To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
D2 · Cognitive Complexity · Metadata.keepIfDifferent (cognitive 36) · ×1
  • Metadata.keepIfDifferent (cognitive 36) src/main/scala/ai/starlake/schema/model/Metadata.scala:391 — Metadata.keepIfDifferent has cognitive complexity 36 (threshold 15). Drivers by points: if/else 36. To reduce it, split the body: this score is breadth rather than depth — many checks laid out side by side rather than nested inside one another, so inverting conditions into early returns has nothing left to flatten. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.
D2 · Cognitive Complexity · InferSchemaHandler.createAttributes (cognitive 35) · ×1
  • InferSchemaHandler.createAttributes (cognitive 35) src/main/scala/ai/starlake/schema/handlers/InferSchemaHandler.scala:453 — InferSchemaHandler.createAttributes has cognitive complexity 35 (threshold 15). Drivers by points: if/else 20 (29 pts), match/switch 2 (3 pts), error handling 1 (2 pts), boolean chains 1 (nesting depth added 11). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D2 · Cognitive Complexity · SqlCommentStripper.stripComments (cognitive 35) · ×1
  • SqlCommentStripper.stripComments (cognitive 35) src/main/scala/ai/starlake/sql/SqlCommentStripper.scala:13 — SqlCommentStripper.stripComments has cognitive complexity 35 (threshold 15). Drivers by points: if/else 16 (28 pts), boolean chains 6, loops 1 (nesting depth added 12). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D2 · Cognitive Complexity · InferSchemaJob.infer (cognitive 34) · ×1
  • InferSchemaJob.infer (cognitive 34) src/main/scala/ai/starlake/job/infer/InferSchemaJob.scala:282 — InferSchemaJob.infer has cognitive complexity 34 (threshold 15). Drivers by points: if/else 16 (24 pts), match/switch 5 (9 pts), boolean chains 1 (nesting depth added 12). The drivers above price the dispatch low by construction — a dispatch is charged once however many cases it lists, while each branch inside an arm is charged in full — so most of this count is what the case bodies hold, and the arms are where it can be reduced. To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
D2 · Cognitive Complexity · RowValidator.validate (cognitive 34) · ×1
  • RowValidator.validate (cognitive 34) src/main/scala/ai/starlake/job/validator/RowValidator.scala:152 — RowValidator.validate has cognitive complexity 34 (threshold 15). Drivers by points: if/else 20 (27 pts), boolean chains 3, match/switch 3, loops 1 (nesting depth added 7). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D2 · Cognitive Complexity · IngestionWorkflow.load (cognitive 34) · ×1
  • IngestionWorkflow.load (cognitive 34) src/main/scala/ai/starlake/workflow/IngestionWorkflow.scala:344 — IngestionWorkflow.load has cognitive complexity 34 (threshold 15). Drivers by points: if/else 13 (22 pts), match/switch 5 (10 pts), boolean chains 2 (nesting depth added 14). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D2 · Cognitive Complexity · SparkUtils.sqlSchema (cognitive 33) · ×1
  • SparkUtils.sqlSchema (cognitive 33) REDACTED:298 — SparkUtils.sqlSchema has cognitive complexity 33 (threshold 15). Drivers by points: if/else 20 (27 pts), match/switch 2 (4 pts), boolean chains 2 (nesting depth added 9). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D2 · Cognitive Complexity · BigQueryJobBase.applyCLS (cognitive 32) · ×1
  • BigQueryJobBase.applyCLS (cognitive 32) src/main/scala/ai/starlake/job/sink/bigquery/BigQueryJobBase.scala:276 — BigQueryJobBase.applyCLS has cognitive complexity 32 (threshold 15). Drivers by points: if/else 7 (15 pts), match/switch 4 (15 pts), boolean chains 2 (nesting depth added 19). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D2 · Cognitive Complexity · SqlParser.getSelectStatementIndex (cognitive 31) · ×1
  • SqlParser.getSelectStatementIndex (cognitive 31) src/main/scala/ai/starlake/sql/SqlParser.scala:178 — SqlParser.getSelectStatementIndex has cognitive complexity 31 (threshold 15). Drivers by points: if/else 8 (17 pts), match/switch 2 (7 pts), boolean chains 5, loops 1 (2 pts) (nesting depth added 15). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D2 · Cognitive Complexity · flow.ModelNode (cognitive 31) · ×1
  • flow.ModelNode (cognitive 31) src/main/resources/templates/site/standalone/flow.js:121 — flow.ModelNode has cognitive complexity 31 (threshold 15). Drivers by points: ternaries 15 (17 pts), boolean chains 14 (nesting depth added 2). Of this number, 25 points are the body's own statements and 6 belong to one function literal inside it that branches. To reduce it, split the body: most of this score is breadth rather than depth — checks laid out side by side rather than stacked — so group the statements between the checks into named steps and move each step into its own function. Some of it IS depth: where a check sits inside another whose only job is to reach it, merge the two into one condition.
D2 · Cognitive Complexity · AppConfig.checkValidity (cognitive 30) · ×1
  • AppConfig.checkValidity (cognitive 30) src/main/scala/ai/starlake/config/Settings.scala:489 — AppConfig.checkValidity has cognitive complexity 30 (threshold 15). Drivers by points: if/else 20 (22 pts), match/switch 6 (7 pts), boolean chains 1 (nesting depth added 3). To reduce it, split the body: most of this score is breadth rather than depth — checks laid out side by side rather than stacked — so group the statements between the checks into named steps and move each step into its own function. Some of it IS depth: where a check sits inside another whose only job is to reach it, merge the two into one condition, and where an else follows a branch that already returns, drop the trailing else and let the rest of the body continue at one level.
D2 · Cognitive Complexity · SparkAutoTask.runSparkOnSpark (cognitive 30) · ×1
  • SparkAutoTask.runSparkOnSpark (cognitive 30) REDACTED:300 — SparkAutoTask.runSparkOnSpark has cognitive complexity 30 (threshold 15). Drivers by points: if/else 10 (18 pts), match/switch 4 (9 pts), boolean chains 3 (nesting depth added 13). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D2 · Cognitive Complexity · IngestionWorkflow.listStageFiles (cognitive 30) · ×1
  • IngestionWorkflow.listStageFiles (cognitive 30) src/main/scala/ai/starlake/workflow/IngestionWorkflow.scala:179 — IngestionWorkflow.listStageFiles has cognitive complexity 30 (threshold 15). Drivers by points: if/else 9 (18 pts), match/switch 3 (6 pts), loops 2 (4 pts), boolean chains 2 (nesting depth added 14). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D2 · Cognitive Complexity · WriteStrategy.checkValidity (cognitive 29) · ×1
  • WriteStrategy.checkValidity (cognitive 29) src/main/scala/ai/starlake/schema/model/WriteStrategy.scala:33 — WriteStrategy.checkValidity has cognitive complexity 29 (threshold 15). Drivers by points: if/else 14 (23 pts), match/switch 3 (4 pts), boolean chains 2 (nesting depth added 10). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D2 · Cognitive Complexity · PreLoadCmd.run (cognitive 28) · ×1
  • PreLoadCmd.run (cognitive 28) src/main/scala/ai/starlake/job/ingest/PreLoadCmd.scala:95 — PreLoadCmd.run has cognitive complexity 28 (threshold 15). Drivers by points: if/else 10 (16 pts), match/switch 4 (8 pts), boolean chains 2, loops 1 (2 pts) (nesting depth added 11). The drivers above price the dispatch low by construction — a dispatch is charged once however many cases it lists, while each branch inside an arm is charged in full — so most of this count is what the case bodies hold, and the arms are where it can be reduced. To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
D2 · Cognitive Complexity · JsonIngestionUtil.compareTypes (cognitive 26) · ×1
  • JsonIngestionUtil.compareTypes (cognitive 26) src/main/scala/org/apache/spark/sql/execution/datasources/json/JsonIngestionUtil.scala:63 — JsonIngestionUtil.compareTypes has cognitive complexity 26 (threshold 15). Drivers by points: if/else 9 (18 pts), loops 2 (5 pts), boolean chains 2, match/switch 1 (nesting depth added 12). The drivers above price the dispatch low by construction — a dispatch is charged once however many cases it lists, while each branch inside an arm is charged in full — so most of this count is what the case bodies hold, and the arms are where it can be reduced. To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
D2 · Cognitive Complexity · JdbcDbUtils.runDuckLakePreActions (cognitive 25) · ×1
  • JdbcDbUtils.runDuckLakePreActions (cognitive 25) REDACTED:187 — JdbcDbUtils.runDuckLakePreActions has cognitive complexity 25 (threshold 15). Drivers by points: if/else 12 (17 pts), match/switch 3 (5 pts), boolean chains 2, error handling 1 (nesting depth added 7). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D2 · Cognitive Complexity · SchemaHandler.taskOnly (cognitive 25) · ×1
  • SchemaHandler.taskOnly (cognitive 25) src/main/scala/ai/starlake/schema/handlers/SchemaHandler.scala:1559 — SchemaHandler.taskOnly has cognitive complexity 25 (threshold 15). Drivers by points: match/switch 4 (13 pts), loops 2 (7 pts), if/else 4 (5 pts) (nesting depth added 15). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
D2 · Cognitive Complexity · JdbcAutoTask.buildTableSchemaSQL (cognitive 24) · ×1
  • JdbcAutoTask.buildTableSchemaSQL (cognitive 24) src/main/scala/ai/starlake/job/transform/JdbcAutoTask.scala:502 — JdbcAutoTask.buildTableSchemaSQL has cognitive complexity 24 (threshold 15). Drivers by points: if/else 15 (23 pts), boolean chains 1 (nesting depth added 8). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D2 · Cognitive Complexity · DagGenerateJob.generateDomainDags (cognitive 24) · ×1
  • DagGenerateJob.generateDomainDags (cognitive 24) src/main/scala/ai/starlake/schema/generator/DagGenerateJob.scala:312 — DagGenerateJob.generateDomainDags has cognitive complexity 24 (threshold 15). Drivers by points: if/else 17 (24 pts) (nesting depth added 7). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D2 · Cognitive Complexity · SchemaHandler.checkDagsValidity (cognitive 24) · ×1
  • SchemaHandler.checkDagsValidity (cognitive 24) src/main/scala/ai/starlake/schema/handlers/SchemaHandler.scala:149 — SchemaHandler.checkDagsValidity has cognitive complexity 24 (threshold 15). Drivers by points: if/else 14 (19 pts), match/switch 3 (5 pts) (nesting depth added 7). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D2 · Cognitive Complexity · Bootstrap.bootstrap (cognitive 23) · ×1
  • Bootstrap.bootstrap (cognitive 23) src/main/scala/ai/starlake/job/bootstrap/Bootstrap.scala:80 — Bootstrap.bootstrap has cognitive complexity 23 (threshold 15). Drivers by points: if/else 11 (17 pts), boolean chains 3, error handling 1 (2 pts), match/switch 1 (nesting depth added 7). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D2 · Cognitive Complexity · InferSchemaJob.resolveRef (cognitive 23) · ×1
  • InferSchemaJob.resolveRef (cognitive 23) src/main/scala/ai/starlake/job/infer/InferSchemaJob.scala:498 — InferSchemaJob.resolveRef has cognitive complexity 23 (threshold 15). Drivers by points: if/else 12 (21 pts), boolean chains 2 (nesting depth added 9). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D2 · Cognitive Complexity · ColLineage.extractRelations (cognitive 23) · ×1
  • ColLineage.extractRelations (cognitive 23) src/main/scala/ai/starlake/lineage/ColLineage.scala:340 — ColLineage.extractRelations has cognitive complexity 23 (threshold 15). Drivers by points: if/else 13 (19 pts), boolean chains 4 (nesting depth added 6). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D2 · Cognitive Complexity · BigQuerySchemaConverters.convertByBigQueryType (cognitive 23) · ×1
  • BigQuerySchemaConverters.convertByBigQueryType (cognitive 23) src/main/java/ai/starlake/utils/repackaged/BigQuerySchemaConverters.java:168 — BigQuerySchemaConverters.convertByBigQueryType has cognitive complexity 23 (threshold 15). Drivers by points: if/else 12 (20 pts), boolean chains 3 (nesting depth added 8). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D2 · Cognitive Complexity · ExtractDataJob.extractTableData (cognitive 22) · ×1
  • ExtractDataJob.extractTableData (cognitive 22) src/main/scala/ai/starlake/extract/ExtractDataJob.scala:457 — ExtractDataJob.extractTableData has cognitive complexity 22 (threshold 15). Drivers by points: match/switch 9 (12 pts), if/else 9 (10 pts) (nesting depth added 4). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
D2 · Cognitive Complexity · RestAPIClient.execute (cognitive 22) · ×1
  • RestAPIClient.execute (cognitive 22) src/main/scala/ai/starlake/extract/impl/restapi/RestAPIClient.scala:65 — RestAPIClient.execute has cognitive complexity 22 (threshold 15). Drivers by points: if/else 10 (15 pts), error handling 2 (4 pts), boolean chains 2, loops 1 (nesting depth added 7). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D2 · Cognitive Complexity · RowValidator.prepareData (cognitive 22) · ×1
  • RowValidator.prepareData (cognitive 22) src/main/scala/ai/starlake/job/validator/RowValidator.scala:403 — RowValidator.prepareData has cognitive complexity 22 (threshold 15). Drivers by points: if/else 7 (8 pts), match/switch 5 (7 pts), boolean chains 4, loops 2 (3 pts) (nesting depth added 4). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D2 · Cognitive Complexity · DagDeployJob.deployDags (cognitive 22) · ×1
  • DagDeployJob.deployDags (cognitive 22) src/main/scala/ai/starlake/schema/generator/DagDeployJob.scala:34 — DagDeployJob.deployDags has cognitive complexity 22 (threshold 15). Drivers by points: if/else 11 (17 pts), boolean chains 3, match/switch 1 (2 pts) (nesting depth added 7). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D2 · Cognitive Complexity · ParseUtils.parse (cognitive 22) · ×1
  • ParseUtils.parse (cognitive 22) src/main/scala/ai/starlake/utils/ParseUtils.scala:12 — ParseUtils.parse has cognitive complexity 22 (threshold 15). Drivers by points: if/else 8 (11 pts), loops 3 (7 pts), boolean chains 4 (nesting depth added 7). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D2 · Cognitive Complexity · InferSchemaJob.inferPattern (cognitive 21) · ×1
  • InferSchemaJob.inferPattern (cognitive 21) src/main/scala/ai/starlake/job/infer/InferSchemaJob.scala:595 — InferSchemaJob.inferPattern has cognitive complexity 21 (threshold 15). Drivers by points: if/else 8 (12 pts), boolean chains 7, loops 1 (2 pts) (nesting depth added 5). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D2 · Cognitive Complexity · KafkaJob.pipeline (cognitive 21) · ×1
  • KafkaJob.pipeline (cognitive 21) src/main/scala/ai/starlake/job/sink/kafka/KafkaJob.scala:132 — KafkaJob.pipeline has cognitive complexity 21 (threshold 15). Drivers by points: if/else 9 (16 pts), match/switch 2 (4 pts), boolean chains 1 (nesting depth added 9). The drivers above price the dispatch low by construction — a dispatch is charged once however many cases it lists, while each branch inside an arm is charged in full — so most of this count is what the case bodies hold, and the arms are where it can be reduced. To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
D2 · Cognitive Complexity · AllSinks.checkValidity (cognitive 21) · ×1
  • AllSinks.checkValidity (cognitive 21) src/main/scala/ai/starlake/schema/model/Sink.scala:241 — AllSinks.checkValidity has cognitive complexity 21 (threshold 15). Drivers by points: if/else 10 (19 pts), match/switch 2 (nesting depth added 9). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D2 · Cognitive Complexity · DependencySync.reconcile (cognitive 21) · ×1
  • DependencySync.reconcile (cognitive 21) src/main/java/ai/starlake/setup/DependencySync.java:84 — DependencySync.reconcile has cognitive complexity 21 (threshold 15). Drivers by points: if/else 7 (12 pts), boolean chains 3, loops 3, ternaries 1 (3 pts) (nesting depth added 7). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D2 · Cognitive Complexity · ExtractSchema.extract (cognitive 20) · ×1
  • ExtractSchema.extract (cognitive 20) src/main/scala/ai/starlake/extract/ExtractSchema.scala:77 — ExtractSchema.extract has cognitive complexity 20 (threshold 15). Drivers by points: if/else 8 (15 pts), match/switch 3 (5 pts) (nesting depth added 9). The drivers above price the dispatch low by construction — a dispatch is charged once however many cases it lists, while each branch inside an arm is charged in full — so most of this count is what the case bodies hold, and the arms are where it can be reduced. To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
D2 · Cognitive Complexity · ExtractSchema.fromConfig (cognitive 20) · ×1
  • ExtractSchema.fromConfig (cognitive 20) src/main/scala/ai/starlake/extract/ExtractSchema.scala:184 — ExtractSchema.fromConfig has cognitive complexity 20 (threshold 15). Drivers by points: if/else 10 (14 pts), match/switch 2 (5 pts), boolean chains 1 (nesting depth added 7). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D2 · Cognitive Complexity · SchemaHandler.deserializedDomains (cognitive 20) · ×1
  • SchemaHandler.deserializedDomains (cognitive 20) src/main/scala/ai/starlake/schema/handlers/SchemaHandler.scala:678 — SchemaHandler.deserializedDomains has cognitive complexity 20 (threshold 15). Drivers by points: if/else 15 (20 pts) (nesting depth added 5). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D2 · Cognitive Complexity · SchemaHandler.loadExternals (cognitive 20) · ×1
  • SchemaHandler.loadExternals (cognitive 20) src/main/scala/ai/starlake/schema/handlers/SchemaHandler.scala:952 — SchemaHandler.loadExternals has cognitive complexity 20 (threshold 15). Drivers by points: match/switch 5 (13 pts), if/else 5 (7 pts) (nesting depth added 10). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
D2 · Cognitive Complexity · ExtractTableOrTransformMigrator.migrateExtractTable (cognitive 20) · ×1
  • ExtractTableOrTransformMigrator.migrateExtractTable (cognitive 20) src/main/scala/ai/starlake/utils/YamlMigrator.scala:428 — ExtractTableOrTransformMigrator.migrateExtractTable has cognitive complexity 20 (threshold 15). Drivers by points: match/switch 5 (15 pts), if/else 5 (nesting depth added 10). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
D2 · Cognitive Complexity · Setup.main (cognitive 20) · ×1
  • Setup.main (cognitive 20) REDACTED:1037 — Setup.main has cognitive complexity 20 (threshold 15). Drivers by points: if/else 10 (11 pts), boolean chains 6, loops 1 (2 pts), error handling 1 (nesting depth added 2). To reduce it, split the body: this score is breadth rather than depth — many checks laid out side by side rather than nested inside one another, so inverting conditions into early returns has nothing left to flatten. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.
D2 · Cognitive Complexity · ConnectionInfo.getJdbcEngineName (cognitive 19) · ×1
  • ConnectionInfo.getJdbcEngineName (cognitive 19) src/main/scala/ai/starlake/config/ConnectionInfo.scala:446 — ConnectionInfo.getJdbcEngineName has cognitive complexity 19 (threshold 15). Drivers by points: if/else 10 (16 pts), match/switch 2 (3 pts) (nesting depth added 7). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D2 · Cognitive Complexity · RestAPISchemaExtractor.inferAttribute (cognitive 19) · ×1
  • RestAPISchemaExtractor.inferAttribute (cognitive 19) src/main/scala/ai/starlake/extract/impl/restapi/RestAPISchemaExtractor.scala:187 — RestAPISchemaExtractor.inferAttribute has cognitive complexity 19 (threshold 15). Drivers by points: if/else 9 (16 pts), match/switch 1 (2 pts), boolean chains 1 (nesting depth added 8). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D2 · Cognitive Complexity · CliConfig.markdown (cognitive 19) · ×1
  • CliConfig.markdown (cognitive 19) src/main/scala/ai/starlake/utils/CliConfig.scala:27 — CliConfig.markdown has cognitive complexity 19 (threshold 15). Drivers by points: if/else 18 (19 pts) (nesting depth added 1). To reduce it, split the body: this score is breadth rather than depth — many checks laid out side by side rather than nested inside one another, so inverting conditions into early returns has nothing left to flatten. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.
D2 · Cognitive Complexity · Main.run (cognitive 18) · ×1
  • Main.run (cognitive 18) src/main/scala/ai/starlake/job/Main.scala:293 — Main.run has cognitive complexity 18 (threshold 15). Drivers by points: if/else 5 (12 pts), match/switch 2 (4 pts), boolean chains 2 (nesting depth added 9). The drivers above price the dispatch low by construction — a dispatch is charged once however many cases it lists, while each branch inside an arm is charged in full — so most of this count is what the case bodies hold, and the arms are where it can be reduced. To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
D2 · Cognitive Complexity · XlsDomainReader.readAttribute (cognitive 18) · ×1
  • XlsDomainReader.readAttribute (cognitive 18) src/main/scala/ai/starlake/schema/generator/XlsDomainReader.scala:569 — XlsDomainReader.readAttribute has cognitive complexity 18 (threshold 15). Drivers by points: if/else 10 (12 pts), match/switch 4 (6 pts) (nesting depth added 4). To reduce it, split the body: most of this score is breadth rather than depth — checks laid out side by side rather than stacked — so group the statements between the checks into named steps and move each step into its own function. Some of it IS depth: where a check sits inside another whose only job is to reach it, merge the two into one condition, and where an else follows a branch that already returns, drop the trailing else and let the rest of the body continue at one level.
D2 · Cognitive Complexity · SchemaInfo.checkValidity (cognitive 18) · ×1
  • SchemaInfo.checkValidity (cognitive 18) src/main/scala/ai/starlake/schema/model/SchemaInfo.scala:319 — SchemaInfo.checkValidity has cognitive complexity 18 (threshold 15). Drivers by points: if/else 9, boolean chains 5, loops 3, match/switch 1. To reduce it, split the body: this score is breadth rather than depth — many checks laid out side by side rather than nested inside one another, so inverting conditions into early returns has nothing left to flatten. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.
D2 · Cognitive Complexity · TableAttribute.checkValidity (cognitive 18) · ×1
  • TableAttribute.checkValidity (cognitive 18) src/main/scala/ai/starlake/schema/model/TableAttribute.scala:153 — TableAttribute.checkValidity has cognitive complexity 18 (threshold 15). Drivers by points: if/else 11 (13 pts), match/switch 3, boolean chains 2 (nesting depth added 2). To reduce it, split the body: most of this score is breadth rather than depth — checks laid out side by side rather than stacked — so group the statements between the checks into named steps and move each step into its own function. Some of it IS depth: where a check sits inside another whose only job is to reach it, merge the two into one condition, and where an else follows a branch that already returns, drop the trailing else and let the rest of the body continue at one level.
D2 · Cognitive Complexity · SemanticModelOps.assignModelMetrics (cognitive 18) · ×1
  • SemanticModelOps.assignModelMetrics (cognitive 18) src/main/scala/ai/starlake/semantic/SemanticModelOps.scala:49 — SemanticModelOps.assignModelMetrics has cognitive complexity 18 (threshold 15). Drivers by points: loops 3 (9 pts), if/else 4 (5 pts), match/switch 1 (4 pts) (nesting depth added 10). To reduce it, break up the iteration: give each loop body a named function, and split a multi-phase loop into one function per phase so no single body carries the whole pipeline.
D2 · Cognitive Complexity · SqlFormatter.format (cognitive 18) · ×1
  • SqlFormatter.format (cognitive 18) src/main/scala/ai/starlake/sql/SqlFormatter.scala:16 — SqlFormatter.format has cognitive complexity 18 (threshold 15). Drivers by points: if/else 10 (13 pts), boolean chains 3, match/switch 1 (2 pts) (nesting depth added 4). To reduce it, split the body: most of this score is breadth rather than depth — checks laid out side by side rather than stacked — so group the statements between the checks into named steps and move each step into its own function. Some of it IS depth: where a check sits inside another whose only job is to reach it, merge the two into one condition, and where an else follows a branch that already returns, drop the trailing else and let the rest of the body continue at one level.
D2 · Cognitive Complexity · JsonIngestionUtil.compatibleType (cognitive 18) · ×1
  • JsonIngestionUtil.compatibleType (cognitive 18) src/main/scala/org/apache/spark/sql/execution/datasources/json/JsonIngestionUtil.scala:196 — JsonIngestionUtil.compatibleType has cognitive complexity 18 (threshold 15). Drivers by points: if/else 6 (9 pts), loops 3 (6 pts), boolean chains 2, match/switch 1 (nesting depth added 6). The drivers above price the dispatch low by construction — a dispatch is charged once however many cases it lists, while each branch inside an arm is charged in full — so most of this count is what the case bodies hold, and the arms are where it can be reduced. To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
D2 · Cognitive Complexity · BigQuerySchemaConverters.getStandardDataType (cognitive 18) · ×1
  • BigQuerySchemaConverters.getStandardDataType (cognitive 18) src/main/java/ai/starlake/utils/repackaged/BigQuerySchemaConverters.java:357 — BigQuerySchemaConverters.getStandardDataType has cognitive complexity 18 (threshold 15). Drivers by points: if/else 17 (18 pts) (nesting depth added 1). To reduce it, split the body: this score is breadth rather than depth — many checks laid out side by side rather than nested inside one another, so inverting conditions into early returns has nothing left to flatten. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.
D2 · Cognitive Complexity · Setup.generateVersions (cognitive 18) · ×1
  • Setup.generateVersions (cognitive 18) REDACTED:637 — Setup.generateVersions has cognitive complexity 18 (threshold 15). Drivers by points: boolean chains 9, if/else 9. To reduce it, name the conditions: bind each compound test to a well-named local or a small predicate function, so the body reads as a sequence of named decisions rather than a chain of operators.
D2 · Cognitive Complexity · ExtractDataJob.extractData (cognitive 17) · ×1
  • ExtractDataJob.extractData (cognitive 17) src/main/scala/ai/starlake/extract/ExtractDataJob.scala:141 — ExtractDataJob.extractData has cognitive complexity 17 (threshold 15). Drivers by points: if/else 8 (11 pts), match/switch 3 (5 pts), boolean chains 1 (nesting depth added 5). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D2 · Cognitive Complexity · JdbcDbUtils.extractJDBCTables (cognitive 17) · ×1
  • JdbcDbUtils.extractJDBCTables (cognitive 17) REDACTED:832 — JdbcDbUtils.extractJDBCTables has cognitive complexity 17 (threshold 15). Drivers by points: if/else 7 (9 pts), boolean chains 4, match/switch 3 (4 pts) (nesting depth added 3). To reduce it, split the body: most of this score is breadth rather than depth — checks laid out side by side rather than stacked — so group the statements between the checks into named steps and move each step into its own function. Some of it IS depth: where a check sits inside another whose only job is to reach it, merge the two into one condition, and where an else follows a branch that already returns, drop the trailing else and let the rest of the body continue at one level.
D2 · Cognitive Complexity · FreshnessJob.freshness (cognitive 17) · ×1
  • FreshnessJob.freshness (cognitive 17) REDACTED:145 — FreshnessJob.freshness has cognitive complexity 17 (threshold 15). Drivers by points: match/switch 7 (14 pts), if/else 3 (nesting depth added 7). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
D2 · Cognitive Complexity · OpenAPISchemaExtractor.extractApiEssentialInformation (cognitive 17) · ×1
  • OpenAPISchemaExtractor.extractApiEssentialInformation (cognitive 17) src/main/scala/ai/starlake/extract/impl/openapi/OpenAPISchemaExtractor.scala:666 — OpenAPISchemaExtractor.extractApiEssentialInformation has cognitive complexity 17 (threshold 15). Drivers by points: if/else 3 (6 pts), loops 3 (6 pts), match/switch 1 (5 pts) (nesting depth added 10). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D2 · Cognitive Complexity · Main.runWithExitCode (cognitive 17) · ×1
  • Main.runWithExitCode (cognitive 17) src/main/scala/ai/starlake/job/Main.scala:208 — Main.runWithExitCode has cognitive complexity 17 (threshold 15). Drivers by points: if/else 8 (14 pts), match/switch 2, error handling 1 (nesting depth added 6). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D2 · Cognitive Complexity · DuckDbNativeLoader.runLoad (cognitive 17) · ×1
  • DuckDbNativeLoader.runLoad (cognitive 17) REDACTED:78 — DuckDbNativeLoader.runLoad has cognitive complexity 17 (threshold 15). Drivers by points: if/else 12 (13 pts), error handling 1 (2 pts), boolean chains 1, match/switch 1 (nesting depth added 2). To reduce it, split the body: this score is breadth rather than depth — many checks laid out side by side rather than nested inside one another, so inverting conditions into early returns has nothing left to flatten. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.
D2 · Cognitive Complexity · DuckDbUtils.createDuckDbColumn (cognitive 17) · ×1
  • DuckDbUtils.createDuckDbColumn (cognitive 17) src/main/scala/ai/starlake/utils/conversion/DuckDbUtils.scala:36 — DuckDbUtils.createDuckDbColumn has cognitive complexity 17 (threshold 15). Drivers by points: if/else 12 (15 pts), match/switch 2 (nesting depth added 3). To reduce it, split the body: most of this score is breadth rather than depth — checks laid out side by side rather than stacked — so group the statements between the checks into named steps and move each step into its own function. Some of it IS depth: where a check sits inside another whose only job is to reach it, merge the two into one condition, and where an else follows a branch that already returns, drop the trailing else and let the rest of the body continue at one level.
D2 · Cognitive Complexity · CaseClassToPojoConverter.convertCollectionElements (cognitive 16) · ×1
  • CaseClassToPojoConverter.convertCollectionElements (cognitive 16) src/main/scala/ai/starlake/core/utils/CaseClassToPojoConverter.scala:8 — CaseClassToPojoConverter.convertCollectionElements has cognitive complexity 16 (threshold 15). Drivers by points: match/switch 6 (11 pts), if/else 5 (nesting depth added 5). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
D2 · Cognitive Complexity · RestAPIClient.doRequest (cognitive 16) · ×1
  • RestAPIClient.doRequest (cognitive 16) src/main/scala/ai/starlake/extract/impl/restapi/RestAPIClient.scala:259 — RestAPIClient.doRequest has cognitive complexity 16 (threshold 15). Drivers by points: if/else 9, loops 2 (3 pts), match/switch 3, boolean chains 1 (nesting depth added 1). To reduce it, split the body: this score is breadth rather than depth — many checks laid out side by side rather than nested inside one another, so inverting conditions into early returns has nothing left to flatten. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.
D2 · Cognitive Complexity · SchemaHandler.attributesAsDiff (cognitive 16) · ×1
  • SchemaHandler.attributesAsDiff (cognitive 16) src/main/scala/ai/starlake/schema/handlers/SchemaHandler.scala:2270 — SchemaHandler.attributesAsDiff has cognitive complexity 16 (threshold 15). Drivers by points: match/switch 4 (11 pts), if/else 2 (5 pts) (nesting depth added 10). To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
D2 · Cognitive Complexity · DagInfo.checkValidity (cognitive 16) · ×1
  • DagInfo.checkValidity (cognitive 16) src/main/scala/ai/starlake/schema/model/DagInfo.scala:76 — DagInfo.checkValidity has cognitive complexity 16 (threshold 15). Drivers by points: if/else 12, boolean chains 4. To reduce it, split the body: this score is breadth rather than depth — many checks laid out side by side rather than nested inside one another, so inverting conditions into early returns has nothing left to flatten. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.
D2 · Cognitive Complexity · ProxySettings.parseProxy (cognitive 16) · ×1
  • ProxySettings.parseProxy (cognitive 16) src/main/scala/ai/starlake/utils/ProxySettings.scala:16 — ProxySettings.parseProxy has cognitive complexity 16 (threshold 15). Drivers by points: if/else 10 (16 pts) (nesting depth added 6). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D2 · Cognitive Complexity · TableFormatter.format (cognitive 16) · ×1
  • TableFormatter.format (cognitive 16) src/main/scala/ai/starlake/utils/TableFormatter.scala:6 — TableFormatter.format has cognitive complexity 16 (threshold 15). Drivers by points: loops 4 (9 pts), if/else 3 (6 pts), match/switch 1 (nesting depth added 8). The drivers above price the dispatch low by construction — a dispatch is charged once however many cases it lists, while each branch inside an arm is charged in full — so most of this count is what the case bodies hold, and the arms are where it can be reduced. To reduce it, keep the dispatch but shrink the arms: move each non-trivial case body into its own named function (or onto the value being matched) so the dispatch reads one line per case, and group related cases into a sub-dispatch. Keep every case explicit, and make the behaviour for cases you do not list a deliberate choice rather than an accident.
D2 · Cognitive Complexity · IngestionWorkflow.load (cognitive 16) · ×1
  • IngestionWorkflow.load (cognitive 16) src/main/scala/ai/starlake/workflow/IngestionWorkflow.scala:637 — IngestionWorkflow.load has cognitive complexity 16 (threshold 15). Drivers by points: if/else 6 (8 pts), loops 2 (5 pts), match/switch 1 (2 pts), boolean chains 1 (nesting depth added 6). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D2 · Cognitive Complexity · Setup.ensureWinutilsRuntime (cognitive 16) · ×1
  • Setup.ensureWinutilsRuntime (cognitive 16) REDACTED:171 — Setup.ensureWinutilsRuntime has cognitive complexity 16 (threshold 15). Drivers by points: if/else 5 (6 pts), loops 2 (4 pts), boolean chains 3, error handling 1 (3 pts) (nesting depth added 5). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D29 · Static Analysis (SAST) · REDACTED · ×1
  • REDACTED
D29 · Static Analysis (SAST) · REDACTED · ×1
  • REDACTED
D29 · Static Analysis (SAST) · REDACTED · ×1
  • REDACTED
D3 · God Classes · MethodTooLong · ×1
  • MethodTooLong: Yml2DDLJob.withDdl src/main/scala/ai/starlake/schema/generator/Yml2DDLJob.scala:63 — MethodTooLong — withDdl runs 205 significant lines (blank, comment-only and punctuation-only lines excluded) in one body. The bar is 100 significant lines; this is 105 over it, 2.05× the bar. This is length, not branching: a long straight-line body scores low on complexity and is still read whole to change any part of it, so the complexity numbers beside this row neither confirm nor excuse it. To reduce it, extract each cohesive step of the body — the runs of statements that work on the same values and would earn the same name — into its own named unit, and have this one call them in order.
D30 · Dependency Vulnerabilities · Medium CVE · ×1
  • REDACTED
D31 · IaC & Container Security · Medium IaC · ×1
  • REDACTED
D36 · Supply-chain Provenance & Signing · REDACTED · ×1
  • REDACTED
D36 · Supply-chain Provenance & Signing REDACTED · ×1
  • REDACTED
D4 · Code Duplication · Edited copy of a member (43 corresponding lines) · ×1
  • Edited copy of a member (43 corresponding lines) src/main/scala/ai/starlake/lineage/AclDependencies.scala:464 — src/main/scala/ai/starlake/lineage/AclDependencies.scala:464-532 | src/main/scala/ai/starlake/lineage/AclDependencies.scala:533-594 — These two members are one piece of code written twice and then edited apart: 43 consecutive lines correspond almost exactly, broken only by small local edits. Most of that correspondence is NOT reported as duplicated blocks below — the edits cut it into fragments and only the largest of them clear the block floor, so the rows below understate it. The repair is at the members' grain — factor the shared implementation into one the two call with their differences as parameters or as an injected step, or, where the difference is systematic (an extra return value, one transport against another), generate one from the other. Left alone, the next edit has to be made twice and the two will drift further apart.
D4 · Code Duplication · Edited copy of a member (20 corresponding lines) · ×1
  • Edited copy of a member (20 corresponding lines) src/main/scala/ai/starlake/schema/model/SchemaInfo.scala:508 — src/main/scala/ai/starlake/schema/model/SchemaInfo.scala:508-537 | src/main/scala/ai/starlake/schema/model/SchemaInfo.scala:543-566 — These two members are one piece of code written twice and then edited apart: 20 consecutive lines correspond almost exactly, broken only by small local edits. Most of that correspondence is NOT reported as duplicated blocks below — the edits cut it into fragments and only the largest of them clear the block floor, so the rows below understate it. The repair is at the members' grain — factor the shared implementation into one the two call with their differences as parameters or as an injected step, or, where the difference is systematic (an extra return value, one transport against another), generate one from the other. Left alone, the next edit has to be made twice and the two will drift further apart.
D4 · Code Duplication · Edited copy of a member (14 corresponding lines) · ×1
  • Edited copy of a member (14 corresponding lines) src/main/scala/ai/starlake/lineage/AclDependencies.scala:151 — src/main/scala/ai/starlake/lineage/AclDependencies.scala:151-179 | src/main/scala/ai/starlake/lineage/AclDependencies.scala:181-235 — These two members are one piece of code written twice and then edited apart: 14 consecutive lines correspond almost exactly, broken only by small local edits. Most of that correspondence is NOT reported as duplicated blocks below — the edits cut it into fragments and only the largest of them clear the block floor, so the rows below understate it. The repair is at the members' grain — factor the shared implementation into one the two call with their differences as parameters or as an injected step, or, where the difference is systematic (an extra return value, one transport against another), generate one from the other. Left alone, the next edit has to be made twice and the two will drift further apart.
D4 · Code Duplication · Members sharing a duplicated core (4 members, 50+ identical tokens) · ×1
  • Members sharing a duplicated core (4 members, 50+ identical tokens) src/main/scala/ai/starlake/job/transform/TransformContext.scala:253 — src/main/scala/ai/starlake/job/transform/TransformContext.scala:253-269 | src/main/scala/ai/starlake/job/transform/TransformContext.scala:286-302 | src/main/scala/ai/starlake/job/transform/TransformContext.scala:320-336 | src/main/scala/ai/starlake/job/transform/TransformContext.scala:348-362 — These 4 members share a duplicated core: a run of at least 50 identical tokens appears in every one of them. That run is NOT broken out as duplicated-block rows below — it is what admitted this row, and the blocks below cover only the part of it that clears the block floor, so they understate the correspondence. Read the members as one construct written 4 times. The repair is at the members' grain — factor the shared implementation out once and have all of them call it with their differences as parameters or as an injected step, or, where the difference is systematic, generate them from one template. Extracting the individual blocks below is not the same fix: it leaves every body in place and the next edit still has to be made 4 times.
D4 · Code Duplication · Duplicated block (42 lines × 2) · ×1
  • Duplicated block (42 lines × 2) src/main/scala/ai/starlake/lineage/AclDependencies.scala:603 — src/main/scala/ai/starlake/lineage/AclDependencies.scala:603-644 | src/main/scala/ai/starlake/lineage/AclDependencies.scala:674-715 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
D4 · Code Duplication · Duplicated block (41 lines × 2) · ×1
  • Duplicated block (41 lines × 2) src/main/scala/ai/starlake/lineage/AclDependencies.scala:465 — src/main/scala/ai/starlake/lineage/AclDependencies.scala:465-505 | src/main/scala/ai/starlake/lineage/AclDependencies.scala:534-574 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
D4 · Code Duplication · Duplicated block (35–37 lines × 2) · ×1
  • Duplicated block (35–37 lines × 2) src/main/scala/ai/starlake/lineage/AclDependencies.scala:59 — src/main/scala/ai/starlake/lineage/AclDependencies.scala:59-95 | src/main/scala/ai/starlake/lineage/AclDependencies.scala:114-148 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Read the line range as the matched WINDOW rather than a finished unit: at `src/main/scala/ai/starlake/lineage/AclDependencies.scala:59` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
D4 · Code Duplication · Duplicated block (31 lines × 2) · ×1
  • Duplicated block (31 lines × 2) src/main/scala/ai/starlake/job/validator/FlatRowValidator.scala:27 — src/main/scala/ai/starlake/job/validator/FlatRowValidator.scala:27-57 | src/main/scala/ai/starlake/job/validator/TreeRowValidator.scala:27-57 — before extracting anything, compare `src/main/scala/ai/starlake/job/validator/FlatRowValidator.scala` and `src/main/scala/ai/starlake/job/validator/TreeRowValidator.scala` as WHOLE FILES: 97% of the shorter file's lines also appear in the other, so this reads as one file having been copied from the other rather than as a helper waiting to be extracted. The 1 duplicated block(s) this scan matched between them are fragments of that copy, not the extent of it — treat the file pair as the unit. If that is what happened, the fix is to keep one copy and have the other call it (or delete it), which resolves this row and its siblings together — extracting one helper per block leaves the fork in place. Each matched range is the entire body of the declaration above it, so the region is already a complete unit: move that whole declaration to the shared location and have each site call it, rather than lifting the lines out of their bodies. Any `return` inside it is the body's own exit and keeps its meaning in the moved unit.
D4 · Code Duplication · Duplicated block (29 lines × 2) · ×1
  • Duplicated block (29 lines × 2) src/main/scala/ai/starlake/job/ingest/GenericIngestionJob.scala:252 — src/main/scala/ai/starlake/job/ingest/GenericIngestionJob.scala:252-280 | src/main/scala/ai/starlake/job/ingest/ParquetIngestionJob.scala:79-107 — before extracting anything, compare `src/main/scala/ai/starlake/job/ingest/GenericIngestionJob.scala` and `src/main/scala/ai/starlake/job/ingest/ParquetIngestionJob.scala` as WHOLE FILES: 91% of the shorter file's lines also appear in the other, so this reads as one file having been copied from the other rather than as a helper waiting to be extracted. The 1 duplicated block(s) this scan matched between them are fragments of that copy, not the extent of it — treat the file pair as the unit. If that is what happened, the fix is to keep one copy and have the other call it (or delete it), which resolves this row and its siblings together — extracting one helper per block leaves the fork in place. Read the line range as the matched WINDOW rather than a finished unit: at `src/main/scala/ai/starlake/job/ingest/GenericIngestionJob.scala:252` it runs out through the closing brace of the declaration holding it — the window is that declaration's tail, not a fragment that begins part-way through something, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
D4 · Code Duplication · Duplicated block (25–27 lines × 2) · ×1
  • Duplicated block (25–27 lines × 2) src/main/scala/ai/starlake/schema/generator/DagGenerateJob.scala:488 — src/main/scala/ai/starlake/schema/generator/DagGenerateJob.scala:488-514 | src/main/scala/ai/starlake/schema/generator/DagGenerateJob.scala:518-542 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Read the line range as the matched WINDOW rather than a finished unit: at `src/main/scala/ai/starlake/schema/generator/DagGenerateJob.scala:488` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
D4 · Code Duplication · Duplicated block (23–24 lines × 2) · ×1
  • Duplicated block (23–24 lines × 2) src/main/scala/ai/starlake/job/ingest/loaders/NativeLoader.scala:270 — src/main/scala/ai/starlake/job/ingest/loaders/NativeLoader.scala:270-292 | src/main/scala/ai/starlake/job/ingest/loaders/SnowflakeNativeLoader.scala:76-99 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from both call sites, so a change lands once. Read the line range as the matched WINDOW rather than a finished unit: at `src/main/scala/ai/starlake/job/ingest/loaders/NativeLoader.scala:270` it does not close everything it opens, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
D4 · Code Duplication · Duplicated block (22 lines × 2) · ×1
  • Duplicated block (22 lines × 2) src/main/scala/ai/starlake/schema/model/AutoTaskInfo.scala:143 — src/main/scala/ai/starlake/schema/model/AutoTaskInfo.scala:143-164 | src/main/scala/ai/starlake/schema/model/Metadata.scala:134-155 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from both call sites, so a change lands once. Read the line range as the matched WINDOW rather than a finished unit: at `src/main/scala/ai/starlake/schema/model/AutoTaskInfo.scala:143` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
D4 · Code Duplication · Duplicated block (19–21 lines × 2) · ×1
  • Duplicated block (19–21 lines × 2) src/main/scala/ai/starlake/schema/handlers/InferSchemaHandler.scala:89 — src/main/scala/ai/starlake/schema/handlers/InferSchemaHandler.scala:89-107 | src/main/scala/ai/starlake/schema/handlers/InferSchemaHandler.scala:111-131 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Read the line range as the matched WINDOW rather than a finished unit: at `src/main/scala/ai/starlake/schema/handlers/InferSchemaHandler.scala:89` it does not close everything it opens, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
D4 · Code Duplication · Duplicated block (19–20 lines × 2) · ×1
  • Duplicated block (19–20 lines × 2) src/main/scala/ai/starlake/lineage/AclDependencies.scala:266 — src/main/scala/ai/starlake/lineage/AclDependencies.scala:266-284 | src/main/scala/ai/starlake/lineage/AclDependencies.scala:315-334 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Note that the copies do not run to the end of the range shown: their LAST lines are different code, not the same code under different names — the matched region ends inside that line. Extract the lines above it, and read the last line of each site separately.
D4 · Code Duplication · Duplicated block (11–16 lines × 2) · ×1
  • Duplicated block (11–16 lines × 2) src/main/scala/ai/starlake/config/SparkEnv.scala:96 — src/main/scala/ai/starlake/config/SparkEnv.scala:96-106 | src/main/scala/ai/starlake/config/SparkEnv.scala:115-130 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Note that the copies do not run to the end of the range shown: their LAST lines are different code, not the same code under different names — the matched region ends inside that line. Extract the lines above it, and read the last line of each site separately.
D4 · Code Duplication · Duplicated block (16 lines × 2) · ×1
  • Duplicated block (16 lines × 2) src/main/scala/ai/starlake/schema/generator/DagGenerateJob.scala:502 — src/main/scala/ai/starlake/schema/generator/DagGenerateJob.scala:502-517 | src/main/scala/ai/starlake/schema/generator/DagGenerateJob.scala:589-604 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Read the line range as the matched WINDOW rather than a finished unit: at `src/main/scala/ai/starlake/schema/generator/DagGenerateJob.scala:502` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
D4 · Code Duplication · Duplicated block (15–16 lines × 2) · ×1
  • Duplicated block (15–16 lines × 2) src/main/scala/ai/starlake/schema/handlers/SchemaHandler.scala:1847 — src/main/scala/ai/starlake/schema/handlers/SchemaHandler.scala:1847-1862 | src/main/scala/ai/starlake/schema/handlers/SchemaHandler.scala:1871-1885 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Read the line range as the matched WINDOW rather than a finished unit: at `src/main/scala/ai/starlake/schema/handlers/SchemaHandler.scala:1847` it runs out through the closing brace of the declaration holding it and carries on into the declaration that follows — the window is the tail of one member plus the head of the next, so no call can be substituted for those exact lines, and the smallest declaration that contains all of them is the type they sit in. The repeated unit is the member each site sits in: where those members' bodies are the same, move one whole member to the shared location and have the others delegate to it; where the copies are a run of near-identical overloads or wrappers that differ only in their signatures, the repetition IS the run — a one-line delegation has no helper inside it to lift — so generate the run from the set it enumerates, or accept it and keep each member's own documentation with it. Note that the copies do not run to the end of the range shown: their LAST lines are different code, not the same code under different names — the matched region ends inside that line. Extract the lines above it, and read the last line of each site separately.
D4 · Code Duplication · Duplicated block (13–15 lines × 2) · ×1
  • Duplicated block (13–15 lines × 2) src/main/scala/ai/starlake/job/site/SiteHandler.scala:210 — src/main/scala/ai/starlake/job/site/SiteHandler.scala:210-222 | src/main/scala/ai/starlake/job/site/SiteHandler.scala:309-323 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Read the line range as the matched WINDOW rather than a finished unit: at `src/main/scala/ai/starlake/job/site/SiteHandler.scala:210` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
D4 · Code Duplication · Duplicated block (12–13 lines × 3) · ×1
  • Duplicated block (12–13 lines × 3) REDACTED:184 — REDACTED:184-196 | src/main/scala/ai/starlake/job/ingest/loaders/NativeLoader.scala:284-295 | src/main/scala/ai/starlake/job/ingest/loaders/SnowflakeNativeLoader.scala:90-102 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from all 3 call sites, so a change lands once. Read the line range as the matched WINDOW rather than a finished unit: at `REDACTED:184` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
D4 · Code Duplication · Duplicated block (11–13 lines × 3) · ×1
  • Duplicated block (11–13 lines × 3) REDACTED:563 — REDACTED:563-575 | src/main/scala/ai/starlake/job/transform/BigQueryAutoTask.scala:189-200 | REDACTED:421-431 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from all 3 call sites, so a change lands once.
D4 · Code Duplication · Duplicated block (12 lines × 4) · ×1
  • Duplicated block (12 lines × 4) src/main/scala/ai/starlake/schema/AdaptiveWriteStrategy.scala:108 — src/main/scala/ai/starlake/schema/AdaptiveWriteStrategy.scala:108-119 | src/main/scala/ai/starlake/schema/AdaptiveWriteStrategy.scala:124-135 | src/main/scala/ai/starlake/schema/AdaptiveWriteStrategy.scala:142-153 | src/main/scala/ai/starlake/schema/AdaptiveWriteStrategy.scala:158-169 — all 4 copies are in the same file, so extract the block into one function there and call it from every one of those sites — resolving only two of them leaves the rest to drift apart the first time one is edited. Read the line range as the matched WINDOW rather than a finished unit: at `src/main/scala/ai/starlake/schema/AdaptiveWriteStrategy.scala:108` it runs out through the closing brace of the declaration holding it and carries on into the declaration that follows — the window is the tail of one member plus the head of the next, so no call can be substituted for those exact lines, and the smallest declaration that contains all of them is the type they sit in. The repeated unit is the member each site sits in: where those members' bodies are the same, move one whole member to the shared location and have the others delegate to it; where the copies are a run of near-identical overloads or wrappers that differ only in their signatures, the repetition IS the run — a one-line delegation has no helper inside it to lift — so generate the run from the set it enumerates, or accept it and keep each member's own documentation with it.
D4 · Code Duplication · Duplicated block (11–12 lines × 3) · ×1
  • Duplicated block (11–12 lines × 3) src/main/scala/ai/starlake/job/sink/jdbc/SparkJdbcWriter.scala:136 — src/main/scala/ai/starlake/job/sink/jdbc/SparkJdbcWriter.scala:136-147 | src/main/scala/ai/starlake/job/transform/JdbcAutoTask.scala:287-297 | REDACTED:885-895 — the copies span different directories, so extracting a shared function means choosing where it lives: put it somewhere all 3 call sites can already reach — a location they all depend on today, or a new shared one if there is none — and call it from each site; until then, every change has to be made 3 times. ★ These copies have DRIFTED, and that is worth reading before extracting anything: just after the matched lines, `src/main/scala/ai/starlake/job/transform/JdbcAutoTask.scala:298` calls `isDuckDb` and `src/main/scala/ai/starlake/job/sink/jdbc/SparkJdbcWriter.scala:148` does not — after which the two agree again for 4 more lines. One of those two behaviours is the intended one and the other is what a copy-paste left behind, so decide which BEFORE unifying them: extracting the shared part will silently settle it, and if the copy that skips the call is the wrong one, that bug is already live.
D4 · Code Duplication · Duplicated block (10 lines × 4) · ×1
  • Duplicated block (10 lines × 4) src/main/scala/ai/starlake/job/transform/TransformContext.scala:255 — src/main/scala/ai/starlake/job/transform/TransformContext.scala:255-264 | src/main/scala/ai/starlake/job/transform/TransformContext.scala:288-297 | src/main/scala/ai/starlake/job/transform/TransformContext.scala:322-331 | src/main/scala/ai/starlake/job/transform/TransformContext.scala:350-359 — all 4 copies are in the same file, so extract the block into one function there and call it from every one of those sites — resolving only two of them leaves the rest to drift apart the first time one is edited.
D4 · Code Duplication · Duplicated block (7 lines × 2) · ×1
  • Duplicated block (7 lines × 2) src/main/scala/ai/starlake/config/SparkSessionBuilder.scala:50 — src/main/scala/ai/starlake/config/SparkSessionBuilder.scala:50-56 | src/main/scala/ai/starlake/schema/handlers/HdfsStorageHandler.scala:146-152 — the copies span different directories, so extracting a shared function means choosing where it lives: put it somewhere both call sites can already reach — a location they all depend on today, or a new shared one if there is none — and call it from each site; until then, every change has to be made twice. Read the line range as the matched WINDOW rather than a finished unit: at `src/main/scala/ai/starlake/config/SparkSessionBuilder.scala:50` it does not close everything it opens, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
D4 · Code Duplication · Duplicated block (5 lines × 4) · ×1
  • Duplicated block (5 lines × 4) src/main/scala/ai/starlake/schema/generator/DagGenerateJob.scala:492 — src/main/scala/ai/starlake/schema/generator/DagGenerateJob.scala:492-499 | src/main/scala/ai/starlake/schema/generator/DagGenerateJob.scala:522-526 | src/main/scala/ai/starlake/schema/generator/DagGenerateJob.scala:580-586 | src/main/scala/ai/starlake/schema/generator/DagGenerateJob.scala:605-609 — all 4 copies are in the same file, so extract the block into one function there and call it from every one of those sites — resolving only two of them leaves the rest to drift apart the first time one is edited. Read the line range as the matched WINDOW rather than a finished unit: at `src/main/scala/ai/starlake/schema/generator/DagGenerateJob.scala:492` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
D4 · Code Duplication · Duplicated block (5 lines × 3) · ×1
  • Duplicated block (5 lines × 3) src/main/scala/ai/starlake/schema/model/SchemaInfo.scala:511 — src/main/scala/ai/starlake/schema/model/SchemaInfo.scala:511-515 | src/main/scala/ai/starlake/schema/model/SchemaInfo.scala:546-550 | src/main/scala/ai/starlake/schema/model/SchemaInfo.scala:573-580 — all 3 copies are in the same file, so extract the block into one function there and call it from every one of those sites — resolving only two of them leaves the rest to drift apart the first time one is edited. Read the line range as the matched WINDOW rather than a finished unit: at `src/main/scala/ai/starlake/schema/model/SchemaInfo.scala:511` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that. Note that the copies do not run to the end of the range shown: their LAST lines are different code, not the same code under different names — the matched region ends inside that line. Extract the lines above it, and read the last line of each site separately.
D4 · Code Duplication · Duplicated block (7–22 lines × 2) · ×1
  • Duplicated block (7–22 lines × 2) REDACTED:923 — REDACTED:923-929 | REDACTED:986-1007 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Read the line range as the matched WINDOW rather than a finished unit: at `REDACTED:923` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that. The matched lines also transfer control out of the body holding them, which cannot survive a move into a called unit unchanged: have the extracted unit return that decision and let each site act on it. Note that the copies do not run to the end of the range shown: their LAST lines are different code, not the same code under different names — the matched region ends inside that line. Extract the lines above it, and read the last line of each site separately.
Minor — 52 finding(s)
IC1 · Incompleteness & stubs · Blanket warning suppression · ×11
  • Blanket warning suppression src/main/scala/ai/starlake/config/ConnectionInfo.scala:406 — A bare `// scalastyle:off`, `// scalafix:off` or `@nowarn` (or Wartremover's `warts.All`) switches every rule or warning off — hidden debt. Suppress the specific rule on the specific line, with a justification, instead.
  • Blanket warning suppression src/main/scala/ai/starlake/extract/BigQueryTableInfo.scala:196 — A bare `// scalastyle:off`, `// scalafix:off` or `@nowarn` (or Wartremover's `warts.All`) switches every rule or warning off — hidden debt. Suppress the specific rule on the specific line, with a justification, instead.
  • Blanket warning suppression src/main/scala/ai/starlake/extract/ExtractDataJob.scala:29 — A bare `// scalastyle:off`, `// scalafix:off` or `@nowarn` (or Wartremover's `warts.All`) switches every rule or warning off — hidden debt. Suppress the specific rule on the specific line, with a justification, instead.
  • Blanket warning suppression src/main/scala/ai/starlake/extract/ExtractSchema.scala:25 — A bare `// scalastyle:off`, `// scalafix:off` or `@nowarn` (or Wartremover's `warts.All`) switches every rule or warning off — hidden debt. Suppress the specific rule on the specific line, with a justification, instead.
  • Blanket warning suppression REDACTED:381 — A bare `// scalastyle:off`, `// scalafix:off` or `@nowarn` (or Wartremover's `warts.All`) switches every rule or warning off — hidden debt. Suppress the specific rule on the specific line, with a justification, instead.
  • Blanket warning suppression src/main/scala/ai/starlake/job/Main.scala:91 — A bare `// scalastyle:off`, `// scalafix:off` or `@nowarn` (or Wartremover's `warts.All`) switches every rule or warning off — hidden debt. Suppress the specific rule on the specific line, with a justification, instead.
  • Blanket warning suppression src/main/scala/ai/starlake/job/sink/bigquery/BigQueryJobBase.scala:1053 — A bare `// scalastyle:off`, `// scalafix:off` or `@nowarn` (or Wartremover's `warts.All`) switches every rule or warning off — hidden debt. Suppress the specific rule on the specific line, with a justification, instead.
  • Blanket warning suppression src/main/scala/ai/starlake/schema/model/DomainInfo.scala:86 — A bare `// scalastyle:off`, `// scalafix:off` or `@nowarn` (or Wartremover's `warts.All`) switches every rule or warning off — hidden debt. Suppress the specific rule on the specific line, with a justification, instead.
  • Blanket warning suppression src/main/scala/ai/starlake/utils/StarlakeObjectMapper.scala:19 — A bare `// scalastyle:off`, `// scalafix:off` or `@nowarn` (or Wartremover's `warts.All`) switches every rule or warning off — hidden debt. Suppress the specific rule on the specific line, with a justification, instead.
  • Blanket warning suppression src/main/scala/ai/starlake/utils/Unpacker.scala:85 — A bare `// scalastyle:off`, `// scalafix:off` or `@nowarn` (or Wartremover's `warts.All`) switches every rule or warning off — hidden debt. Suppress the specific rule on the specific line, with a justification, instead.
  • Blanket warning suppression src/main/scala/ai/starlake/workflow/IngestionWorkflow.scala:344 — A bare `// scalastyle:off`, `// scalafix:off` or `@nowarn` (or Wartremover's `warts.All`) switches every rule or warning off — hidden debt. Suppress the specific rule on the specific line, with a justification, instead.
D34 · Knowledge Freshness · Orphaned file · ×9
  • Orphaned file src/main/scala/ai/starlake/core/utils/LoadConfigMelder.scala:1 — No one still active in this repository holds living knowledge of this file: everyone who made a meaningful change to it has gone quiet, or what the current team did to it (moves, reformatting, log-level or import edits, renames) never needed understanding it. If it breaks, no one currently understands it. Attach the read to the next change that touches it: have a second person review that change, and leave behind a short comment or test recording what the file is for.
  • Orphaned file src/main/scala/ai/starlake/schema/AdaptiveWriteStrategy.scala:1 — No one still active in this repository holds living knowledge of this file: everyone who made a meaningful change to it has gone quiet, or what the current team did to it (moves, reformatting, log-level or import edits, renames) never needed understanding it. If it breaks, no one currently understands it. Attach the read to the next change that touches it: have a second person review that change, and leave behind a short comment or test recording what the file is for.
  • Orphaned file src/main/scala/ai/starlake/migration/ProjectMigrator.scala:1 — No one still active in this repository holds living knowledge of this file: everyone who made a meaningful change to it has gone quiet, or what the current team did to it (moves, reformatting, log-level or import edits, renames) never needed understanding it. If it breaks, no one currently understands it. Attach the read to the next change that touches it: have a second person review that change, and leave behind a short comment or test recording what the file is for.
  • Orphaned file src/main/scala/ai/starlake/extract/impl/openapi/OpenAPIModel.scala:1 — No one still active in this repository holds living knowledge of this file: everyone who made a meaningful change to it has gone quiet, or what the current team did to it (moves, reformatting, log-level or import edits, renames) never needed understanding it. If it breaks, no one currently understands it. Attach the read to the next change that touches it: have a second person review that change, and leave behind a short comment or test recording what the file is for.
  • Orphaned file src/main/scala/ai/starlake/extract/ParUtils.scala:1 — No one still active in this repository holds living knowledge of this file: everyone who made a meaningful change to it has gone quiet, or what the current team did to it (moves, reformatting, log-level or import edits, renames) never needed understanding it. If it breaks, no one currently understands it. Attach the read to the next change that touches it: have a second person review that change, and leave behind a short comment or test recording what the file is for.
  • Orphaned file src/main/scala/ai/starlake/extract/impl/openapi/OpenAPISchemaExtractorConfig.scala:1 — No one still active in this repository holds living knowledge of this file: everyone who made a meaningful change to it has gone quiet, or what the current team did to it (moves, reformatting, log-level or import edits, renames) never needed understanding it. If it breaks, no one currently understands it. Attach the read to the next change that touches it: have a second person review that change, and leave behind a short comment or test recording what the file is for.
  • Orphaned file src/main/scala/ai/starlake/extract/SLResultSetHelperService.scala:1 — No one still active in this repository holds living knowledge of this file: everyone who made a meaningful change to it has gone quiet, or what the current team did to it (moves, reformatting, log-level or import edits, renames) never needed understanding it. If it breaks, no one currently understands it. Attach the read to the next change that touches it: have a second person review that change, and leave behind a short comment or test recording what the file is for.
  • Orphaned file src/main/scala/ai/starlake/extract/spi/SchemaExtractorWorkflow.scala:1 — No one still active in this repository holds living knowledge of this file: everyone who made a meaningful change to it has gone quiet, or what the current team did to it (moves, reformatting, log-level or import edits, renames) never needed understanding it. If it breaks, no one currently understands it. Attach the read to the next change that touches it: have a second person review that change, and leave behind a short comment or test recording what the file is for.
  • Orphaned file src/main/scala/ai/starlake/core/utils/CaseClassToPojoConverter.scala:1 — No one still active in this repository holds living knowledge of this file: everyone who made a meaningful change to it has gone quiet, or what the current team did to it (moves, reformatting, log-level or import edits, renames) never needed understanding it. If it breaks, no one currently understands it. Attach the read to the next change that touches it: have a second person review that change, and leave behind a short comment or test recording what the file is for.
IC1 · Incompleteness & stubs · Disabled test · ×7
  • Disabled test src/test/scala/ai/starlake/extract/ExtractDataJobSpec.scala:377 — A test is skipped/ignored — coverage that looks present but never runs. Re-enable it or delete it so the green run means something.
  • Disabled test src/test/scala/ai/starlake/integration/freshness/FreshnessSnowflakeIntegrationSpec.scala:7 — A test is skipped/ignored — coverage that looks present but never runs. Re-enable it or delete it so the green run means something.
  • Disabled test src/test/scala/ai/starlake/integration/load/LoadShardBQIntegrationSpec.scala:10 — A test is skipped/ignored — coverage that looks present but never runs. Re-enable it or delete it so the green run means something.
  • Disabled test src/test/scala/ai/starlake/integration/load/LoadSnowflakeIntegrationSpec.scala:7 — A test is skipped/ignored — coverage that looks present but never runs. Re-enable it or delete it so the green run means something.
  • Disabled test src/test/scala/ai/starlake/integration/transform/TransformIntegrationSnowflakeSpec.scala:8 — A test is skipped/ignored — coverage that looks present but never runs. Re-enable it or delete it so the green run means something.
  • Disabled test src/test/scala/ai/starlake/job/strategies/SnowflakeTransformStrategiesBuilderSpec.scala:5 — A test is skipped/ignored — coverage that looks present but never runs. Re-enable it or delete it so the green run means something.
  • Disabled test src/test/scala/ai/starlake/schema/generator/Yml2DagTemplateLoaderSpec.scala:8 — A test is skipped/ignored — coverage that looks present but never runs. Re-enable it or delete it so the green run means something.
X10 · Duplicated predicate · Duplicated predicate · ×6
  • Duplicated predicate src/main/scala/ai/starlake/job/transform/JdbcAutoTask.scala:116 — `!exists && taskDesc._auditTableName.isDefined` appears character-identically in 2 files — src/main/scala/ai/starlake/job/transform/JdbcAutoTask.scala, REDACTED. It is one line, so the duplication detector's token window never sees it; the copies drift when only one is corrected. Give the condition a name and one home.
  • Duplicated predicate src/main/scala/ai/starlake/job/sink/bigquery/BigQueryJobBase.scala:138 — `forceApply || settings.appConfig.accessPolicies.apply` appears character-identically in 2 files — src/main/scala/ai/starlake/job/sink/bigquery/BigQueryJobBase.scala, src/main/scala/ai/starlake/schema/model/AccessControlEntry.scala. It is one line, so the duplication detector's token window never sees it; the copies drift when only one is corrected. Give the condition a name and one home.
  • Duplicated predicate src/main/scala/ai/starlake/schema/generator/XlsAutoJobReader.scala:169 — `freshness.warn.isEmpty && freshness.error.isEmpty` appears character-identically in 2 files — src/main/scala/ai/starlake/schema/generator/XlsAutoJobReader.scala, src/main/scala/ai/starlake/schema/generator/XlsDomainReader.scala. It is one line, so the duplication detector's token window never sees it; the copies drift when only one is corrected. Give the condition a name and one home.
  • Duplicated predicate src/main/scala/ai/starlake/extract/impl/restapi/RestAPIDataExtractor.scala:307 — `node == null || node.isNull || node.isMissingNode` appears character-identically in 2 files — src/main/scala/ai/starlake/extract/impl/restapi/RestAPIDataExtractor.scala, src/main/scala/ai/starlake/extract/impl/restapi/RestAPISchemaExtractor.scala. It is one line, so the duplication detector's token window never sees it; the copies drift when only one is corrected. Give the condition a name and one home.
  • Duplicated predicate src/main/scala/ai/starlake/schema/model/AutoTaskInfo.scala:149 — `schedule.contains(" ") || settings.appConfig.schedulePresets.contains(schedule)` appears character-identically in 2 files — src/main/scala/ai/starlake/schema/model/AutoTaskInfo.scala, src/main/scala/ai/starlake/schema/model/Metadata.scala. It is one line, so the duplication detector's token window never sees it; the copies drift when only one is corrected. Give the condition a name and one home.
  • Duplicated predicate src/main/scala/ai/starlake/job/transform/BigQueryAutoTask.scala:561 — `settings.appConfig.expectations.active && !taskDesc.isAuditTable()` appears character-identically in 3 files — src/main/scala/ai/starlake/job/transform/BigQueryAutoTask.scala, src/main/scala/ai/starlake/job/transform/JdbcAutoTask.scala, REDACTED. It is one line, so the duplication detector's token window never sees it; the copies drift when only one is corrected. Give the condition a name and one home.
D29 · Static Analysis (SAST) · REDACTED · ×2
  • REDACTED
  • REDACTED
AX6 · Interface segregation · Fat interface · ×1
  • Fat interface: StorageHandler (28 abstract members) src/main/scala/ai/starlake/schema/handlers/StorageHandler.scala:38 — `StorageHandler` declares 28 abstract members: `move`, `copy`, `delete`, `exists`, `mkdirs`, `copyFromLocal`, `copyToLocal`, `moveFromLocal`, `read`, `readAndExecute`, `readAndExecuteIS`, `write`, `writeBinary`, `listDirectories`, `list`, `stat`, `blockSize`, `lastModified`, `spaceConsumed`, `touchz`, `touch`, `lockAcquisitionPollTime`, `lockRefreshPollTime`, `getScheme`, `copyMerge`, `open`, `output`, `initFS`. Counted over its ABSTRACT members — a `def` with no body or a `val`/`var` with no initializer, once per signature; concrete members, private and protected members, and members of a companion `object` are not counted, because no implementer is forced to write them. A wide interface forces every implementer and caller to depend on methods they don't use (the Interface-Segregation 'I' in SOLID). Split it into focused role-interfaces.
D16 · Bus Factor · Off-boarding risk · ×1
  • Off-boarding risk: anonymized user #1 — If anonymized user #1 becomes unavailable, 135 significant file(s) lose their only recent owner: src/main/scala/ai/starlake/schema/handlers/SchemaHandler.scala, REDACTED, src/main/scala/ai/starlake/config/Settings.scala, REDACTED, REDACTED, REDACTED, src/main/java/ai/starlake/utils/repackaged/BigQuerySchemaConverters.java, src/main/scala/ai/starlake/lineage/AclDependencies.scala (+127 more). Pair on, review, or document these before any departure.
D16 · Bus Factor · Further sole-owners (lower concentration) · ×1
  • Further sole-owners (lower concentration) — 1 other contributor(s) are each the sole owner of a small amount of code below the off-boarding threshold — folded into the bus-factor score and metrics (137 single-owned of 225 analysed files in total, counted over production source files of roughly 2,400 bytes or more, excluding vendored, generated and example/demo trees and test files identified by path convention, largest first; 225 of the 387 production source files in this repository met that bar). They are anonymized user #2 (2 file(s)) — spread or document their files in the same way, at lower priority than the named off-boarding risks above. The files folded here: src/main/scala/ai/starlake/schema/generator/XlsModel.scala, src/main/scala/ai/starlake/schema/generator/XlsPolicyReader.scala.
D20 · ADR Quality · No ADRs found · ×1
  • No ADRs found — No ADRs found. No recognised ADR directory (`docs/adr/`, `docs/decisions/`, `adr/`, `docs/rfcs/`, an `ADR0001/` folder, or their siblings) exists anywhere in this tree. What was searched, so you can tell an empty log from a search that missed one: every directory under the tree (build output, dependencies and VCS metadata excepted), for a document that is either any non-index page inside a recognised ADR directory, whatever its name and however deeply nested (`docs/adr/use-postgres.md`, `docs/adr/2024/0001-x.md`); or a file anywhere whose name is ADR-shaped (`0001-use-postgres.md`, `adr-012-caching.md`); or, when neither turned anything up, a document carrying the decision-record signature (an "Architecture Decision Record" heading, or Status / Context / Decision / Consequences as section headings). A decision log that clears none of these — unnumbered files outside any recognised directory, without those headings — is not seen by this check and this row is then wrong. If that is your case, say so rather than renaming anything; otherwise, consider recording architectural decisions in `docs/adr/`.
D26 · Project Cohesion · Projects may be oversized for their cohesion · ×1
  • Projects may be oversized for their cohesion — 1 of 1 project(s) overshoot their size bounds, lowering Project Cohesion to 0.0/10. The most over is `(repository root)` (65450 LoC, 986 module-visible types across 52 directories). Review these for cohesion — draw the boundary inside the module first (group each responsibility into its own package or directory and keep the cross-boundary members non-public), since splitting a published package moves types between packages and breaks consumers.
D28 · Secrets (history) · Rotate the exposed credentials · ×1
  • REDACTED
D34 · Knowledge Freshness · Further orphaned files (smaller) · ×1
  • Further orphaned files (smaller) — 9 smaller file(s) also have no living knowledge — folded into the freshness score and metrics rather than raised one row each — most significant first: src/main/scala/ai/starlake/core/utils/LoadConfigMelder.scala, src/main/scala/ai/starlake/schema/AdaptiveWriteStrategy.scala, src/main/scala/ai/starlake/migration/ProjectMigrator.scala, src/main/scala/ai/starlake/extract/impl/openapi/OpenAPIModel.scala, src/main/scala/ai/starlake/extract/ParUtils.scala, src/main/scala/ai/starlake/extract/impl/openapi/OpenAPISchemaExtractorConfig.scala, src/main/scala/ai/starlake/extract/SLResultSetHelperService.scala, src/main/scala/ai/starlake/extract/spi/SchemaExtractorWorkflow.scala (and 1 more) (10 orphaned of 225 analysed files in total, counted over production source files of roughly 2,400 bytes or more, excluding vendored, generated and example/demo trees and test files identified by path convention, largest first; 225 of the 387 production source files in this repository met that bar). Attach the read to the next change that touches one of them: have a second person review that change, and leave behind a short comment or test recording what the file is for, so the knowledge comes back at the cost of a change you were making anyway.
D36 · Supply-chain Provenance & Signing · REDACTED · ×1
  • REDACTED
IC1 · Incompleteness & stubs · Empty method body · ×1
  • Empty method body src/main/scala/ai/starlake/schema/model/WriteStrategy.scala:152 — `validate` has an empty body — confirm it's an intentional no-op and not an unfinished method.
M2 · Architecture documentation · No ADRs · ×1
  • No ADRs — No Architecture Decision Records found — no conventional ADR directory, no numbered `NNNN-title` documents in any markup this check reads, and nothing ADR-shaped by content. Design rationale recorded elsewhere (a design-notes tree, a mailing list, pull-request discussion) is not visible to this check and is not re-findable per decision, so a future maintainer cannot ask why one choice was made and get an answer.
P10 · Library API & versioning · Large public API surface · ×1
  • Large public API surface — 8/10 types (80%) declared in the published library are public. For a library, every public type is a stability contract — keep implementation types off the surface and expose only the intended API.
P3 · Security & performance tooling · No SAST · ×1
  • No SAST — No static application security testing detected. For this repository's stack, add scalafix or scapegoat (or `semgrep --config=auto`, which runs on any language) as a CI step. What was searched, so you can tell an absence from a miss: the 49336 CI workflow file(s) in this repository, and the scanner and linter configuration checked in beside them. A scan that runs outside CI, one configured in your forge's web UI rather than in a committed file, or a tool whose name is none of those this check carries, is not seen — if that is your case the row is wrong, and saying so is more useful than adding a second scanner.
S1 · Web-Security Posture · No app-layer HTTPS enforcement detected · ×1
  • No app-layer HTTPS enforcement detected — This repository runs an HTTP server and nothing in its Scala source or committed configuration secures the transport: no HTTPS served (akka/pekko-http `ConnectionContext.httpsServer`/`enableHttps`, an http4s builder's `withTLS`, Jetty's `SslContextFactory`, zio-http's `SSLConfig`, Play's `https.port`), no `Strict-Transport-Security` header, HSTS middleware or Play `RedirectHttpsFilter`, no `X-Forwarded-Proto` read or Play `play.http.forwarded.trustedProxies` for a TLS-terminating proxy — so its traffic may cross the network in clear text. (−2.0 on this card.) The server: src/main/scala/ai/starlake/serve/SingleUserRequestHandler.scala:10 — `HttpServlet`.
S1 · Web-Security Posture · No security response headers detected · ×1
  • No security response headers detected — None of the 11 security response headers the OWASP Secure Headers Project recommends (Content-Security-Policy, Strict-Transport-Security, X-Content-Type-Options, X-Frame-Options, …) is set on a response this repository's Scala server sends — defense in depth, even when a proxy could set them. (−2.0 on this card.) The server: src/main/scala/ai/starlake/serve/SingleUserRequestHandler.scala:10 — `HttpServlet`.
X5 · Nullable reference types · Scala's null is not held to the type system everywhere · ×1
  • Scala's null is not held to the type system everywhere — No build over this repository's 380 Scala product file(s) passes `-Yexplicit-nulls`, enables WartRemover's `Null` wart or runs Scalafix's `DisableSyntax.noNulls`, so every reference type admits null and nothing reports a `null` written into the code or returned from a Java API — the first one to reach a method call is a `NullPointerException`. `Option` by convention is a habit no tool here holds the code to.
X6 · Hand-rolled structured-format parsing · Hand-rolled JSON/XML parsing via regex · ×1
  • Hand-rolled JSON/XML parsing via regex REDACTED:346 — A regex whose pattern encodes JSON/XML syntax is parsing a structured format by hand — malformed or differently-typed content (e.g. `"amount": "5"` as a string) silently mis-parses instead of failing validation. This JVM code already uses a real parser in the same package; use it here too.
X7 · Silent fallback defaults · Silent fallback default in catch · ×1
  • Silent fallback default in catch REDACTED:1415 — `probeSize` swallows every exception into `return -1L` — a data error becomes a silent behavior change with no trail. Log the failure or let it propagate. If that constant is the correct answer rather than a stand-in for a value that could not be read, say so in a comment in the catch or in the Javadoc, and the row stops firing.
Minor — 15 finding(s)
D12 · Dependency Hygiene · Outdated · ×15
  • Outdated: ai.starlake:starlake-airflow — `ai.starlake %% starlake-airflow` is declared at 0.6.17 in REDACTED, but 0.6.18 is the current release on Maven Central. An sbt declaration names an exact version, so this repository builds against 0.6.17 until the declaration is edited — there is no lockfile refresh that would move it.
  • Outdated: com.fasterxml.jackson.dataformat:jackson-dataformat-csv — `com.fasterxml.jackson.dataformat %% jackson-dataformat-csv` is declared at 2.21.2 in build.sbt, but 2.22.4 is the current release on Maven Central. An sbt declaration names an exact version, so this repository builds against 2.21.2 until the declaration is edited — there is no lockfile refresh that would move it.
  • Outdated: com.github.ben-manes.caffeine:caffeine — `com.github.ben-manes.caffeine %% caffeine` is declared at 3.2.4 in REDACTED, but 3.3.1 is the current release on Maven Central. An sbt declaration names an exact version, so this repository builds against 3.2.4 until the declaration is edited — there is no lockfile refresh that would move it.
  • Outdated: com.github.pureconfig:pureconfig_2.13 — `com.github.pureconfig %% pureconfig_2.13` is declared at 0.17.10 in REDACTED, but 0.17.11 is the current release on Maven Central. An sbt declaration names an exact version, so this repository builds against 0.17.10 until the declaration is edited — there is no lockfile refresh that would move it.
  • Outdated: com.github.scopt:scopt_2.13 — `com.github.scopt %% scopt_2.13` is declared at 4.1.0 in REDACTED, but 4.2.0 is the current release on Maven Central. An sbt declaration names an exact version, so this repository builds against 4.1.0 until the declaration is edited — there is no lockfile refresh that would move it.
  • Outdated: com.google.cloud:google-cloud-bigquery — `com.google.cloud %% google-cloud-bigquery` is declared at 2.71.0 in REDACTED, but 2.73.0 is the current release on Maven Central. An sbt declaration names an exact version, so this repository builds against 2.71.0 until the declaration is edited — there is no lockfile refresh that would move it.
  • Outdated: com.google.cloud:google-cloud-logging — `com.google.cloud %% google-cloud-logging` is declared at 3.38.0 in REDACTED, but 3.40.0 is the current release on Maven Central. An sbt declaration names an exact version, so this repository builds against 3.38.0 until the declaration is edited — there is no lockfile refresh that would move it.
  • Outdated: com.google.cloud.bigdataoss:gcs-connector — `com.google.cloud.bigdataoss %% gcs-connector` is declared at 4.0.5 in REDACTED, but 4.0.6 is the current release on Maven Central. An sbt declaration names an exact version, so this repository builds against 4.0.5 until the declaration is edited — there is no lockfile refresh that would move it.
  • Outdated: com.google.guava:guava — `com.google.guava %% guava` is declared at 33.4.8-jre in build.sbt, but 33.7.2-android is the current release on Maven Central. An sbt declaration names an exact version, so this repository builds against 33.4.8-jre until the declaration is edited — there is no lockfile refresh that would move it.
  • Outdated: com.google.protobuf:protobuf-java — `com.google.protobuf %% protobuf-java` is declared at 4.36.1 in build.sbt, but 4.36.2 is the current release on Maven Central. An sbt declaration names an exact version, so this repository builds against 4.36.1 until the declaration is edited — there is no lockfile refresh that would move it.
  • Outdated: com.hubspot.jinjava:jinjava — `com.hubspot.jinjava %% jinjava` is declared at 2.7.4 in REDACTED, but 2.8.4 is the current release on Maven Central. An sbt declaration names an exact version, so this repository builds against 2.7.4 until the declaration is edited — there is no lockfile refresh that would move it.
  • Outdated: com.networknt:json-schema-validator — `com.networknt %% json-schema-validator` is declared at 2.0.7 in REDACTED, but 3.0.8 is the current release on Maven Central. An sbt declaration names an exact version, so this repository builds against 2.0.7 until the declaration is edited — there is no lockfile refresh that would move it.
  • Outdated: org.apache.poi:poi-ooxml — `org.apache.poi %% poi-ooxml` is declared at 4.1.2 in REDACTED, but 5.5.1 is the current release on Maven Central. An sbt declaration names an exact version, so this repository builds against 4.1.2 until the declaration is edited — there is no lockfile refresh that would move it.
  • Outdated: org.apache.spark:spark-avro_2.13 — `org.apache.spark %% spark-avro_2.13` is declared at 4.1.3 in REDACTED, but 4.2.0 is the current release on Maven Central. An sbt declaration names an exact version, so this repository builds against 4.1.3 until the declaration is edited — there is no lockfile refresh that would move it.
  • Outdated: org.apache.spark:spark-sql-kafka-0-10_2.13 — `org.apache.spark %% spark-sql-kafka-0-10_2.13` is declared at 4.1.3 in REDACTED, but 4.2.0 is the current release on Maven Central. An sbt declaration names an exact version, so this repository builds against 4.1.3 until the declaration is edited — there is no lockfile refresh that would move it.

Appendix B — Reproduction & audit trail

Every external tool invocation behind a deep-scan dimension — the tool, its captured version, the exact command, how many findings it yielded, and a link to the retained raw output. To reproduce any finding: check out the same commit and run the command shown (repo-relative — never an absolute scratch path). The complete raw scanner output is retained verbatim under artifacts/raw/ (indexed in artifacts/raw/index.json); per-invocation exit codes and wall-clock durations are in sidecar.json — kept out of this table so the rendered report stays byte-identical across runs of the same commit.

DimensionToolVersionCommandFindingsRaw output
D28 · Secrets (history)gitleaks—gitleaks detect --no-banner --config /opt/gitleaks-rules/watchdog-gitleaks.toml --report-format json --report-path /tmp/watchdog-gitleaks-d54036affc684b5da51ef703d2bbdb99/history.json --exit-code 0 --source .22artifacts/raw/gitleaks-history.json
D28 · Secrets (history)gitleaks—gitleaks detect --no-git --no-banner --config /opt/gitleaks-rules/watchdog-gitleaks.toml --report-format json --report-path /tmp/watchdog-gitleaks-d54036affc684b5da51ef703d2bbdb99/tree.json --exit-code 0 --source .0artifacts/raw/gitleaks-tree.json
D29 · Static Analysis (SAST)semgrep—semgrep --config /opt/semgrep-rules/security-audit.yml --config /opt/semgrep-rules/owasp-top-ten.yml --config /opt/semgrep-rules/watchdog-sast.yml --json --quiet --timeout 10 --timeout-threshold 3 --metrics off .241artifacts/raw/semgrep.json
D30 · Dependency Vulnerabilitiesosv-scanner—osv-scanner --format json --recursive --experimental-disable-plugins transitivedependency/requirements --experimental-disable-plugins dotnet/csproj --experimental-disable-plugins dotnet/nugetcpm . /tmp/codehealth-osv-sbom-a3e72f34bfc3472a96fc3435a2b82eec2artifacts/raw/osv-scanner.json
D31 · IaC & Container Securitytrivy—trivy config --format json --quiet .2artifacts/raw/trivy-config.json
D32 · Data Compliance (PII/GDPR)semgrep—semgrep: not applicable — No personal data was found crossing a boundary the PII/GDPR ruleset checks — nothing written to a log or console sink, placed in a URL or query string, or persisted to browser storage. That is a clean result for the LEAK surface only: this ruleset detects personal data escaping, it does not inventory the personal data a repository holds, so it is not evidence that this repository has no personal-data surface. The personal-data map (Appendix C) and the C1-C5 compliance cards are what speak to that. semgrep could not parse 3 file(s) — `src/main/scala/ai/starlake/job/bootstrap/Bootstrap.scala`, `src/main/scala/ai/starlake/schema/handlers/SchemaHandler.scala`, `src/main/scala/ai/starlake/schema/model/Sink.scala` — so the PII/GDPR sweep did not cover them at all.0—
D37 · Vulnerability-disclosure Policydisclosure—disclosure: not applicable — No vulnerability-disclosure policy file found (SECURITY.md/.markdown/.rst/.txt at root or under .github/.forgejo/.gitea/docs, .well-known/security.txt). A coordinated-disclosure policy may live off-repo, so this is not evidenced rather than failed.0—
D40 · Network Egress Confinementruntime-hardening—runtime-hardening: not applicable — No Kubernetes/orchestration workloads found in the repository manifests; network egress policy is a cluster-native control that may live at the platform/firewall layer, so there is nothing to assess here.0—
D41 · Kernel & Syscall Confinementruntime-hardening—runtime-hardening: not applicable — No Kubernetes/orchestration workloads found in the repository manifests; seccomp/AppArmor/SELinux confinement is a workload-level control, so there is nothing to assess here.0—
D42 · Runtime Threat Enforcementruntime-hardening—runtime-hardening: not applicable — No Kubernetes/orchestration workloads found in the repository manifests; runtime threat-detection and admission-control policy are cluster-level controls, so there is nothing to assess here.0—
D43 · Malicious Dependenciesosv-scanner—osv-scanner --format json --recursive --experimental-disable-plugins transitivedependency/requirements --experimental-disable-plugins dotnet/csproj --experimental-disable-plugins dotnet/nugetcpm . /tmp/codehealth-osv-sbom-a3e72f34bfc3472a96fc3435a2b82eec0artifacts/raw/osv-scanner.json

Run 01a15e0e-bf24-75f2-8d97-a1e51a4b6328 · every finding is also locatable in findings.md, and the complete scoring record (with exit codes + durations) in sidecar.json.

Issues: 279 · Warnings: 352 · Recommendations: 52 · Info: 15 — Appendix A · all findings · full markdown report.
Generated by Watchdog — deterministic code-health analysis. 21-10-2026 @ 10:43 UTC.

Downloadable artifacts

Machine-readable and reproducible from this commit + frozen rubric — drop them straight into a contract appendix, a CRA dossier, or a downstream SCA / VEX tool.

⬇ Findings, MITRE CWE-tagged .sarif⬇ Health changelog .md