aws/sagemaker-spark

Executive summary

aws/sagemaker-spark scores 55 out of 100 and is rated Adequate, while three of its five lenses score higher still: Architecture at 99 and Security and Compliance at 97, both rated Exemplary, and Code Health at 72, rated Adequate.

The score is lower than those three because the weakest lens counts most, and here that is Production Readiness, which scores 41 and is rated Weak. The project has no CI/CD gates and no security or performance tooling, so those dimensions score 0 out of 10, and until the CI/CD gates dimension reaches 4, Production Readiness can't be rated higher than Adequate. Adding a CI workflow that builds and runs the test suite on every push or PR is therefore the first action, and if Production Readiness rose to 70, the score would rise from 55 to about 64.

Two findings are critical: one secret in the code's history, which should be checked and replaced if it is real, and one orphaned knowledge item in Knowledge Freshness, which should be resolved.

Watchdog couldn't measure Code Coverage because the test suite is not readable by the collector, Test Reliability because the Scala suite was not re-run, and Navigability because too few call sites were resolved for this language. It also could not assess Data Protection, Access Controls, Audit Trail, Data Retention, or Data-Subject Rights because the repository is written in a language the analyzer does not yet model for these checks. The product code is 11,506 lines, which would take an estimated 0.1 person-years, around €20,000, to rebuild.

Code Health 72 Adequate, capped by Nullable reference types Architecture 99 ★ Exemplary Maturity 49 Weak, capped by Knowledge Freshness and Architecture documentation Production Readiness 41 Weak, capped by CI/CD gates and Security & performance tooling Security and Compliance 97 ★ Exemplary

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.

Production Readiness 41 · counts for 47%Maturity 49 · counts for 26%Code Health 72 · counts for 14%Security and Compliance 97 · counts for 8%Architecture 99 · counts for 4%

If Production Readiness rose from 41 to 70, the lowest score rated Strong, the score would rise from 55 to about 64.

Code composition: what the lines of code are

Product code 57%Tests 43%

Rebuild cost: about €20,000 · Estimate

Cost to rebuild	€6,700 to €34,000, for 111 to 353 hours of work, about 0.1 person-years for one engineer
Difficulty of the problem	Standard, 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	×0.8 at a score of 55. 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.
Size	Small: 11,506 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.

Resolve the 3 Orphaned file finding(s) in Knowledge Freshness — start with RequestBatchIterator.scala, build.sbt, setup.py.

Raises the score by about 7 points · Low effort · Knowledge Freshness

Add a CI workflow that builds and runs the test suite on every push/PR.

Raises the score by about 7 points · Medium effort · CI/CD gates

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.

Raises the score by about 7 points · Medium effort · Architecture documentation

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 €20,000 to build again, about 0.1 person-years of work. Its weakest lens is Production Readiness, at 41 (Weak). Production Readiness is about whether the product is ready to run in production and to change without breaking. Because the weakest lens counts most in the score, work on Production Readiness also moves the score most.

Based on: the rebuild estimate · the Production Readiness grade · how much each lens counts

What to do: Start with the Production Readiness actions in Top priorities.

If you do one thing first · First action

Resolve the 3 Orphaned file finding(s) in Knowledge Freshness. Of all the actions in this report, it raises the score most for the work it takes: about 7 points for low effort, because it clears the 3 Knowledge Freshness findings. 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.

30 modules, with 23 dependencies between them. Every module uses only modules listed above it, so there are no loops.

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 →	3 com.amazonaws.services.sagemaker.sparksdk.transformation	8 sagemaker-pyspark-sdk.src.sagemaker_pyspark.wrapper	9 com.amazonaws.services.sagemaker.sparksdk	17 sagemaker-pyspark-sdk.src.sagemaker_pyspark.SageMakerEstimator
1 aialgorithms.proto2
2 com.amazonaws.services.sagemaker.sparksdk.protobuf
3 com.amazonaws.services.sagemaker.sparksdk.transformation
4 docs.lib
5 sagemaker-pyspark-sdk
6 sagemaker-pyspark-sdk.docs
7 sagemaker-pyspark-sdk.src
8 sagemaker-pyspark-sdk.src.sagemaker_pyspark.wrapper
9 com.amazonaws.services.sagemaker.sparksdk	2
10 com.amazonaws.services.sagemaker.sparksdk.transformation.deserializers	3
11 com.amazonaws.services.sagemaker.sparksdk.transformation.serializers	4
12 com.amazonaws.services.sagemaker.sparksdk.transformation.util	3
13 sagemaker-pyspark-sdk.src.sagemaker_pyspark.IAMRoleResource		1
14 sagemaker-pyspark-sdk.src.sagemaker_pyspark.NamePolicy		2
15 sagemaker-pyspark-sdk.src.sagemaker_pyspark.S3Resources		3
16 sagemaker-pyspark-sdk.src.sagemaker_pyspark.SageMakerClients		4
17 sagemaker-pyspark-sdk.src.sagemaker_pyspark.SageMakerEstimator		4
18 sagemaker-pyspark-sdk.src.sagemaker_pyspark.SageMakerModel		1
19 sagemaker-pyspark-sdk.src.sagemaker_pyspark.SageMakerResourceCleanup		2
20 sagemaker-pyspark-sdk.src.sagemaker_pyspark.transformation.deserializers.deserializers		1
21 sagemaker-pyspark-sdk.src.sagemaker_pyspark.transformation.serializers.serializers		1
22 com.amazonaws.services.sagemaker.sparksdk.algorithms	10		20
23 com.amazonaws.services.sagemaker.sparksdk.exceptions			2
24 com.amazonaws.services.sagemaker.sparksdk.internal			4
25 sagemaker-pyspark-sdk.src.sagemaker_pyspark.algorithms.FactorizationMachinesSageMakerEstimator				2
26 sagemaker-pyspark-sdk.src.sagemaker_pyspark.algorithms.KMeansSageMakerEstimator				1
27 sagemaker-pyspark-sdk.src.sagemaker_pyspark.algorithms.LDASageMakerEstimator				1
28 sagemaker-pyspark-sdk.src.sagemaker_pyspark.algorithms.LinearLearnerSageMakerEstimator				3
29 sagemaker-pyspark-sdk.src.sagemaker_pyspark.algorithms.PCASageMakerEstimator				1
30 sagemaker-pyspark-sdk.src.sagemaker_pyspark.algorithms.XGBoostSageMakerEstimator				1

Uses a module listed above itPart of a loopThe module itself

At a glance — Code Health · 72% · Adequate · gated by X5 ·

The Good

D1 · Scala Cyclomatic Complexity 10.0 D2 · Scala Cognitive Complexity 9.3 D3 · Scala God Classes 8.1 D4 · Scala Code Duplication 9.8 D17 · Scala Explicit Debt 10.0 X1 · Repo Async correctness 10.0 X12 · Repo Unreachable branch 10.0 X13 · Repo Undrained process stream 10.0 X16 · Repo Unfloored truncation loop 10.0 X18 · Repo Disposal-pattern correctness 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 X3 · Repo Exception handling 10.0 X30 · Repo Support guard that admits what it rejects 10.0

The Ugly

X5 · Repo Nullable reference types 2.0

At a glance — Architecture · 99% · Exemplary ·

The Good

D5 · Scala Coupling 10.0 D6 · Scala Cohesion (LCOM4) 10.0 D22 · Scala Internal API Consistency 9.0 D26 · Scala Project Cohesion 10.0 D35 · Scala Change Coupling 10.0 AX10 · Repo Code composition 10.0 AX3 · Repo Project dependency cycles 10.0 AX4 · Repo Dependency direction 10.0 AX6 · Repo Interface segregation 9.6

At a glance — Maturity · 49% · Weak · gated by D34, M2 ·

The Good

D15 · Scala Churn × Complexity Hotspots 10.0 D19 · Scala Documentation Quality 7.8 D21 · Scala Naming Consistency 10.0 M1 · Repo Documentation (README) 8.7 M3 · Repo Folder & project structure 10.0 M4 · Repo Documentation accuracy 8.0

The Ugly

D34 · Scala Knowledge Freshness 0.0 M2 · Repo Architecture documentation 0.0

At a glance — Readiness · 41% · Weak · gated by P1, P3 ·

The Good

D9 · Scala Test Distribution 10.0 D13 · Scala REDACTED Scanning 10.0 D14 · Scala License Compliance 10.0 P10 · Repo Library API & versioning 10.0 P6 · Repo Release Hygiene 10.0

The Ugly

D12 · Scala Dependency Hygiene 5.0 P1 · Repo CI/CD gates 0.0 P3 · Repo Security & performance tooling 0.0

At a glance — Security · 97% · Exemplary ·

The Good

D28 · Scala Secrets (history) 9.0 D29 · Scala Static Analysis (SAST) 10.0 D44 · Scala Platform End-of-Life 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

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 category	Findings	Severity
A07:2025 — Authentication Failures	2	High / Critical

Roadmap

First, resolve the three orphaned files in the Knowledge Freshness dimension to ensure code assets are properly tracked. Next, establish a CI workflow that builds and tests every push or pull request to prevent undetected regressions. Simultaneously, document significant design decisions in a dedicated ADR folder to capture architectural context and consequences. Finally, enforce strict null-safety by enabling explicit null checks in Scala and improving the overall documentation quality to provide clearer project overviews and usage guidance.

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

Do this	Helps	Effort	Dimension
Resolve the 3 Orphaned file finding(s) in Knowledge Freshness — start with RequestBatchIterator.scala, build.sbt, setup.py.	+7.0 pts	Low	Knowledge Freshness
Add a CI workflow that builds and runs the test suite on every push/PR.	+6.7 pts	Medium	CI/CD gates
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.	+6.5 pts	Medium	Architecture documentation
Resolve the 1 Further orphaned files (smaller) finding(s) in Knowledge Freshness.	+2.6 pts	Low	Knowledge Freshness
Resolve the 1 Orphaned knowledge finding(s) in Knowledge Freshness — start with LinearLearnerSageMakerEstimator.py.	+2.6 pts	Low	Knowledge Freshness
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.	+3.6 pts	Medium	Nullable reference types
Improve Documentation Quality — currently 7.8/10.	+2.9 pts	Medium	Documentation Quality
Reconcile the README with reality: README advertises Docker containerisation, but no Dockerfile/compose file exists.	+0.8 pts	Medium	Documentation accuracy

File quality

Per-file score 0–10 — a quality signature. Of 12 files carrying findings, judged against the Production bar: 0% slop · 42% mixed · 58% near-clean.

File	Score	Band	Worst signal
REDACTED	7.2	Mixed	Secrets (history): REDACTED: REDACTED
sagemaker-pyspark-sdk/src/sagemaker_pyspark/algorithms/XGBoostSageMakerEstimator.py	7.8	Mixed	God Classes: TooManyMethods: XGBoostSageMakerEstimator
sagemaker-pyspark-sdk/src/sagemaker_pyspark/algorithms/LinearLearnerSageMakerEstimator.py	7.8	Mixed	God Classes: TooManyMethods: LinearLearnerParams
sagemaker-spark-sdk/src/main/scala/com/amazonaws/services/sagemaker/sparksdk/algorithms/LinearLearnerSageMakerEstimator.scala	7.8	Mixed	God Classes: TooManyMethods: LinearLearnerSageMakerEstimator
sagemaker-pyspark-sdk/src/sagemaker_pyspark/SageMakerEstimator.py	7.8	Mixed	Code Duplication: Near-duplicate member family (10 members, 23 shared lines)
sagemaker-spark-sdk/src/main/scala/com/amazonaws/services/sagemaker/sparksdk/protobuf/ProtobufConverter.scala	8.5	Near-clean	Cognitive Complexity: ProtobufConverter.setFeatures (cognitive 26)
sagemaker-pyspark-sdk/src/sagemaker_pyspark/algorithms/FactorizationMachinesSageMakerEstimator.py	8.5	Near-clean	God Classes: TooManyMethods: FactorizationMachinesParams
sagemaker-spark-sdk/src/main/scala/com/amazonaws/services/sagemaker/sparksdk/algorithms/XGBoostSageMakerEstimator.scala	8.5	Near-clean	God Classes: TooManyMethods: XGBoostSageMakerEstimator
sagemaker-spark-sdk/src/main/scala/com/amazonaws/services/sagemaker/sparksdk/SageMakerModel.scala	8.5	Near-clean	Code Duplication: Duplicated block (14 lines × 2)
sagemaker-pyspark-sdk/src/sagemaker_pyspark/SageMakerModel.py	8.5	Near-clean	Code Duplication: Duplicated block (64–65 lines × 2)
sagemaker-pyspark-sdk/docs/conf.py	8.5	Near-clean	Code Duplication: Duplicated block (5 lines × 2)
sagemaker-pyspark-sdk/src/sagemaker_pyspark/algorithms/LDASageMakerEstimator.py	8.5	Near-clean	Code Duplication: Duplicated block (12 lines × 2)

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 — 2

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 — 17

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 — 14

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

Could not be resolved — 13

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. 51 of 55 evaluated dimensions are computed purely by tools and static analysis (confidence 1.0); 4 documentation/naming judgement(s) are LLM-assisted and labelled advisory. Overall confidence is 0.9 — 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 — 55 dimensions across the health lenses

D1D2D3D4D5D6D9D12D13D14D15D17D19D21D22D26D28D29D34D35D44AX10AX3AX4AX6M1M2M3M4P1P10P3P6X1X12X13X14X15X16X17X18X19X20X21X22X23X24X25X26X27X28X29X3X30X5

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

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, 24 of 33 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.)
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.
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.

Method	Backs	Version	Evaluator
Roslyn static analysis	Complexity, cohesion, coupling, dead code, API surface, layering	5.3.0	✓ deterministic
Native secret scanner	Hardcoded secrets / credentials	1.0.0	✓ deterministic
Watchdog duplication detector (in-process)	Code duplication	1.0.0	✓ deterministic
Coverage (coverlet / dotnet-coverage)	Line & branch coverage	10.0.400	✓ deterministic
NuGet / dotnet	Outdated, vulnerable & deprecated dependencies	10.0.400	✓ deterministic
git / LibGit2Sharp	Churn hotspots, knowledge concentration, history	2.43.0 · 0.31.0	✓ deterministic
gitleaks · semgrep · trivy	Secrets in history, SAST, CVEs, IaC & container, PII / GDPR	1.86.0 · 0.69.3	✓ deterministic
LLM (sampled · advisory)	Documentation quality, ADR conformance, naming — sampled over a bounded sample; advisory, never a deterministic measurement	Local LLM	◐ LLM · sampled · advisory

Every finding is locatable in findings.md. Run 01a15dc3-14f8-7a76-ac79-be705c1d6dfc.

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 (.py, .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 (`coverage run -m pytest` then `coverage xml`, or `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 (`coverage run -m pytest` then `coverage xml`, or `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 (.py, .scala) and this repository declares an sbt build (sagemaker-spark-sdk, 23 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.
D14 License Compliance — 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 declares an sbt build (build.sbt), but the licence verdict published here was taken over its Python distribution dependencies. Nothing was read about its sbt dependencies' licensing in either direction, and a clean score on this card must not be read as covering them.
D16 Bus Factor — not measured this run — This is a true statement about the repository that carries nothing for its owner to act on, so it is reported here rather than as a defect in their code. All 4 significant source file(s) were last meaningfully changed so long ago that no living knowledge remains — nothing since has been substantial enough to re-establish ownership (a broad, mechanical sweep that touches many files shallowly does not count, and neither does no activity at all). There is no concentration to measure, so the bus factor is not scored. This is not a clean bill: nobody currently holds working knowledge of this code (see D34 Knowledge Freshness). Counted over 2 of the 9 production source files in this repository: 5 are under the ~2,400-byte size floor this dimension measures over, and the remaining 2 have no attributable history left to measure.
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. `docs/lib/jquery.js` 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.
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. These audit 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 audit controls.
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. These retention 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 retention controls.
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. These data-subject rights 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 data-subject rights controls.
GD1 Unfinished & placeholder code — 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 check reads C# syntax, F#, JavaScript/TypeScript, Dart, Ruby and Scala source only, and this repository's Java, Python is most of its product and is not read yet, so it had nothing of this repository's product to read. That is a gap in this analyzer's language reach — not a finding that the repository is free of what this check looks for.
IC1 Incompleteness & stubs — 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 check reads C# syntax, JavaScript/TypeScript, Java, Dart, Ruby and Scala source only, and this repository's Python is most of its product and is not read yet, so it had nothing of this repository's product to read. That is a gap in this analyzer's language reach — not a finding that the repository is free of what this check looks for.
P5 DR & Backup — not measured this run — This is a true statement about the repository that carries nothing for its owner to act on, so it is reported here rather than as a defect in their code. No backup/snapshot/replication config, RTO/RPO or restore-procedure documentation was found — and no production persistence was detected either (no data-access packages, no data-store services, no database resources), so there is nothing in this repository whose loss a DR control would recover. If this system's data lives in a platform or ops repo we can't see, that's where the DR evidence belongs.
S1 Web-Security Posture — 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 web-security 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 web-security controls.

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.
D5 Coupling: Coupling is measured between projects/assemblies — runtime coupling through DI, reflection, messaging or shared databases is invisible to a static reference graph.
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.
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.
D19 Documentation Quality: Documentation quality is judged by an LLM over a bounded sample of docs — it reads what is written, not whether the docs match the running system, and it is advisory, not a measurement. Its critique rows are drawn from a closed category vocabulary and each row means the same thing in every run, so two scans can be compared row by row; the SET that fires is still a sample, and does not repeat exactly. Measured on one frozen input, six scans at one engine SHA: 2-5 critique rows per scan, 8 distinct rows across the six, 3 of those 8 seen in only one scan. So a D19 row is evidence about the documentation, but a COUNT of D19 rows is not a quantity — never read a change in it as an improvement or a regression.
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.
D22 Internal API Consistency: API-surface coherence is an LLM judgement over a sample of the public surface — consistency of intent across the whole API is approximated, not exhaustively verified.
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").
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.
D44 Platform End-of-Life: The support table is FROZEN, so it goes out of date by losing RECALL: a release that ended support after the table was written is missed until the table is refreshed, and this dimension reading 10 is not evidence that a platform is current. Only platforms the repository DECLARES in a place this pass reads are seen — a runtime named only in a Dockerfile (D31's subject), in a CI workflow (D29's), or in a file this pass does not parse (go.mod, a Gemfile ruby directive) is invisible here, which is why a repository declaring none of them abstains rather than scoring. Only frameworks with a PUBLISHED support policy are tracked: React, Flask and Express publish none, so their age cannot be judged and their absence from a report is not a statement that they are supported.
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.
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 (4): D19, D21, D22, 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 Complexity10.0 / 10Exemplary✓ 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 10.0 / 10 · rule-coverage 100% · ceiling Prevented

0 method(s) exceeded the cyclomatic complexity threshold of 15.

✓ On the Gold path — maintain.

Detailed fixes: d1_recommendation.md.

Do you agree with this assessment?

D2 · Cognitive Complexity9.3 / 10Stronggated by 1 serious finding✓ 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 9.3 / 10 · rule-coverage 100% · ceiling Prevented

1 method(s) exceeded the cognitive complexity threshold of 15; the worst was ProtobufConverter.setFeatures at 26.

ProtobufConverter.setFeatures (cognitive 26)sagemaker-spark-sdk/src/main/scala/com/amazonaws/services/sagemaker/sparksdk/protobuf/ProtobufConverter.scala:238

What to do

Bring the 1 body over 15 down to 15 or less in Cognitive Complexity — start with ProtobufConverter.setFeatures (cognitive 26). — Refactoring it lifts Cognitive Complexity from 9.3 to about 10.0/10.
Stand up a CI pipeline, then gate Cognitive Complexity in it to reach Verified (currently Documented). — This repository has no CI pipeline, so there is nothing to add a gate to yet — the pipeline comes first. 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.

Do you agree with this assessment?

D3 · God Classes8.1 / 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 8.1 / 10 · rule-coverage 100% · ceiling Prevented

8 god class(es) detected.

TooManyMethods: XGBoostSageMakerEstimator · ×5sagemaker-pyspark-sdk/src/sagemaker_pyspark/algorithms/XGBoostSageMakerEstimator.py:23

FileTooLong: algorithms/LinearLearnerSageMakerEstimator.py · ×3sagemaker-pyspark-sdk/src/sagemaker_pyspark/algorithms/LinearLearnerSageMakerEstimator.py

What to do

Resolve the 5 TooManyMethods finding(s) in God Classes — start with XGBoostSageMakerEstimator.py, LinearLearnerSageMakerEstimator.py, FactorizationMachinesSageMakerEstimator.py. — One of this dimension's main actionable groups (5 warning-level).
Resolve the 3 FileTooLong finding(s) in God Classes — start with LinearLearnerSageMakerEstimator.py, LinearLearnerSageMakerEstimator.scala, XGBoostSageMakerEstimator.py. — One of this dimension's main actionable groups (3 warning-level).
Stand up a CI pipeline, then gate God Classes in it to reach Verified (currently Documented). — This repository has no CI pipeline, so there is nothing to add a gate to yet — the pipeline comes first. 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.

Do you agree with this assessment?

D4 · Code Duplication9.8 / 10Stronggated by 6 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 9.8 / 10 · rule-coverage 100% · ceiling Verified

5 duplicated block group(s) detected. One further row reports members as variants of one another; it aggregates block groups already counted above and is not itself counted.

Near-duplicate member family (10 members, 23 shared lines)sagemaker-pyspark-sdk/src/sagemaker_pyspark/SageMakerEstimator.py:443

Duplicated block (14 lines × 2)sagemaker-spark-sdk/src/main/scala/com/amazonaws/services/sagemaker/sparksdk/SageMakerModel.scala:109

Duplicated block (64–65 lines × 2)sagemaker-pyspark-sdk/src/sagemaker_pyspark/SageMakerModel.py:154

Duplicated block (23 lines × 10)sagemaker-pyspark-sdk/src/sagemaker_pyspark/SageMakerEstimator.py:447

Duplicated block (5 lines × 2)sagemaker-pyspark-sdk/docs/conf.py:18

+ 1 more group(s) — more in Appendix A; the complete list is findings.md.

What to do

Resolve the 1 Near-duplicate member family (10 members, 23 shared lines) finding(s) in Code Duplication — start with SageMakerEstimator.py. — One of this dimension's main actionable groups (1 warning-level).
Resolve the 1 Duplicated block (14 lines × 2) finding(s) in Code Duplication — start with SageMakerModel.scala. — One of this dimension's main actionable groups (1 warning-level).
Resolve the 1 Duplicated block (64–65 lines × 2) finding(s) in Code Duplication — start with SageMakerModel.py. — One of this dimension's main actionable groups (1 warning-level).
Stand up a CI pipeline, then gate Code Duplication in it to reach Verified (currently Documented). — This repository has no CI pipeline, so there is nothing to add a gate to yet — the pipeline comes first. 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.

Do you agree with this assessment?

D5 · Coupling10.0 / 10Exemplary✓ Tool-verified

What it measures: Whether volatile projects sit underneath others that depend on them (so their churn ripples upward), and whether project dependencies form cycles. A widely-depended-on but stable shared/kernel project is healthy, not penalised.

Method: Dependency cycles via elementary-DFS over real .csproj references, plus Martin instability (afferent/efferent) per project. Exhaustive over the reference graph, deterministic.

Coverage: Exhaustive · type-level: afferent/efferent coupling + cycles computed over every production type — the population is all types, not a name convention.

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

2 production modules (Python+sbt), 0 dependency cycle(s), 0 unstable depended-on module(s). Read from the build's own module declarations; a dependsOn spread (deps: _*) and a project whose directory a helper computes are not visible there; 0 module(s) off the main sequence.

✓ On the Gold path — maintain.

Detailed fixes: d5_recommendation.md.

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D6 · Cohesion (LCOM4)10.0 / 10Exemplary✓ 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 10.0 / 10 · rule-coverage 100% · ceiling Verified

0 of 11 classes have LCOM4 above 3.

✓ On the Gold path — maintain.

Detailed fixes: d6_recommendation.md.

Do you agree with this assessment?

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

664 test methods: 664 unit, 0 integration, 0 BDD, 0 e2e. The Python suite contributes 233 test function(s) across 15 file(s) declaring at least one — every `def test…` in a file pytest or unittest would collect, which is those frameworks' own definition of a case; a parametrize table counts once, so this is a floor. Its tier split is read from file names and paths only. The Scala suite contributes 431 test case(s) across 23 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.

Do you agree with this assessment?

D12 · Dependency Hygiene~~9.9~~ 5.0 / 10Weak✓ 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 5.0 / 10 · rule-coverage 50% · ceiling Verified

1 outdated, 0 yanked pinned Python distributions. 1 of 2 shipped distributions were graded against pypi.org (0 not published there, 1 declared without an exact pin, which installs the newest release the declaration admits and so cannot be behind one). Only EXACT pins are graded for currency: a floor or a range already installs the newest release it admits, so reporting one would report this repository for being current. Whether a deliberate upper cap has itself gone stale is a different and weaker question, and is not asked. Whether any distribution is UNMAINTAINED is not graded — PyPI publishes no maintenance status, and release age does not stand in for one. Lockfiles are not read (poetry.lock, uv.lock, Pipfile.lock, pdm.lock), so a lock-resolved install is outside this verdict. Known CVEs in this dependency graph are D30's question.

Outdated: pyspark

What to do

Stand up a CI pipeline, then gate Dependency Hygiene in it to reach Verified (currently Documented). — This repository has no CI pipeline, so there is nothing to add a gate to yet — the pipeline comes first. 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.

Do you agree with this assessment?

D13 · REDACTED Scanning10.0 / 10Exemplary○ Nothing flagged

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 10.0 / 10 · rule-coverage 100% · ceiling Prevented

REDACTED scan ran and found no leaked secrets.

✓ On the Gold path — maintain.

Detailed fixes: d13_recommendation.md.

Do you agree with this assessment?

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 3 shipped Python distribution(s) use a banned license. Licences were resolved from PyPI over the distributions a consumer installs — this repository's 2 declared runtime requirement(s) closed transitively over each distribution's published `requires_dist` (1 reached that way). Requirements it states ONLY under an extra, a PEP 735 dependency group, a Poetry dev group or a dev-named requirements file are excluded: pip does not install any of them for a consumer. ★ This repository commits no dependency lockfile that this pass reads, so each licence is the one PyPI publishes for the distribution's CURRENT release rather than for a pinned version. 1 of them publish no licence on PyPI this pass can read; that is missing data, not a violation, and none of them is charged. ★ COVERAGE OF THIS VERDICT: it grades this repository's Python distribution dependencies and nothing else. The repository also declares an sbt build (build.sbt), and the licences of those dependencies were NOT read by this pass — a gap in this engine's coverage, not a statement about them. So this result says the graded closure carries no banned licence; it does NOT say this repository's licensing is clear.

✓ On the Gold path — maintain.

Detailed fixes: d14_recommendation.md.

Do you agree with this assessment?

D15 · Churn × Complexity Hotspots10.0 / 10Exemplary✓ 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 10.0 / 10 · rule-coverage 100% · ceiling Documented

No churn × complexity hotspots in the window.

✓ On the Gold path — maintain.

Detailed fixes: d15_recommendation.md.

Do you agree with this assessment?

D17 · Explicit Debt10.0 / 10Exemplary○ Nothing flagged

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 10.0 / 10 · rule-coverage 100% · ceiling Prevented

0 deducted task-comment markers across 11506 LoC (0.0/KLoC) → score 10.0. 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.

✓ On the Gold path — maintain.

Detailed fixes: d17_recommendation.md.

Do you agree with this assessment?

D19 · Documentation QualityStrong◐ Sampled · advisory

What it measures: Whether the project's documentation is clear, complete, and useful.

Method: Judged by language model at low temperature (0.0-0.1) on a deterministic doc sample (READMEs plus first 25 architecture docs), with two-pass stability filtering. Advisory, sampled.

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

The SageMaker Spark project is well documented: the repository root README gives a clear overview of what the library does (constructing Spark ML pipelines using SageMaker stages), its Amazon-provided ML algorithms, and links to Read the Docs. The sagemaker-pyspark-sdk/README.rst documents that directory (SageMaker PySpark SDK for Python) with install instructions, credential setup, a code example loading the classpath programatically, and an API table of contents; it is not the root README but clearly serves its own tier. A companion architecture/Docs markdown set at 98 files gives detailed design docs, which are clipped mid-sentence in the summary (the visible text ends with The repository's READMEs are thin and mostly describe individual components (e.g. PCAProtobufResponseRowDeserializer, SageMakerEstimator) rather than the project as a whole; there is no overview of what the project does or its scope, no installation/build instructions, no usage examples, and no contribution guidance. The architecture/Docs markdown files are comprehensive but not shown in this summary.

What to do

Improve Documentation Quality — currently 7.8/10. — The SageMaker Spark project is well documented: the repository root README gives a clear overview of what the library does (constructing Spark ML pipelines using SageMaker stages), its Amazon-provided ML algorithms, and links to Read the Docs. The sagemaker-pyspark-sdk/README.rst documents that directory (SageMaker PySpark SDK for Python) with install instructions, credential setup, a code example loading the classpath programatically, and an API table of contents; it is not the root README but clearly serves its own tier. A companion architecture/Docs markdown set at 98 files gives detailed design docs, which are clipped mid-sentence in the summary (the visible text ends with The repository's READMEs are thin and mostly describe individual components (e.g. PCAProtobufResponseRowDeserializer, SageMakerEstimator) rather than the project as a whole; there is no overview of what the project does or its scope, no installation/build instructions, no usage examples, and no contribution guidance. The architecture/Docs markdown files are comprehensive but not shown in this summary.

Detailed fixes: d19_recommendation.md.

Do you agree with this assessment?

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.

Do you agree with this assessment?

D22 · Internal API ConsistencyStronggated by 1 serious finding◐ Sampled · advisory

What it measures: Whether the internal API surface is consistent and coherent.

Method: Judged by language model at low temperature over a sample of the public API surface (IsPackable or .Contracts types). Sampled, advisory; confidence discounted by model uncertainty.

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

1 API inconsistencies across a 400-member sample of 60 exposed types.

Redundant Type Hierarchy: The `CustomNamePolicyWithTimeStampSuffix` class and its corresponding `CustomNamePolicyWithTimeStampSuffixFactory` appear to be functionally identical to `CustomNamePolicy` and `CustomNamePolicyFactory` except for the implicit timestamp suffix behavior. However, the API exposes them as distinct, parallel classes with identical constructor signatures (trainingJobName, modelName, endpointConfigName, endpointName). This forces users to choose between two nearly identical types rather than configuring a single type via a parameter (e.g., `use_timestamp=True`).

What to do

Resolve the 1 Redundant Type Hierarchy finding(s) in Internal API Consistency. — One of this dimension's main actionable groups (1 warning-level).

Detailed fixes: d22_recommendation.md · top locations in Appendix A, every location in findings.md.

Do you agree with this assessment?

D26 · Project Cohesion10.0 / 10Exemplary✓ 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 10.0 / 10 · rule-coverage 100% · ceiling Documented

0 of 2 build units (Python, sbt) flagged as possibly oversized/incoherent.

✓ On the Gold path — maintain.

Detailed fixes: d26_recommendation.md.

Do you agree with this assessment?

D28 · Secrets (history)9.0 / 10Adequategated by 1 critical finding✓ 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 9.0 / 10 · rule-coverage 100% · ceiling Documented

1 finding(s): 0 critical, 1 high, 0 medium, 0 low. Remediation for historically-committed secrets is credential rotation — they remain in history regardless of later deletion.

REDACTED

What to do

Resolve the 1 REDACTED finding(s) in Secrets (history) — start with REDACTED. — One of this dimension's main actionable groups (1 issue-level).
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.

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D29 · Static Analysis (SAST)10.0 / 10Exemplary○ Nothing flagged

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 10.0 / 10 · rule-coverage 100% · ceiling Documented

semgrep found no security issues. semgrep hit a parse error in 1 file(s) — `docs/lib/jquery.js` (line 2) — 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.

✓ On the Gold path — maintain.

Detailed fixes: d29_recommendation.md.

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D34 · Knowledge Freshness0.0 / 10Critical✓ 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 0.0 / 10 · rule-coverage 100% · ceiling Documented

4 of 4 significant source file(s) are orphaned — their living knowledge has decayed to nothing, so no one currently understands them. The largest is sagemaker-pyspark-sdk/src/sagemaker_pyspark/algorithms/LinearLearnerSageMakerEstimator.py. Counted over 4 of the 9 production source files in this repository: the rest are under the ~2,400-byte size floor this dimension measures over.

Orphaned knowledgesagemaker-pyspark-sdk/src/sagemaker_pyspark/algorithms/LinearLearnerSageMakerEstimator.py:1

Orphaned file · ×3sagemaker-spark-sdk/src/main/scala/com/amazonaws/services/sagemaker/sparksdk/transformation/util/RequestBatchIterator.scala:1

Further orphaned files (smaller)

What to do

Resolve the 3 Orphaned file finding(s) in Knowledge Freshness — start with RequestBatchIterator.scala, build.sbt, setup.py. — One of this dimension's main actionable groups (3 recommendation-level).
Resolve the 1 Orphaned knowledge finding(s) in Knowledge Freshness — start with LinearLearnerSageMakerEstimator.py. — One of this dimension's main actionable groups (1 issue-level).
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.

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D35 · Change Coupling10.0 / 10Exemplary✓ 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 10.0 / 10 · rule-coverage 100% · ceiling Documented

No strong hidden change-coupling between production files.

✓ On the Gold path — maintain.

Detailed fixes: d35_recommendation.md.

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D44 · Platform End-of-Life10.0 / 10Exemplary✓ Tool-verified

What it measures: Whether anyone still ships security patches for the platform this repository RUNS ON — the runtime it pins and the framework majors its own constraints hold it to. Separate from D12 because the question differs: a current Django on an end-of-life Python is perfectly up to date and completely unsupported, and the fix is a migration rather than a version bump. What the repository says it merely SUPPORTS is never charged.

Method: End-of-life PLATFORM read from the repository's own declarations and graded against a FROZEN, dated table of vendor support dates — no network, no feed, no API, so this dimension answers identically inside a closed scan fence. Two subjects: a RUNTIME the project pins (a single or all-end-of-life TargetFramework, a .nvmrc or .python-version, a requires-python CAP) and a FRAMEWORK major a dependency constraint cannot move off (a caret, tilde or exact version; `vue@^2.7.16` pins Vue 2). A FLOOR is deliberately never charged — `requires-python = ">=3.8"` states what a package SUPPORTS, not what it runs on — and a multi-target project is charged only when EVERY target is out of support. Runtime 4.0/product capped 8.0, framework 1.5 capped 4.5. The table is safe to freeze because a statement about support that ended in the past cannot become false: it loses recall as it ages, never precision, and a test asserts every entry predates the freeze date. Disjoint from D31 (a container image's OS layer) and D29 (the toolchain a CI workflow installs). Abstains when the repository declares no platform this pass reads — never scores it clean.

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

0 end-of-life runtime(s) and 0 end-of-life framework(s), read from 0 platform declaration(s) and 2 dependency declaration(s). This dimension reads what the repository says about ITSELF — a pinned target framework, a version file, a capped requires-python, a Rust toolchain pin, a framework major a constraint cannot move off. A FLOOR is deliberately never charged: `requires-python = ">=3.8"` states what the package SUPPORTS, not what it runs on, and a well-maintained library declares exactly that while running its own CI on a current release. The end-of-life facts are FROZEN and dated, so this dimension needs no network and answers identically inside a closed scan fence; as the table ages it loses recall and never precision, because a statement about support that ended in the past cannot become false. The OS layer of a container image is D31's question and the toolchain a CI workflow installs is D29's; this row is neither.

✓ On the Gold path — maintain.

Detailed fixes: d44_recommendation.md.

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Frontend & cross-cutting dimensions

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

AX10 · Code composition10.0 / 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.

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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.

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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.

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AX6 · Interface segregation9.6 / 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.

`RecordOrBuilder` declares 19 abstract methods: `getConfiguration`, `getConfigurationBytes`, `getFeatures`, `getFeaturesCount`, `getFeaturesList`, `getFeaturesOrBuilder`, `getFeaturesOrBuilderList`, `getLabel`, `getLabelCount`, `getLabelList`, `getLabelOrBuilder`, `getLabelOrBuilderList`, `getMetadata`, `getMetadataBytes`, `getUid`, `getUidBytes`, `hasConfiguration`, `hasMetadata`, `hasUid`. Counted over its ABSTRACT methods, once per signature; `default`, `static` and `private` methods and constants 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. — sagemaker-spark-sdk/src/main/scala/aialgorithms/proto2/RecordProto2.java:5619

What to do

Split fat interfaces into focused role-interfaces so clients depend only on what they use.

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M1 · Documentation (README)8.7 / 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.

What to do

Explain in the README how to run the tests. Add a section called Testing to the README in the repository's top folder, saying how to run the test suite.
Add an 'Architecture' / 'How it works' section to the root README — the high-level shape.

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M2 · Architecture documentation0.0 / 10Critical✓ 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.
No C4/Structurizr/PlantUML/Mermaid/Graphviz/D2 diagram, no drawn diagram named for the architecture, no file named `architecture` or `design` in any markup this check reads, and nothing in the README, docs or contributor guides that announces the shape — no `## Architecture` heading, no "architecture overview"/"high-level design" phrasing, no "the architecture is …" introduction, no guided code tour. A shape laid out in prose that never names itself as the architecture is not visible to this check, and neither is one kept outside the repository, so this row reports the absence of a re-findable shape document — not evidence that nobody wrote the shape down.

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.
Add a C4 context/container diagram (Structurizr, PlantUML or Mermaid) or an architecture.md overview.

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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.

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M4 · Documentation accuracy8.0 / 10Strong◐ 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.

README advertises Docker containerisation, but no Dockerfile/compose file exists — searched for: `dockerfile`, `docker-compose`, `compose.yaml`, `compose.yml`. Each was matched case- and separator-insensitively against file and directory NAMES anywhere in the tree, and against the CONTENTS of manifest and configuration files (package.json and other *.json, *.csproj, *.props, *.sln/*.slnx, *.yml, *.toml, *.config, tool configs such as eslint.config.js, Dockerfile); the README's own prose never counts, so a claim is never refuted by merely being made. Nothing outside that search was read — a footprint living only in a submodule, in a file type not listed here, or under a name none of those terms matches is not seen, and this row is then wrong. — README.md:11

What to do

Reconcile the README with reality: README advertises Docker containerisation, but no Dockerfile/compose file exists.

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P1 · CI/CD gates0.0 / 10Critical✓ Tool-verified

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.

No CI workflow found (.github/workflows, azure-pipelines.yml, .gitlab-ci.yml, …) — changes aren't gated by an automated build/test.

What to do

Add a CI workflow that builds and runs the test suite on every push/PR.

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P10 · Library API & versioning10.0 / 10Exemplary○ Nothing flagged

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.

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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) — this repository has no CI pipeline yet, so run it locally to clear the existing findings, then make it a step of the first workflow you add so a regression fails the build. What was searched, so you can tell an absence from a miss: the 0 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

Run what this repository's stack ships: scalafix or scapegoat — or `semgrep --config=auto`, which runs on any language — locally for now, since there is no CI pipeline here yet, and as a step of the first workflow you add so a security regression fails the build instead of landing.
Add gitleaks/trufflehog in CI to block PRs that introduce committed secrets.

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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.

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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.

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X12 · Unreachable branch10.0 / 10Exemplary○ Nothing flagged

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.

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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.

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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.

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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.

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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.

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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.

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X18 · Disposal-pattern correctness10.0 / 10Exemplary○ Nothing flagged

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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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X3 · Exception handling10.0 / 10Exemplary○ Nothing flagged

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.

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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.

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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 34 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.

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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.

Lens	Score	Rating	Impact
Code Health	72%	Adequate — gated by X5	Capped at Fair by a Critical contributor — resolve it before relying on this lens.
Architecture	99%	Exemplary	Strongest area.
Maturity	49%	Weak — gated by D34, M2	Capped at Fair by a Critical contributor — resolve it before relying on this lens.
Readiness	41%	Weak — gated by P1, P3	Capped at Fair by a Critical contributor — resolve it before relying on this lens.
Security	97%	Exemplary	Solid.

Unscored — 1 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 — 2 observation(s) recorded · Advisory — this card reports evidence and never carries a score.

Not evidenced — 5 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: these audit 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 audit 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.
C4 Data Retention — Not assessed: these retention 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 retention 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.
C5 Data-Subject Rights — Not assessed: these data-subject rights 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 data-subject rights 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.
P4 Deployment & Rollback — not evidenced — no deploy/rollback/approval signal in the repo; absence of evidence is not evidence of a manual release
P5 DR & Backup — not evidenced — repo shows no backup/RTO/RPO controls; absence of evidence is not evidence of a working control

Not included — 59 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 — Frontend below the scale floor (10 DOM element(s) < 25) — too little surface to assess accessibility.
AC2 Forms & labels — Frontend below the scale floor (10 DOM element(s) < 25) — too little surface to assess accessibility.
AC3 Page structure — Frontend below the scale floor (10 DOM element(s) < 25) — too little surface to assess accessibility.
AC4 Keyboard semantics — Frontend below the scale floor (10 DOM element(s) < 25) — too little surface to assess accessibility.
AC5 ARIA correctness — Frontend below the scale floor (10 DOM element(s) < 25) — too little surface to assess accessibility.
AC6 Visual & motion safety — Frontend below the scale floor (10 DOM element(s) < 25) — too little surface to assess accessibility.
AC7 A11y enforcement — Frontend below the scale floor (10 DOM element(s) < 25) — too little surface to assess accessibility.
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
AXB1 Runtime evidence locked — no reproducible boot — This repository has nothing the runtime tiers could boot or serve — 1 markup document under demonstration, documentation or developer-tool trees (or modules the build marks non-production) but none in the product; no UI framework or web-server dependency, no UI component source, no native UI project and no API definition — those pages are demos or docs for the code, not something this tier can boot — so runtime a11y/egress/header evidence has no subject here. Not applicable: this is neither a gap in the scan nor a finding about your code.
AXB2 Runtime readiness — Advisory — this card reports evidence and never carries a score, so there is nothing missing here.
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 — ~8721 lines of test source are present (.py, .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
D16 Bus Factor — dormant codebase — no living knowledge left to concentrate
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.
D20 ADR Quality — N/A — ADRs are expected on deployable products with a user-facing host, not consumed libraries; no ADR log is required here.
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.
D30 Dependency Vulnerabilities — Not scored — no dependency manifest in a supported ecosystem was read for this repository. A gap in the analyzer's language coverage, NOT a finding that the repository is free of vulnerable dependencies (an sbt build (build.sbt) — not scanned yet).
D31 IaC & Container Security — No Infrastructure-as-Code or container manifests found (Dockerfile, Docker Compose, Terraform, Kubernetes/Helm, CloudFormation, ARM, Bicep, Ansible); nothing to scan.
D32 Data Compliance (PII/GDPR) — 1 file(s) were not parsed by semgrep — the PII/GDPR ruleset never ran over them
D36 Supply-chain Provenance & Signing — No CI/build pipeline found (.github/.forgejo/.gitea workflows, .circleci, .buildkite, .woodpecker, .teamcity, .gitlab-ci.yml, .travis.yml, bitbucket-pipelines.yml, .drone.yml, .cirrus.yml, .woodpecker.yml, appveyor.yml, azure-pipelines*.yml, .pipelines/, .vsts-ci/, .azuredevops/, Jenkinsfile); there is no build to attest provenance for.
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.
D43 Malicious Dependencies — Not scored — no dependency manifest in a supported ecosystem was read for this repository. A gap in the analyzer's language coverage, NOT a finding that the repository is free of vulnerable dependencies (an sbt build (build.sbt) — not scanned yet).
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 — not scored — this repository shows none of the 2 signals this lens looks for
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
GD1 Unfinished & placeholder code — no source files were read — this check reads C#, F#, JavaScript/TypeScript, Dart, Ruby and Scala, and none was read for this repository's product. That is a limit of the analyzer, not a finding about your code.
IC1 Incompleteness & stubs — not analysed — these correctness checks read a source model 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
P12 CI test-gate honesty — no CI workflow found
P2 Observability — This repository's JavaScript/TypeScript, Python, JVM source (3 module(s), 70 file(s) read) declares no entry point and bootstraps no server, and nothing here deploys a service — it is a library, run inside whatever hosts it, so production observability (structured logging, tracing/metrics, health checks) is N/A. If it grows a binary or a service, the dimension reactivates.
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 7,180 production line(s), 5 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.
S1 Web-Security Posture — Not assessed: these web-security 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 web-security 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.
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.
X6 Hand-rolled structured-format parsing — Advisory — this card reports evidence and never carries a score, so there is nothing missing here.
X7 Silent fallback defaults — Advisory — this card reports evidence and never carries a score, so there is nothing missing here.
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 — 2 finding(s)

D28 · Secrets (history) · REDACTED · ×1

REDACTED

D34 · Knowledge Freshness · Orphaned knowledge · ×1

Orphaned knowledge sagemaker-pyspark-sdk/src/sagemaker_pyspark/algorithms/LinearLearnerSageMakerEstimator.py: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.

Serious — 17 finding(s)

D3 · God Classes · TooManyMethods · ×5

TooManyMethods: XGBoostSageMakerEstimator sagemaker-pyspark-sdk/src/sagemaker_pyspark/algorithms/XGBoostSageMakerEstimator.py:23 — TooManyMethods — 73 methods. The bar is 30 methods; this is 43 over it, 2.43× 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: LinearLearnerParams sagemaker-pyspark-sdk/src/sagemaker_pyspark/algorithms/LinearLearnerSageMakerEstimator.py:15 — TooManyMethods — 72 methods. The bar is 30 methods; this is 42 over it, 2.40× 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: FactorizationMachinesParams sagemaker-pyspark-sdk/src/sagemaker_pyspark/algorithms/FactorizationMachinesSageMakerEstimator.py:25 — TooManyMethods — 50 methods. The bar is 30 methods; this is 20 over it, 1.67× 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: LinearLearnerSageMakerEstimator sagemaker-spark-sdk/src/main/scala/com/amazonaws/services/sagemaker/sparksdk/algorithms/LinearLearnerSageMakerEstimator.scala:1055 — TooManyMethods — 38 methods. The bar is 30 methods; this is 8 over it, 1.27× 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: XGBoostSageMakerEstimator sagemaker-spark-sdk/src/main/scala/com/amazonaws/services/sagemaker/sparksdk/algorithms/XGBoostSageMakerEstimator.scala:473 — TooManyMethods — 35 methods. The bar is 30 methods; this is 5 over it, 1.17× 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.

D3 · God Classes · FileTooLong · ×3

FileTooLong: algorithms/LinearLearnerSageMakerEstimator.py sagemaker-pyspark-sdk/src/sagemaker_pyspark/algorithms/LinearLearnerSageMakerEstimator.py — FileTooLong — 766 significant lines (blank, comment-only and punctuation-only lines excluded). The bar is 500 significant lines; this is 266 over it, 1.53× 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: algorithms/LinearLearnerSageMakerEstimator.scala sagemaker-spark-sdk/src/main/scala/com/amazonaws/services/sagemaker/sparksdk/algorithms/LinearLearnerSageMakerEstimator.scala — FileTooLong — 581 significant lines (blank, comment-only and punctuation-only lines excluded). The bar is 500 significant lines; this is 81 over it, 1.16× 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: algorithms/XGBoostSageMakerEstimator.py sagemaker-pyspark-sdk/src/sagemaker_pyspark/algorithms/XGBoostSageMakerEstimator.py — FileTooLong — 501 significant lines (blank, comment-only and punctuation-only lines excluded). The bar is 500 significant lines; this is 1 over it, 1.00× 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.

D2 · Cognitive Complexity · ProtobufConverter.setFeatures (cognitive 26) · ×1

ProtobufConverter.setFeatures (cognitive 26) sagemaker-spark-sdk/src/main/scala/com/amazonaws/services/sagemaker/sparksdk/protobuf/ProtobufConverter.scala:238 — ProtobufConverter.setFeatures has cognitive complexity 26 (threshold 15). Drivers by points: loops 6 (17 pts), if/else 4 (8 pts), match/switch 1 (nesting depth added 15). This file's own header attributes it to another copyright holder, so it is code this repository carries rather than code it wrote: restructuring the body in place forks it from upstream and turns every future re-sync into a manual merge. The performable moves are to leave the body as close to its upstream form as possible and keep it behind a narrow interface of your own, and to re-sync it when upstream changes — or, if it has already diverged far enough that you maintain it here, adopt it deliberately and then split the body into named stages.

D22 · Internal API Consistency · Redundant Type Hierarchy · ×1

Redundant Type Hierarchy: The `CustomNamePolicyWithTimeStampSuffix` class and its corresponding `CustomNamePolicyWithTimeStampSuffixFactory` appear to be functionally identical to `CustomNamePolicy` and `CustomNamePolicyFactory` except for the implicit timestamp suffix behavior. However, the API exposes them as distinct, parallel classes with identical constructor signatures (trainingJobName, modelName, endpointConfigName, endpointName). This forces users to choose between two nearly identical types rather than configuring a single type via a parameter (e.g., `use_timestamp=True`). — Unify into a single `CustomNamePolicy` class that accepts an optional boolean flag `include_timestamp` in its constructor. Remove `CustomNamePolicyWithTimeStampSuffix` and its factory. (signatures: sagemaker_pyspark.NamePolicy.CustomNamePolicyWithTimeStampSuffix | sagemaker_pyspark.NamePolicy.CustomNamePolicyWithTimeStampSuffixFactory)

D4 · Code Duplication · Near-duplicate member family (10 members, 23 shared lines) · ×1

Near-duplicate member family (10 members, 23 shared lines) sagemaker-pyspark-sdk/src/sagemaker_pyspark/SageMakerEstimator.py:443 — sagemaker-pyspark-sdk/src/sagemaker_pyspark/SageMakerEstimator.py:443-477 | sagemaker-pyspark-sdk/src/sagemaker_pyspark/algorithms/FactorizationMachinesSageMakerEstimator.py:467-502 | sagemaker-pyspark-sdk/src/sagemaker_pyspark/algorithms/FactorizationMachinesSageMakerEstimator.py:648-683 | sagemaker-pyspark-sdk/src/sagemaker_pyspark/algorithms/KMeansSageMakerEstimator.py:225-257 | sagemaker-pyspark-sdk/src/sagemaker_pyspark/algorithms/LDASageMakerEstimator.py:194-226 | sagemaker-pyspark-sdk/src/sagemaker_pyspark/algorithms/LinearLearnerSageMakerEstimator.py:696-731 | sagemaker-pyspark-sdk/src/sagemaker_pyspark/algorithms/LinearLearnerSageMakerEstimator.py:928-963 | sagemaker-pyspark-sdk/src/sagemaker_pyspark/algorithms/LinearLearnerSageMakerEstimator.py:1133-1168 | sagemaker-pyspark-sdk/src/sagemaker_pyspark/algorithms/PCASageMakerEstimator.py:186-218 | sagemaker-pyspark-sdk/src/sagemaker_pyspark/algorithms/XGBoostSageMakerEstimator.py:388-420 — These 10 members are variants of one another: a block of 23 lines reported below appears in every one of them, and the pairwise near-duplicate rows they would otherwise produce are collapsed into this row. Read them as one construct written 10 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 10 times.

D4 · Code Duplication · Duplicated block (14 lines × 2) · ×1

Duplicated block (14 lines × 2) sagemaker-spark-sdk/src/main/scala/com/amazonaws/services/sagemaker/sparksdk/SageMakerModel.scala:109 — sagemaker-spark-sdk/src/main/scala/com/amazonaws/services/sagemaker/sparksdk/SageMakerModel.scala:109-122 | sagemaker-spark-sdk/src/main/scala/com/amazonaws/services/sagemaker/sparksdk/SageMakerModel.scala:173-186 — 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 `sagemaker-spark-sdk/src/main/scala/com/amazonaws/services/sagemaker/sparksdk/SageMakerModel.scala:109` 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 (64–65 lines × 2) · ×1

Duplicated block (64–65 lines × 2) sagemaker-pyspark-sdk/src/sagemaker_pyspark/SageMakerModel.py:154 — sagemaker-pyspark-sdk/src/sagemaker_pyspark/SageMakerModel.py:154-217 | sagemaker-pyspark-sdk/src/sagemaker_pyspark/SageMakerModel.py:299-363 — 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. 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.

D4 · Code Duplication · Duplicated block (23 lines × 10) · ×1

Duplicated block (23 lines × 10) sagemaker-pyspark-sdk/src/sagemaker_pyspark/SageMakerEstimator.py:447 — sagemaker-pyspark-sdk/src/sagemaker_pyspark/SageMakerEstimator.py:447-469 | sagemaker-pyspark-sdk/src/sagemaker_pyspark/algorithms/FactorizationMachinesSageMakerEstimator.py:472-494 | sagemaker-pyspark-sdk/src/sagemaker_pyspark/algorithms/FactorizationMachinesSageMakerEstimator.py:653-675 | sagemaker-pyspark-sdk/src/sagemaker_pyspark/algorithms/KMeansSageMakerEstimator.py:227-249 | sagemaker-pyspark-sdk/src/sagemaker_pyspark/algorithms/LDASageMakerEstimator.py:196-218 | sagemaker-pyspark-sdk/src/sagemaker_pyspark/algorithms/LinearLearnerSageMakerEstimator.py:701-723 | sagemaker-pyspark-sdk/src/sagemaker_pyspark/algorithms/LinearLearnerSageMakerEstimator.py:933-955 | sagemaker-pyspark-sdk/src/sagemaker_pyspark/algorithms/LinearLearnerSageMakerEstimator.py:1138-1160 | sagemaker-pyspark-sdk/src/sagemaker_pyspark/algorithms/PCASageMakerEstimator.py:188-210 | sagemaker-pyspark-sdk/src/sagemaker_pyspark/algorithms/XGBoostSageMakerEstimator.py:390-412 — there are 10 copies across 7 file(s) — more copies than files, so at least one file holds the block twice. Extract it once into a single shared function every call site can reach and call it from all 10 sites; resolving a subset leaves the remainder to drift apart. ★ These copies have DRIFTED, and that is worth reading before extracting anything: just after the matched lines, `sagemaker-pyspark-sdk/src/sagemaker_pyspark/algorithms/FactorizationMachinesSageMakerEstimator.py:495` calls `Option` and `sagemaker-pyspark-sdk/src/sagemaker_pyspark/SageMakerEstimator.py:470` 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 (5 lines × 2) · ×1

Duplicated block (5 lines × 2) sagemaker-pyspark-sdk/docs/conf.py:18 — sagemaker-pyspark-sdk/docs/conf.py:18-22 | sagemaker-pyspark-sdk/setup.py:18-22 — 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 (12 lines × 2) · ×1

Duplicated block (12 lines × 2) sagemaker-pyspark-sdk/src/sagemaker_pyspark/algorithms/LDASageMakerEstimator.py:179 — sagemaker-pyspark-sdk/src/sagemaker_pyspark/algorithms/LDASageMakerEstimator.py:179-191 | sagemaker-pyspark-sdk/src/sagemaker_pyspark/algorithms/XGBoostSageMakerEstimator.py:374-385 — 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.

P1 · CI/CD gates · No CI pipeline · ×1

No CI pipeline — No CI workflow found (.github/workflows, azure-pipelines.yml, .gitlab-ci.yml, …) — changes aren't gated by an automated build/test.

Minor — 13 finding(s)

D34 · Knowledge Freshness · Orphaned file · ×3

Orphaned file sagemaker-spark-sdk/src/main/scala/com/amazonaws/services/sagemaker/sparksdk/transformation/util/RequestBatchIterator.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 sagemaker-spark-sdk/build.sbt: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 sagemaker-pyspark-sdk/setup.py: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.

X10 · Duplicated predicate · Duplicated predicate · ×2

Duplicated predicate sagemaker-pyspark-sdk/src/sagemaker_pyspark/algorithms/FactorizationMachinesSageMakerEstimator.py:467 — `'javaObject' in kwargs and kwargs['javaObject'] is not None` appears character-identically in 2 files — sagemaker-pyspark-sdk/src/sagemaker_pyspark/algorithms/FactorizationMachinesSageMakerEstimator.py, sagemaker-pyspark-sdk/src/sagemaker_pyspark/algorithms/LinearLearnerSageMakerEstimator.py. 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 sagemaker-pyspark-sdk/src/sagemaker_pyspark/algorithms/KMeansSageMakerEstimator.py:319 — `isinstance(value, numbers.Real) and value < 1` appears character-identically in 2 files — sagemaker-pyspark-sdk/src/sagemaker_pyspark/algorithms/KMeansSageMakerEstimator.py, sagemaker-pyspark-sdk/src/sagemaker_pyspark/algorithms/LinearLearnerSageMakerEstimator.py. 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.

AX6 · Interface segregation · Fat interface · ×1

Fat interface: RecordOrBuilder (19 abstract methods) sagemaker-spark-sdk/src/main/scala/aialgorithms/proto2/RecordProto2.java:5619 — `RecordOrBuilder` declares 19 abstract methods: `getConfiguration`, `getConfigurationBytes`, `getFeatures`, `getFeaturesCount`, `getFeaturesList`, `getFeaturesOrBuilder`, `getFeaturesOrBuilderList`, `getLabel`, `getLabelCount`, `getLabelList`, `getLabelOrBuilder`, `getLabelOrBuilderList`, `getMetadata`, `getMetadataBytes`, `getUid`, `getUidBytes`, `hasConfiguration`, `hasMetadata`, `hasUid`. Counted over its ABSTRACT methods, once per signature; `default`, `static` and `private` methods and constants 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.

D28 · Secrets (history) · Rotate the exposed credentials · ×1

REDACTED

D34 · Knowledge Freshness · Further orphaned files (smaller) · ×1

Further orphaned files (smaller) — 3 smaller file(s) also have no living knowledge — folded into the freshness score and metrics rather than raised one row each — most significant first: sagemaker-spark-sdk/src/main/scala/com/amazonaws/services/sagemaker/sparksdk/transformation/util/RequestBatchIterator.scala, sagemaker-spark-sdk/build.sbt, sagemaker-pyspark-sdk/setup.py (4 orphaned of 4 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; 4 of the 9 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.

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.

M2 · Architecture documentation · No architecture diagram/doc · ×1

No architecture diagram/doc — No C4/Structurizr/PlantUML/Mermaid/Graphviz/D2 diagram, no drawn diagram named for the architecture, no file named `architecture` or `design` in any markup this check reads, and nothing in the README, docs or contributor guides that announces the shape — no `## Architecture` heading, no "architecture overview"/"high-level design" phrasing, no "the architecture is …" introduction, no guided code tour. A shape laid out in prose that never names itself as the architecture is not visible to this check, and neither is one kept outside the repository, so this row reports the absence of a re-findable shape document — not evidence that nobody wrote the shape down.

M4 · Documentation accuracy · README/code drift · ×1

README/code drift README.md:11 — README advertises Docker containerisation, but no Dockerfile/compose file exists — searched for: `dockerfile`, `docker-compose`, `compose.yaml`, `compose.yml`. Each was matched case- and separator-insensitively against file and directory NAMES anywhere in the tree, and against the CONTENTS of manifest and configuration files (package.json and other *.json, *.csproj, *.props, *.sln/*.slnx, *.yml, *.toml, *.config, tool configs such as eslint.config.js, Dockerfile); the README's own prose never counts, so a claim is never refuted by merely being made. Nothing outside that search was read — a footprint living only in a submodule, in a file type not listed here, or under a name none of those terms matches is not seen, and this row is then wrong.

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) — this repository has no CI pipeline yet, so run it locally to clear the existing findings, then make it a step of the first workflow you add so a regression fails the build. What was searched, so you can tell an absence from a miss: the 0 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.

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 34 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.

Minor — 1 finding(s)

D12 · Dependency Hygiene · Outdated · ×1

Outdated: pyspark — `pyspark` is pinned to 3.3.2, but 4.2.0 is the current stable release on PyPI. An exact pin never moves on its own, so this repository installs 3.3.2 until the declaration is edited. This crosses a major version, so it is a migration rather than a bump.

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.

Dimension	Tool	Version	Command	Findings	Raw output
D28 · Secrets (history)	gitleaks	—	gitleaks detect --no-banner --config /opt/gitleaks-rules/watchdog-gitleaks.toml --report-format json --report-path /tmp/watchdog-gitleaks-023524e3cf34408a8774315c6f0829fa/history.json --exit-code 0 --source .	1	artifacts/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-023524e3cf34408a8774315c6f0829fa/tree.json --exit-code 0 --source .	1	artifacts/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 .	0	artifacts/raw/semgrep.json
D30 · Dependency Vulnerabilities	none (dependency manifest found, not scanned for vulnerabilities here)	—	none (dependency manifest found, not scanned for vulnerabilities here): not applicable — Not scored — no dependency manifest in a supported ecosystem was read for this repository. A gap in the analyzer's language coverage, NOT a finding that the repository is free of vulnerable dependencies (an sbt build (build.sbt) — not scanned yet).	0	—
D31 · IaC & Container Security	trivy	—	trivy: not applicable — No Infrastructure-as-Code or container manifests found (Dockerfile, Docker Compose, Terraform, Kubernetes/Helm, CloudFormation, ARM, Bicep, Ansible); nothing to scan.	0	—
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 1 file(s) — `docs/lib/jquery.js` — so the PII/GDPR sweep did not cover the unparsed regions of them; rows reported elsewhere in those files are real.	0	—
D36 · Supply-chain Provenance & Signing	provenance	—	provenance: not applicable — No CI/build pipeline found (.github/.forgejo/.gitea workflows, .circleci, .buildkite, .woodpecker, .teamcity, .gitlab-ci.yml, .travis.yml, bitbucket-pipelines.yml, .drone.yml, .cirrus.yml, .woodpecker.yml, appveyor.yml, azure-pipelines*.yml, .pipelines/, .vsts-ci/, .azuredevops/, Jenkinsfile); there is no build to attest provenance for.	0	—
D37 · Vulnerability-disclosure Policy	disclosure	—	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 Confinement	runtime-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 Confinement	runtime-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 Enforcement	runtime-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 Dependencies	none (dependency manifest found, not scanned for vulnerabilities here)	—	none (dependency manifest found, not scanned for vulnerabilities here): not applicable — Not scored — no dependency manifest in a supported ecosystem was read for this repository. A gap in the analyzer's language coverage, NOT a finding that the repository is free of vulnerable dependencies (an sbt build (build.sbt) — not scanned yet).	0	—

Run 01a15dc3-14f8-7a76-ac79-be705c1d6dfc · every finding is also locatable in findings.md, and the complete scoring record (with exit codes + durations) in sidecar.json.

Findings by how serious they are

This survey was produced by

Executive summary

Top priorities

Diagnosis: what the measurements show together

Architecture: which parts of the code depend on which

At a glance — Code Health · 72% · Adequate · gated by X5 ·

At a glance — Architecture · 99% · Exemplary ·

At a glance — Maturity · 49% · Weak · gated by D34, M2 ·

At a glance — Readiness · 41% · Weak · gated by P1, P3 ·

At a glance — Security · 97% · Exemplary ·

Security & Compliance — OWASP Top-10 mapping

Roadmap

File quality

How the grades work

Methodology & how to trust this report

Tools & methods

Run transparency — what happened this run

Limitations & what we did not check

Per-dimension blind spots

The LLM boundary

Dimensions

Frontend & cross-cutting dimensions

Reference — by lens

Appendix A — Findings (grouped)

Appendix B — Reproduction & audit trail

Downloadable artifacts