Public report — eeg-self-supervision, published 30 Jul 2026. Concrete security findings (CVE IDs, secret matches, dependency versions) are hidden in this version; ask the repo owner for the full report.
Watchdogby Canine Development 30-07-2026 @ 00:39 UTC Public
Code Health Audit

Neerajwagh/eeg-Self-Supervision

no automated tests
47% Weak
CriticalWeakAdequateStrongExemplary
upper third — near Adequate
Architecture 100% Exemplary Security 99% Exemplary Code Health 94% Exemplary Maturity 51% Adequate Readiness 17% Critical

Small · 3,211 LoC · rebuild ~0.1 person-years · weakest lens: Readiness (17%)

Grounded in facts. Every number here is computed, not narrated — reproducible, tool-backed, and traceable to a line of code. How to trust this ▸

19/21dimensions tool-verifieddeterministic · confidence 1.0 · 2 LLM-assisted, advisory
24findings with an exact file:lineof 33 — the remainder are repo-wide signals (a dimension-level measurement, not a single line); open any file:line and verify
21/94dimensions across the health lenses3211 LoC — wide & deep

Executive summary

Read through the Production lens — the standard calibration. *Green* means good enough to run in production. The score is absolute and comparable across repos.

neerajwagh/eeg-self-supervision carries serious gaps (47%). Several issues below can materially affect correctness, security, or the cost of changing it — and propagate to everything that depends on it.

It is strongest in Architecture (100%) — the structure is clean and changes stay contained. Security (99%) is solid too.

The area that most needs attention is Readiness (17%) — releases are harder to depend on — versioning, release notes and dependency hygiene are thin, so consumers can't easily tell what changed or trust an upgrade. Maturity (51%) is the next concern — onboarding is slow — key decisions and the architecture aren't written down, so contributors have to reverse-engineer the intent.

Leadership focus, highest impact first: CI workflow that builds and runs the test suite on every push/PR (CI/CD gates); 1 No automated tests finding(s) in Code Coverage (Code Coverage); 1 No tests found finding(s) in Test Distribution (Test Distribution).

For scale: Small (~3,211 production lines); rebuilding it from scratch would take roughly ~0.1 person-years (~1 engineer). Approximate, ±~30%.

Encouragingly, the gaps are in documentation and release process — not in the code's correctness, structure or security, which are strong. They're low-risk to close, and doing so would lift the grade without re-engineering anything that already works.

Code Health 94% ★ Exemplary Architecture 100% ★ Exemplary Maturity 51% Adequate · gated by M2 Readiness 17% ⚠ Critical · gated by D8, D9, P1, P3 Security 99% ★ Exemplary
How the score is built — each lens's share of the headline Width is the lens's weight in the worst-heaviest fold (the weakest area pulls hardest); colour is that lens's own band. A lens fixes the score in proportion to its width.
Readiness 17% · 47% weightMaturity 51% · 26% weightCode Health 94% · 14% weightSecurity 99% · 8% weightArchitecture 100% · 4% weight

Raise Readiness 17 → 70 (the Healthy floor) ⇒ headline 47 → ~68.

New since the last scan (4+)

4 finding(s) are new versus the previous scan (2026-07-20) — surfaced by this scheduled scan itself, no pull request required.

  • D4 · Duplicated block (13 lines × 2) Fine-tune/SOTA_pipeline.py
  • D4 · Duplicated block (8 lines × 10) Fine-tune/ablation_models.py
  • D4 · Duplicated block (7 lines × 2) evaluation/linear_baseline_eval.py
  • D4 · Duplicated block (5 lines × 2) evaluation/linear_baseline_eval.py

A full-fidelity diff against the previous run's complete recorded findings — line-move tolerant: a finding that only shifted line counts as unchanged, only genuinely new titles/files surface here.

Rebuild cost & value ~ Modeled — €970–€4,900
Cost to rebuild€970–€4,900 (0.1 person-years (16–51 h), ~1 engineer)
Domain complexityStandard — harder problems cost more per line
Quality factor0.7× (at 47% quality) — the last 20% of quality is most of the work
Size & shapeSmall · effort split not classified (source measured from disk; the effort-tier breakdown is a C#-only syntax walk)

This codebase represents roughly ~0.1 person-years of build effort (about ~€2,900 to rebuild). Its weakest lens is Readiness at 17% — the part of that asset most exposed by the findings below.

How we model this: boilerplate at a scaffolding rate + logic × domain Standard (×1.0) — standard service × a 0.7× quality factor, at €60–95/h; indicative, ±~30% · size measured directly from source · effort from total production LoC as straight-line logic (the tier split is a C#-only syntax walk), a conservative lower bound. Indicative only — most sensitive to the hourly rate and the domain tier (both tunable in config).

Top priorities

The highest-leverage moves; the full ranked list is in the Roadmap below.

1
Resolve the 1 No automated tests finding(s) in Code Coverage.
+20.3 pts · Low effort · Code Coverage
2
Resolve the 1 No tests found finding(s) in Test Distribution.
+20.3 pts · Low effort · Test Distribution
3
Add a CI workflow that builds and runs the test suite on every push/PR.
+28.0 pts · Medium effort · CI/CD gates

Diagnosis — what's actually going on

Value concentrated against a weak lens · High · Value at risk
This is a Small asset (~0.1 person-years to rebuild), and its weakest lens is Readiness at 17%. The operational and business risk on an asset this size concentrates there — that's where remediation buys the most protection.
Evidence: valuation: Small, ~0.1 person-years rebuild (3,211 LoC) · weakest lens: Readiness 17%
→ Direct remediation budget at Readiness first — highest risk-reduction per euro on an asset this size.
Highest-leverage move · Medium · Leverage
Of everything flagged, the best return on effort is: Add a CI workflow that builds and runs the test suite on every push/PR. The rest can wait behind it.
Evidence: priority ranking: top of 5 ranked by impact/effort
→ Add a CI workflow that builds and runs the test suite on every push/PR.

At a glance — Code Health · 94% · Exemplary

The Good
D1 · Python Cyclomatic Complexity 10.0 D2 · Python Cognitive Complexity 9.6 D3 · Python God Classes 10.0 D4 · Python Code Duplication 8.0

At a glance — Architecture · 100% · Exemplary

The Good
D35 · Python Change Coupling 10.0

At a glance — Maturity · 51% · Adequate · gated by M2

The Good
D15 · Python Churn × Complexity Hotspots 10.0 D19 · Python Documentation Quality 8.0 D21 · Python Naming Consistency 10.0 M4 · Repo Documentation accuracy 10.0
The Bad
M1 · Repo Documentation (README) 6.7 M3 · Repo Folder & project structure 6.0
The Ugly
M2 · Repo Architecture documentation 0.0

At a glance — Readiness · 17% · Critical · gated by D8, D9, P1, P3

The Good
D13 · Python Secret Scanning 10.0
The Bad
P4 · Repo Deployment & Rollback 5.0
The Ugly
D8 · Python Code Coverage 0.0 P1 · Repo CI/CD gates 0.0 P3 · Repo Security & performance tooling 0.0

At a glance — Security · 99% · Exemplary

The Good
D28 · Python Secrets (history) 10.0 D29 · Python Static Analysis (SAST) 10.0 D31 · Python IaC & Container Security 9.7

Security & Compliance — OWASP Top-10 mapping

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

OWASP categoryFindingsSeverity
A05:2021 — Security Misconfiguration1High / Critical

Roadmap

First, establish a CI/CD pipeline to automatically build and test every change, ensuring immediate feedback on new code. Next, address the lack of automated tests by resolving the identified coverage gaps and ensuring tests are properly distributed. Then, implement a draft release process to prevent bad builds from reaching users. Finally, begin documenting significant architectural decisions to improve long-term maintainability.

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

Do thisHelpsEffortDimension
Resolve the 1 No automated tests finding(s) in Code Coverage.+20.3 ptsLowCode Coverage
Resolve the 1 No tests found finding(s) in Test Distribution.+20.3 ptsLowTest Distribution
Add a CI workflow that builds and runs the test suite on every push/PR.+28.0 ptsMediumCI/CD gates
Nothing pauses a release for a human: publish as a draft release (or gate the release job on a protected tag/manual dispatch) so a bad build can be stopped before users can download it.+20.3 ptsMediumDeployment & Rollback
Start an ADR log (docs/adr/) recording significant decisions and their rationale.+11.9 ptsMediumArchitecture documentation
Group production code under src/ (or split deliberately, e.g. backend/ + frontend/) so production and tooling code aren't mixed at the root.+10.4 ptsMediumFolder & project structure
Add a 'Testing' section to the root README — how to run the test suite.+8.6 ptsMediumDocumentation (README)
Resolve the 1 The 'How to Fine-tune Existing Pre-trained Models for Downstream Tasks'… finding(s) in Documentation Quality — start with README.md.+2.6 ptsLowDocumentation Quality

File quality

Per-file score 0–10 — a quality signature. Of 8 files carrying findings, judged against the Production bar: 0% slop · 62% mixed · 38% near-clean.

FileScoreBandWorst signal
Fine-tune/ablation_models.py7.0MixedCode Duplication: Duplicated block (19 lines × 2)
Fine-tune/utils.py7.1MixedCode Duplication: Duplicated block (16 lines × 3)
evaluation/linear_baseline_eval.py7.2MixedCognitive Complexity: linear_baseline_eval._eval_downstream_task (cognitive 20)
Fine-tune/SOTA_model.py7.2MixedCode Duplication: Duplicated block (24 lines × 2)
Dockerfile7.2MixedIaC & Container Security: High IaC: DS-0029
evaluation/SOTA_eval.py8.5Near-cleanCode Duplication: Duplicated block (14 lines × 3)
Fine-tune/SOTA_pipeline.py8.5Near-cleanCode Duplication: Duplicated block (13 lines × 2)
README.md9.5Near-cleanDocumentation Quality: The 'How to Fine-tune Existing Pre-trained Models for Downstream Tasks' section is present but clipped mid-sentence; the full guide cannot be verified.

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. 19 of 21 evaluated dimensions are computed purely by tools and static analysis (confidence 1.0); 2 documentation/naming judgement(s) are LLM-assisted and labelled advisory. Overall confidence is 0.4 — 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 — 21 dimensions across the health lenses
D1D2D3D4D8D9D13D15D19D21D28D29D31D35M1M2M3M4P1P3P4

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

How to trust any code-health report — three questions
  1. Can you open the finding? Real findings cite a repo-relative file and line you can open at the cited line — never an absolute scratch path. Here, 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.)
  2. Is there a tool behind the number? Every score below names the method that produced it — Roslyn, git, a scanner, or (for a handful of documentation/naming dimensions) an LLM labelled sampled · advisory — not a narrative.
  3. Does re-running give the same result? Run it again on the same commit and the score — and this report, byte for byte — is identical. A report whose numbers move between runs is describing the run, not the code.

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

Tools & methods

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

MethodBacksVersionEvaluator
Roslyn static analysisComplexity, cohesion, coupling, dead code, API surface, layering5.3.0✓ deterministic
Native secret scannerHardcoded secrets / credentials1.0.0✓ deterministic
jscpdCode duplication—✓ deterministic
Coverage (coverlet / dotnet-coverage)Line & branch coverage10.0.302✓ deterministic
NuGet / dotnetOutdated, vulnerable & deprecated dependencies10.0.302✓ deterministic
git / LibGit2SharpChurn hotspots, knowledge concentration, history2.43.0 · 0.31.0✓ deterministic
gitleaks · semgrep · trivy · checkovSecrets in history, SAST, CVEs, IaC & container, PII / GDPR1.86.0 · 0.69.3✓ deterministic
LLM (sampled · advisory)Documentation quality, ADR conformance, naming — sampled over a bounded sample; advisory, never a deterministic measurementLocal LLM◐ LLM · sampled · advisory

Every finding is locatable in findings.md. Run 019fb076-5e32-770a-96f3-81fa8d2612e7.

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.

  • D30 Dependency Vulnerabilities — scanner not present in this environment — The backing tool was not installed where this scan ran, so this dimension was not scored. Install the tool (or run in the hosted environment, where it is always present) for a graded result.

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 (jscpd) — 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.
  • D8 Code Coverage: Coverage is measured by building and running the test suite inside Watchdog's isolated image — the target repo is never modified, and nothing on your systems runs. So coverage exists only when the suite builds and runs within the inline time budget; one that needs external services, can't build, or exceeds the budget yields no coverage (D8 then degrades to not-measured, not a low score). Line coverage also says nothing about assertion quality.
  • 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.
  • D13 Secret Scanning: Secret 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").
  • 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.
  • 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.
  • 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.
  • 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").
  • D31 IaC & Container Security: IaC scanning checks Dockerfiles/Terraform/Kubernetes against best-practice rules — it cannot see the live cloud account, runtime configuration, or drift between the committed config and what is actually deployed.
  • 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.
  • M4 Documentation accuracy: Onboarding quality is an LLM read of the docs/setup present — it cannot run the onboarding or measure how long a real new joiner takes; the verdict is sampled and advisory.
  • P4 Deployment & Rollback: Approval/branch-protection rules live in repository settings the scan cannot see — only their in-repo evidence (config files, workflows) is checked, so a control enforced purely in the host's settings reads as "not evidenced".

The LLM boundary

LLM-set scores this run (3): D19, D21, M4 (model: Local LLM). For these, a model reads a bounded sample and sets the numeric score (documentation, ADR quality, naming, comment value, onboarding) — D25 sets the ADR-conformance fraction over sampled code, D22 judges API accuracy over a sample. These 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.

D2 · Cognitive Complexity9.6 / 10Exemplary✓ 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 9.6 / 10 · rule-coverage 100% · ceiling Prevented

1 method(s) exceeded the cognitive complexity threshold of 15; the worst was linear_baseline_eval._eval_downstream_task at 20.

linear_baseline_eval._eval_downstream_task (cognitive 20)evaluation/linear_baseline_eval.py:87

✓ On the Gold path — maintain.

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

D3 · God Classes10.0 / 10Exemplary✓ 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 10.0 / 10 · rule-coverage 100% · ceiling Prevented

0 god class(es) detected.

✓ On the Gold path — maintain.

Detailed fixes: d3_recommendation.md.

D4 · Code Duplication8.0 / 10Strong✓ 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 8.0 / 10 · rule-coverage 100% · ceiling Verified

21 duplicated block group(s) detected.

Duplicated block (12 lines × 2) · ×3Fine-tune/ablation_models.py:9
Duplicated block (13 lines × 2) · ×2Fine-tune/SOTA_pipeline.py:129
Duplicated block (11 lines × 2) · ×2Fine-tune/SOTA_model.py:99
Duplicated block (24 lines × 2)Fine-tune/SOTA_model.py:48
Duplicated block (21 lines × 2)Fine-tune/SOTA_model.py:279

+ 12 more group(s) — more in Appendix A; the complete list is findings.md.

What to do

  1. Resolve the 3 Duplicated block (12 lines × 2) finding(s) in Code Duplication — start with ablation_models.py (2), linear_baseline_eval.py. — One of this dimension's main actionable groups (3 warning-level).
  2. Resolve the 2 Duplicated block (13 lines × 2) finding(s) in Code Duplication — start with SOTA_pipeline.py, ablation_models.py. — One of this dimension's main actionable groups (2 warning-level).
  3. Resolve the 2 Duplicated block (11 lines × 2) finding(s) in Code Duplication — start with SOTA_model.py, ablation_models.py. — One of this dimension's main actionable groups (2 warning-level).
  4. 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.

D8 · Code Coverage0.0 / 10Critical✓ Tool-verified

What it measures: How much of the code is actually exercised by tests.

Method: Coverage from coverlet runs or committed reports (Cobertura/OpenCover/lcov), computed per-file with structured exclusions for generated, trivial, and glue code. When the suite can't be built/run in-image AND no report is committed, coverage is reported NOT-MEASURED (excluded from the score) with the precondition to make it measurable — never a LoC-ratio proxy folded in as if measured. Deterministic.

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

No automated tests — no test code was found in this repository.

No automated tests

What to do

  1. Resolve the 1 No automated tests finding(s) in Code Coverage. — One of this dimension's main actionable groups (1 issue-level).
  2. Stand up a CI pipeline, then gate Code Coverage 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: d8_recommendation.md · top locations in Appendix A, every location in findings.md.

D9 · Test Distribution0.0 / 10Critical✓ 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 0.0 / 10 · rule-coverage 100% · ceiling Documented

No test suite found.

No tests found

What to do

  1. Resolve the 1 No tests found finding(s) in Test Distribution. — One of this dimension's main actionable groups (1 recommendation-level).

Detailed fixes: d9_recommendation.md · top locations in Appendix A, every location in findings.md.

D13 · Secret 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

Secret scan ran and found no leaked secrets.

✓ On the Gold path — maintain.

Detailed fixes: d13_recommendation.md.

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.

git history depth insufficient

✓ On the Gold path — maintain.

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

D19 · Documentation Quality / 10Strong◐ 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 single README is a well-written research paper with an excellent work-acceptance banner and a detailed table of resources (ArXiv pre-print, PMLR paper, ML4H poster, video, slides, Box models/feature storage, MPI LEMON raw data). It also lists installation, a mapping between the paper's model names and code, and a section outline that is fully present in the visible text. The body is clipped mid-sentence ('SOTA refer') so further sections cannot be confirmed; the quality of the README as an installable documentation guide is constrained by its single file.

The 'How to Fine-tune Existing Pre-trained Models for Downstream Tasks' section is present but clipped mid-sentence; the full guide cannot be verified.README.md

What to do

  1. Resolve the 1 The 'How to Fine-tune Existing Pre-trained Models for Downstream Tasks'… finding(s) in Documentation Quality — start with README.md. — One of this dimension's main actionable groups (1 recommendation-level).

Detailed fixes: d19_recommendation.md · top locations in Appendix A, every location in findings.md.

D21 · Naming Consistency / 10Exemplary◐ 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.

D28 · Secrets (history)10.0 / 10Exemplary○ Nothing flagged

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

Method: Git-history secret scan via gitleaks detect over full history in an isolated checkout; each match flagged High. Exhaustive; degrades cleanly when tool absent.

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

gitleaks scanned the full history AND the current working tree and found no secrets.

✓ On the Gold path — maintain.

Detailed fixes: d28_recommendation.md.

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.

✓ On the Gold path — maintain.

Detailed fixes: d29_recommendation.md.

D31 · IaC & Container Security9.7 / 10Exemplary✓ Tool-verified

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

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

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

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

High IaC: DS-0029Dockerfiledetected by trivy finding

✓ On the Gold path — maintain.

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

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; coupling through a build step, config, or non-source file isn't seen.

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

No strong hidden change-coupling between production files.

git history depth insufficient

✓ On the Gold path — maintain.

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

Frontend & cross-cutting dimensions

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

M1 · Documentation (README)6.7 / 10Adequate✓ 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

  • Add a 'Testing' section to the root README — how to run the test suite.
  • Add an 'Architecture' / 'How it works' section to the root README — the high-level shape.
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 — decisions aren't captured for future maintainers.
  • No C4/PlantUML/Mermaid diagram or architecture.md — the high-level shape isn't documented.

What to do

  • Start an ADR log (docs/adr/) recording significant decisions and their rationale.
  • Add a C4 context/container diagram (Structurizr, PlantUML or Mermaid) or an architecture.md overview.
M3 · Folder & project structure6.0 / 10Adequate✓ 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.

  • Production code isn't grouped under a src/ folder — it's spread across several top-level directories, so there's no one place that says 'this is the product'.
  • Tests aren't grouped in a dedicated test folder — the test surface isn't separable from production code at a glance.

What to do

  • Group production code under src/ (or split deliberately, e.g. backend/ + frontend/) so production and tooling code aren't mixed at the root.
  • Group tests in the folder your build system expects (tests/, test/, spec/, or your module's test source set) so the test surface is discoverable and CI can scope it.
M4 · Documentation accuracy10.0 / 10Exemplary◐ Sampled · advisory

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

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

P1 · CI/CD 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.
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 bandit, `semgrep --config=p/python`, or CodeQL's python pack as a CI step.

What to do

  • Add a SAST step to CI running what this repository's stack ships: bandit, `semgrep --config=p/python`, or CodeQL's python pack — so a security regression fails the build instead of landing.
  • Enable Dependabot/Renovate or a dependency-review gate.
  • Add gitleaks/trufflehog in CI to block PRs that introduce committed secrets.
P4 · Deployment & Rollback5.0 / 10Adequate✓ Tool-verified

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

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

What to do

  • Nothing pauses a release for a human: publish as a draft release (or gate the release job on a protected tag/manual dispatch) so a bad build can be stopped before users can download it.

Reference — by lens

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

LensScoreRatingImpact
Code Health94%ExemplarySolid.
Architecture100%ExemplaryStrongest area.
Maturity51%Adequate — gated by M2Capped at Fair by a Critical contributor — resolve it before relying on this lens.
Readiness17%Critical — gated by D8, D9, P1, P3Capped at Fair by a Critical contributor — resolve it before relying on this lens.
Security99%ExemplarySolid.
Not included — 73 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 — No web markup found — accessibility is not applicable to this repository.
  • AC2 Forms & labels — No web markup found — accessibility is not applicable to this repository.
  • AC3 Page structure — No web markup found — accessibility is not applicable to this repository.
  • AC4 Keyboard semantics — No web markup found — accessibility is not applicable to this repository.
  • AC5 ARIA correctness — No web markup found — accessibility is not applicable to this repository.
  • AC6 Visual & motion safety — No web markup found — accessibility is not applicable to this repository.
  • AC7 A11y enforcement — No web markup found — accessibility is not applicable to this repository.
  • AX1 Captive dependencies — no DI registrations detected
  • AX10 Code composition — not assessed — code composition is computed by ROLE over the .NET document set and none was loaded for this repository, because it is written in another language or the solution failed to load. This is a gap in the analyzer, not a finding about this repository
  • AX2 Stateful singletons — no singleton implementations detected
  • AX3 Project dependency cycles — not assessed — project cycles and dependency direction are computed over the .NET project-reference graph and none was loaded for this repository, because it is written in another language or the solution failed to load. This is a gap in the analyzer, not a finding about this repository
  • AX4 Dependency direction — not assessed — project cycles and dependency direction are computed over the .NET project-reference graph and none was loaded for this repository, because it is written in another language or the solution failed to load. This is a gap in the analyzer, not a finding about this repository
  • AX5 Architecture & structure — not assessed — architecture style/structure is computed from the .NET project graph (projects, types, namespaces) and no such graph was loaded for this repository, because it is written in another language or the solution failed to load. This is a gap in the analyzer, not a finding about this repository
  • AX6 Interface segregation — not assessed — interface segregation is computed over the .NET type surface and none was loaded for this repository, because it is written in another language or the solution 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 — not assessed — test isolation is computed from the .NET project graph (which projects are test projects, and what they reference) and no such graph was loaded for this repository, because it is written in another language or the solution failed to load. This is a gap in the analyzer, not a finding about this repository
  • AX9 CQS / query purity — no CQRS query handlers detected — query purity is not applicable to this codebase
  • AXB2 Runtime readiness — no data
  • C1 Data Protection — Not assessed: these personal data controls are read from C# source (attributes, middleware, entity/column names, guard methods) and no C# source was loaded for this repository — because it is written in another language, or the solution failed to load. Absence of a .NET idiom 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 C# source (attributes, middleware, entity/column names, guard methods) and no C# source was loaded for this repository — because it is written in another language, or the solution failed to load. Absence of a .NET idiom 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.
  • C3 Audit Trail — Not assessed: these audit controls are read from C# source (attributes, middleware, entity/column names, guard methods) and no C# source was loaded for this repository — because it is written in another language, or the solution failed to load. Absence of a .NET idiom 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 C# source (attributes, middleware, entity/column names, guard methods) and no C# source was loaded for this repository — because it is written in another language, or the solution failed to load. Absence of a .NET idiom 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 C# source (attributes, middleware, entity/column names, guard methods) and no C# source was loaded for this repository — because it is written in another language, or the solution failed to load. Absence of a .NET idiom 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.
  • D10 Test Quality — No tests were found in the analyzed repository to assess for quality.
  • D11 Test Reliability — Test reliability not included
  • D12 Dependency Hygiene — Dependency hygiene not measured — no supported dependency manifest was read
  • D14 License Compliance — Not scored — this repository's package manifest is not parsed for licence data yet. A gap in the analyzer's language coverage, NOT a finding that the repository's licenses are compliant (a Python pyproject.toml/requirements.txt (pip/uv/Poetry), a Swift Package.swift/Package.resolved, a Cargo manifest, a Go module (go.mod/go.sum), a Gradle version catalogue, a Maven POM, an sbt build (build.sbt), composer.json, package.json, a Dart pubspec.yaml, a rebar.config / erlang.mk DEPS (Hex), a Ruby Gemfile/Gemfile.lock or .gemspec (Bundler/RubyGems)), which this pass does not parse yet — so this dimension asserts nothing about this repository's licensing in either direction.
  • D16 Bus Factor — early-stage repository — too few commits for a meaningful bus factor
  • D17 Explicit Debt — explicit-debt markers are read through a C# workspace today, so they were not read for this repository's language — this asserts nothing about how many markers the code carries. Not scored — this is a gap in the analyzer, not a finding about this repository
  • 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.
  • D22 Internal API Consistency — No exposed public API
  • D23 Boundary Type-Coupling — Production source is present (.py) but bounded contexts are resolved over the C#/VB project set, which exposed none, so context scope could not be assessed. Not scored — this is a gap in the analyzer, not a verdict about this repository. Declaring the codebase's bounded contexts (≥2) would let cross-boundary type coupling be assessed — see the recommendation on this dimension for where. Declare them in `.codehealth/config.yaml` at the repository root (create it if absent), mapping each context name to the namespace prefixes that belong to it — e.g. `architecture:` → `contexts:` → `Billing: ["Acme.Billing"]`, `Catalog: ["Acme.Catalog"]`.
  • D24 Comment Value — No inline comments to assess — comment value is not applicable here.
  • D25 ADR Conformance — no ADRs to check
  • D26 Project Cohesion — Project cohesion is assessed over the .NET project set; this target exposed no projects, so project size and spread could not be assessed. Not scored — this is a gap in the analyzer's reach, not a verdict about this repository.
  • D27 Navigability — No calls could be sampled, so navigability was not assessed — tracing effort is measured over resolved call sites and this target exposed none. Not scored — this is a gap in the analyzer's reach, 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 (a Python pyproject.toml/requirements.txt (pip/uv/Poetry), a Swift Package.swift/Package.resolved, a Cargo manifest, a Go module (go.mod/go.sum), a Gradle version catalogue, a Maven POM, an sbt build (build.sbt), composer.json, package.json, a Dart pubspec.yaml, a rebar.config / erlang.mk DEPS (Hex), a Ruby Gemfile/Gemfile.lock or .gemspec (Bundler/RubyGems) — not scanned yet) — where an OSV-supported manifest exists, dependency vulnerabilities for this repository are reported under D38 instead.
  • D32 Data Compliance (PII/GDPR) — No PII/GDPR ruleset is bundled (the public p/gdpr semgrep pack was retired) — data compliance is not assessed in this scan.
  • D33 JS/npm Dependency Vulnerabilities — No JS/npm manifest or lockfile found outside build output (package.json, package-lock.json, yarn.lock, pnpm-lock.yaml, bun.lockb); no JS dependencies to scan.
  • D34 Knowledge Freshness — early-stage repository — too little history to judge knowledge freshness
  • D36 Supply-chain Provenance & Signing — No CI/build pipeline found (.github/.forgejo/.gitea workflows, .gitlab-ci.yml, azure-pipelines.yml, Jenkinsfile, .circleci); 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.
  • D38 OSV Dependency Vulnerabilities — No supported non-.NET dependency lockfile found outside build output (npm package-lock/yarn/pnpm/bun, Go go.mod, Rust Cargo.lock, Maven pom.xml, Gradle lockfiles, Python requirements.txt/poetry.lock/Pipfile.lock/pdm.lock, PHP composer.lock, Ruby Gemfile.lock, Elixir mix.lock, Dart pubspec.lock, Swift Package.resolved); nothing for OSV to scan. A NuGet-only repo stays NotApplicable — .NET CVEs are D30's domain.
  • 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.
  • D5 Coupling — Inter-project coupling could not be assessed — no analyzable project graph was found for this repository. Not scored: a gap in the analyzer's reach, not a verdict about this repository. (Coupling here is Martin afferent/efferent/instability plus reference cycles across a project-reference graph, read today from .NET project files; other ecosystems' module graphs are not read yet.)
  • D6 Cohesion (LCOM4) — Cohesion (LCOM4) is measured over a C#/VB class graph, and this repository's production source is .py, which this pass does not read — so no class could be assessed. Not scored — this is a gap in the analyzer, not a finding about this repository.
  • D7 Architectural Integrity — no checkable ADRs and no dependency cycles — architectural integrity not assessed
  • DM1 Domain Modelling — not scored — this repository shows none of the 3 signals this check looks for
  • ED1 Event-Driven — not scored — this repository shows none of the 3 signals this check 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 check looks for
  • GD1 Unfinished & placeholder code — no source files
  • IC1 Incompleteness & stubs — not analysed — these correctness checks are read from C# source and none was loaded for this repository, because it is written in another language or the solution 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 — Observability was not assessed: this check reads a source model that does not carry this repository's product — because the repository is written in a language this check does not yet model, or because its projects failed to load. Absence of a logging idiom this check recognises is NOT evidence that this repo lacks structured logging (it may log through its own ecosystem's logger). This is a gap in the analyzer, not a finding about this repository.
  • P5 DR & Backup — not evidenced — repo shows no backup/RTO/RPO controls; absence of evidence is not evidence of a working control
  • P6 Release Hygiene — not evidenced — no changelog, version stamp or semver release tag in the repo
  • P7 Outbound HTTP resilience — not applicable — this isn't a service/API/worker
  • P8 Schema migrations — not assessed — schema-migration practice is read from 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
  • P9 Domain vs controller coverage — no coverage report found on disk — produce a coverage report in a standard format (`coverage run -m pytest` then `coverage xml`) into the repo working tree before the scan — a CI step is the usual place, since the artefact is commonly gitignored, or wire coverage collection into CI, to enable this cross-layer check
  • PF1 Benchmark discipline — Performance was not assessed: this lens reads a source model that was not loaded for this repository, because the repository is written in a language this lens does not yet model or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository — in particular it is NOT a statement that this repo is unpackaged or performance-careless.
  • PF2 Allocation hygiene — Performance was not assessed: this lens reads a source model that was not loaded for this repository, because the repository is written in a language this lens does not yet model or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository — in particular it is NOT a statement that this repo is unpackaged or performance-careless.
  • PF3 Async & latency hygiene — Performance was not assessed: this lens reads a source model that was not loaded for this repository, because the repository is written in a language this lens does not yet model or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository — in particular it is NOT a statement that this repo is unpackaged or performance-careless.
  • S1 Web-Security Posture — Not assessed: these web-security controls are read from C# source (attributes, middleware, entity/column names, guard methods) and no C# source was loaded for this repository — because it is written in another language, or the solution failed to load. Absence of a .NET idiom 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.
  • X1 Async correctness — not analysed — these correctness checks are read from C# source and none was loaded for this repository, because it is written in another language or the solution failed to load. This is a gap in the analyzer, not a finding about this repository
  • X2 Cancellation propagation — not analysed — these correctness checks are read from C# source and none was loaded for this repository, because it is written in another language or the solution failed to load. This is a gap in the analyzer, not a finding about this repository
  • X3 Exception handling — not analysed — these correctness checks are read from C# source and none was loaded for this repository, because it is written in another language or the solution failed to load. This is a gap in the analyzer, not a finding about this repository
  • X4 Structured logging — not analysed — these correctness checks are read from C# source and none was loaded for this repository, because it is written in another language or the solution failed to load. This is a gap in the analyzer, not a finding about this repository
  • X5 Nullable reference types — not analysed — these correctness checks are read from C# source and none was loaded for this repository, because it is written in another language or the solution failed to load. This is a gap in the analyzer, not a finding about this repository

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.

Issue — 2 finding(s)
D31 · IaC & Container Security · High IaC · ×1
  • High IaC: DS-0029 Dockerfile — 'apt-get' missing '--no-install-recommends'
D8 · Code Coverage · No automated tests · ×1
  • No automated tests — No automated tests — no test code was found in this repository. Untested code is the largest single risk to changing it safely.
Warning — 22 finding(s)
D4 · Code Duplication · Duplicated block (12 lines × 2) · ×3
  • Duplicated block (12 lines × 2) Fine-tune/ablation_models.py:9 — Fine-tune/ablation_models.py:9-20 | evaluation/ablation_models.py:9-20 — the copies span different directories, so extracting a shared function means choosing where it lives: put it wherever the callers may both depend on (the module they already share, or a small common one if they share none) and call it from each site — until then, every change has to be made twice.
  • Duplicated block (12 lines × 2) Fine-tune/ablation_models.py:57 — Fine-tune/ablation_models.py:57-68 | evaluation/ablation_models.py:57-68 — the copies span different directories, so extracting a shared function means choosing where it lives: put it wherever the callers may both depend on (the module they already share, or a small common one if they share none) and call it from each site — until then, every change has to be made twice.
  • Duplicated block (12 lines × 2) evaluation/linear_baseline_eval.py:109 — evaluation/linear_baseline_eval.py:109-120 | supervised_learning/linear_baseline_train.py:36-47 — the copies span different directories, so extracting a shared function means choosing where it lives: put it wherever the callers may both depend on (the module they already share, or a small common one if they share none) and call it from each site — until then, every change has to be made twice.
D4 · Code Duplication · Duplicated block (13 lines × 2) · ×2
  • Duplicated block (13 lines × 2) Fine-tune/SOTA_pipeline.py:129 — Fine-tune/SOTA_pipeline.py:129-141 | Fine-tune/ablation_pipeline.py:127-139 — the copies sit in sibling files of one directory: extract the block into a single shared function in that directory and call it from each site, so a change lands once.
  • Duplicated block (13 lines × 2) Fine-tune/ablation_models.py:171 — Fine-tune/ablation_models.py:171-183 | evaluation/ablation_models.py:171-183 — the copies span different directories, so extracting a shared function means choosing where it lives: put it wherever the callers may both depend on (the module they already share, or a small common one if they share none) and call it from each site — until then, every change has to be made twice.
D4 · Code Duplication · Duplicated block (11 lines × 2) · ×2
  • Duplicated block (11 lines × 2) Fine-tune/SOTA_model.py:99 — Fine-tune/SOTA_model.py:99-109 | evaluation/SOTA_model.py:99-109 — the copies span different directories, so extracting a shared function means choosing where it lives: put it wherever the callers may both depend on (the module they already share, or a small common one if they share none) and call it from each site — until then, every change has to be made twice.
  • Duplicated block (11 lines × 2) Fine-tune/ablation_models.py:114 — Fine-tune/ablation_models.py:114-124 | evaluation/ablation_models.py:114-124 — the copies span different directories, so extracting a shared function means choosing where it lives: put it wherever the callers may both depend on (the module they already share, or a small common one if they share none) and call it from each site — until then, every change has to be made twice.
D2 · Cognitive Complexity · linear_baseline_eval._eval_downstream_task (cognitive 20) · ×1
  • linear_baseline_eval._eval_downstream_task (cognitive 20) evaluation/linear_baseline_eval.py:87 — linear_baseline_eval._eval_downstream_task has cognitive complexity 20 (threshold 15). Drivers by points: if/else 15, loops 4, boolean chains 1 (nesting depth added 8). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
D4 · Code Duplication · Duplicated block (24 lines × 2) · ×1
  • Duplicated block (24 lines × 2) Fine-tune/SOTA_model.py:48 — Fine-tune/SOTA_model.py:48-71 | evaluation/SOTA_model.py:48-71 — the copies span different directories, so extracting a shared function means choosing where it lives: put it wherever the callers may both depend on (the module they already share, or a small common one if they share none) and call it from each site — until then, every change has to be made twice.
D4 · Code Duplication · Duplicated block (21 lines × 2) · ×1
  • Duplicated block (21 lines × 2) Fine-tune/SOTA_model.py:279 — Fine-tune/SOTA_model.py:279-299 | evaluation/SOTA_model.py:279-299 — the copies span different directories, so extracting a shared function means choosing where it lives: put it wherever the callers may both depend on (the module they already share, or a small common one if they share none) and call it from each site — until then, every change has to be made twice.
D4 · Code Duplication · Duplicated block (19 lines × 2) · ×1
  • Duplicated block (19 lines × 2) Fine-tune/ablation_models.py:258 — Fine-tune/ablation_models.py:258-276 | evaluation/ablation_models.py:258-276 — the copies span different directories, so extracting a shared function means choosing where it lives: put it wherever the callers may both depend on (the module they already share, or a small common one if they share none) and call it from each site — until then, every change has to be made twice.
D4 · Code Duplication · Duplicated block (18 lines × 2) · ×1
  • Duplicated block (18 lines × 2) Fine-tune/SOTA_model.py:181 — Fine-tune/SOTA_model.py:181-198 | evaluation/SOTA_model.py:181-198 — the copies span different directories, so extracting a shared function means choosing where it lives: put it wherever the callers may both depend on (the module they already share, or a small common one if they share none) and call it from each site — until then, every change has to be made twice.
D4 · Code Duplication · Duplicated block (16 lines × 3) · ×1
  • Duplicated block (16 lines × 3) Fine-tune/utils.py:111 — Fine-tune/utils.py:111-126 | evaluation/utils.py:111-126 | supervised_learning/utils.py:111-126 — the copies span different directories, so extracting a shared function means choosing where it lives: put it wherever the callers may both depend on (the module they already share, or a small common one if they share none) and call it from each site — until then, every change has to be made twice.
D4 · Code Duplication · Duplicated block (15 lines × 3) · ×1
  • Duplicated block (15 lines × 3) Fine-tune/utils.py:71 — Fine-tune/utils.py:71-85 | evaluation/utils.py:71-85 | supervised_learning/utils.py:71-85 — the copies span different directories, so extracting a shared function means choosing where it lives: put it wherever the callers may both depend on (the module they already share, or a small common one if they share none) and call it from each site — until then, every change has to be made twice.
D4 · Code Duplication · Duplicated block (14 lines × 3) · ×1
  • Duplicated block (14 lines × 3) evaluation/SOTA_eval.py:44 — evaluation/SOTA_eval.py:44-57 | evaluation/ablation_models_eval.py:46-59 | evaluation/linear_baseline_eval.py:18-31 — the copies sit in sibling files of one directory: extract the block into a single shared function in that directory and call it from each site, so a change lands once.
D4 · Code Duplication · Duplicated block (13 lines × 3) · ×1
  • Duplicated block (13 lines × 3) Fine-tune/utils.py:34 — Fine-tune/utils.py:34-46 | evaluation/utils.py:34-46 | supervised_learning/utils.py:34-46 — the copies span different directories, so extracting a shared function means choosing where it lives: put it wherever the callers may both depend on (the module they already share, or a small common one if they share none) and call it from each site — until then, every change has to be made twice.
D4 · Code Duplication · Duplicated block (12 lines × 3) · ×1
  • Duplicated block (12 lines × 3) Fine-tune/utils.py:18 — Fine-tune/utils.py:18-29 | evaluation/utils.py:18-29 | supervised_learning/utils.py:18-29 — the copies span different directories, so extracting a shared function means choosing where it lives: put it wherever the callers may both depend on (the module they already share, or a small common one if they share none) and call it from each site — until then, every change has to be made twice.
D4 · Code Duplication · Duplicated block (8 lines × 10) · ×1
  • Duplicated block (8 lines × 10) Fine-tune/ablation_models.py:27 — Fine-tune/ablation_models.py:27-34 | Fine-tune/ablation_models.py:90-98 | Fine-tune/ablation_models.py:127-134 | Fine-tune/ablation_models.py:230-237 | Fine-tune/ablation_models.py:331-338 | evaluation/ablation_models.py:27-34 | evaluation/ablation_models.py:90-98 | evaluation/ablation_models.py:127-134 | evaluation/ablation_models.py:230-237 | evaluation/ablation_models.py:331-338 — the copies span different directories, so extracting a shared function means choosing where it lives: put it wherever the callers may both depend on (the module they already share, or a small common one if they share none) and call it from each site — until then, every change has to be made twice.
D4 · Code Duplication · Duplicated block (7 lines × 3) · ×1
  • Duplicated block (7 lines × 3) Fine-tune/utils.py:51 — Fine-tune/utils.py:51-57 | evaluation/utils.py:51-57 | supervised_learning/utils.py:51-57 — the copies span different directories, so extracting a shared function means choosing where it lives: put it wherever the callers may both depend on (the module they already share, or a small common one if they share none) and call it from each site — until then, every change has to be made twice.
D4 · Code Duplication · Duplicated block (7 lines × 2) · ×1
  • Duplicated block (7 lines × 2) evaluation/linear_baseline_eval.py:179 — evaluation/linear_baseline_eval.py:179-186 | evaluation/linear_baseline_eval.py:208-214 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
D4 · Code Duplication · Duplicated block (6 lines × 2) · ×1
  • Duplicated block (6 lines × 2) Fine-tune/ablation_models.py:152 — Fine-tune/ablation_models.py:152-157 | evaluation/ablation_models.py:152-157 — the copies span different directories, so extracting a shared function means choosing where it lives: put it wherever the callers may both depend on (the module they already share, or a small common one if they share none) and call it from each site — until then, every change has to be made twice.
D4 · Code Duplication · Duplicated block (5 lines × 2) · ×1
  • Duplicated block (5 lines × 2) evaluation/linear_baseline_eval.py:169 — evaluation/linear_baseline_eval.py:169-173 | evaluation/linear_baseline_eval.py:196-202 — 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.
Recommendation — 5 finding(s)
D11 · Test Reliability · Test reliability not included · ×1
  • Test reliability not included — No test suite was found, so reliability couldn't be assessed.
D16 · Bus Factor · early-stage repository · ×1
  • early-stage repository — too few commits for a meaningful bus factor — early-stage repository — too few commits for a meaningful bus factor (2 author(s) across 12 commit(s) sampled).
D19 · Documentation Quality · The 'How to Fine-tune Existing Pre-trained Models for Downstream Tasks' section is present but clipped mid-sentence; the full guide cannot be verified. · ×1
  • The 'How to Fine-tune Existing Pre-trained Models for Downstream Tasks' section is present but clipped mid-sentence; the full guide cannot be verified. README.md — Add a short code example showing how to fine-tune a pre-trained model on the downstream task, so this step is fully visible.
D34 · Knowledge Freshness · early-stage repository · ×1
  • early-stage repository — too little history to judge knowledge freshness — early-stage repository — too little history to judge knowledge freshness (12 commit(s) sampled).
D9 · Test Distribution · No tests found · ×1
  • No tests found — No test suite could be collected — nothing here references a test framework (xUnit/NUnit/MSTest, Jest/Vitest, pytest, Go testing, JUnit, …), so there were no discoverable tests to count. Tests written as plain executables or shell/PowerShell harnesses are not collectible this way and are not scored here.
Info — 4 finding(s)
D12 · Dependency Hygiene · Dependency hygiene not measured · ×1
  • Dependency hygiene not measured — no supported dependency manifest was read — No dependency manifest this pass reads for hygiene (a Python pyproject.toml/requirements.txt (pip/uv/Poetry), a Swift Package.swift/Package.resolved, a Cargo manifest, a Go module (go.mod/go.sum), a Gradle version catalogue, a Maven POM, an sbt build (build.sbt), composer.json, package.json, a Dart pubspec.yaml, a rebar.config / erlang.mk DEPS (Hex), a Ruby Gemfile/Gemfile.lock or .gemspec (Bundler/RubyGems)) was found in this repository, so no package was assessed. Zero packages read is NOT a clean dependency tree, so this is NOT SCORED — a gap in the analyzer, not a verdict about this repository. This row is about dependency HYGIENE — outdated, deprecated or unmaintained direct dependencies; known CVEs in the same dependency graph are a separate question, reported under D38 wherever the manifest is OSV-readable.
D15 · Churn × Complexity Hotspots · git history depth insufficient · ×1
  • git history depth insufficient — git history depth insufficient — install a full clone for reliable trend signal.
D22 · Internal API Consistency · No exposed public API · ×1
  • No exposed public API — No intentionally-exposed types (IsPackable or .Contracts) to evaluate.
D35 · Change Coupling · git history depth insufficient · ×1
  • git history depth insufficient — git history depth insufficient — a full clone gives reliable change-coupling.

Appendix B — Reproduction & audit trail

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

DimensionToolVersionCommandFindingsRaw output
D28 · Secrets (history)gitleaks—gitleaks detect --no-banner --report-format json --report-path /dev/stdout --exit-code 0 --source .0artifacts/raw/gitleaks-history.json
D29 · Static Analysis (SAST)semgrep—semgrep --config /opt/semgrep-rules/security-audit.yml --config /opt/semgrep-rules/owasp-top-ten.yml --json --quiet --timeout 0 --metrics off .0artifacts/raw/semgrep.json
D30 · Dependency Vulnerabilitiesnone (no readable dependency manifest)—none (no readable dependency manifest): not present in this environment0—
D31 · IaC & Container Securitytrivy—trivy config --format json --quiet .1artifacts/raw/trivy-config.json
D32 · Data Compliance (PII/GDPR)semgrep—semgrep: not applicable — No PII/GDPR ruleset is bundled (the public p/gdpr semgrep pack was retired) — data compliance is not assessed in this scan.0—
D33 · JS/npm Dependency Vulnerabilitiestrivy—trivy: not applicable — No JS/npm manifest or lockfile found outside build output (package.json, package-lock.json, yarn.lock, pnpm-lock.yaml, bun.lockb); no JS dependencies to scan.0—
D36 · Supply-chain Provenance & Signingprovenance—provenance: not applicable — No CI/build pipeline found (.github/.forgejo/.gitea workflows, .gitlab-ci.yml, azure-pipelines.yml, Jenkinsfile, .circleci); there is no build to attest provenance for.0—
D37 · Vulnerability-disclosure Policydisclosure—disclosure: not applicable — No vulnerability-disclosure policy file found (SECURITY.md/.markdown/.rst/.txt at root or under .github/.forgejo/.gitea/docs, .well-known/security.txt). A coordinated-disclosure policy may live off-repo, so this is not evidenced rather than failed.0—
D38 · OSV Dependency Vulnerabilitiesosv-scanner—osv-scanner: not applicable — No supported non-.NET dependency lockfile found outside build output (npm package-lock/yarn/pnpm/bun, Go go.mod, Rust Cargo.lock, Maven pom.xml, Gradle lockfiles, Python requirements.txt/poetry.lock/Pipfile.lock/pdm.lock, PHP composer.lock, Ruby Gemfile.lock, Elixir mix.lock, Dart pubspec.lock, Swift Package.resolved); nothing for OSV to scan. A NuGet-only repo stays NotApplicable — .NET CVEs are D30's domain.0—
D40 · Network Egress Confinementruntime-hardening—runtime-hardening: not applicable — No Kubernetes/orchestration workloads found in the repository manifests; network egress policy is a cluster-native control that may live at the platform/firewall layer, so there is nothing to assess here.0—
D41 · Kernel & Syscall Confinementruntime-hardening—runtime-hardening: not applicable — No Kubernetes/orchestration workloads found in the repository manifests; seccomp/AppArmor/SELinux confinement is a workload-level control, so there is nothing to assess here.0—
D42 · Runtime Threat Enforcementruntime-hardening—runtime-hardening: not applicable — No Kubernetes/orchestration workloads found in the repository manifests; runtime threat-detection and admission-control policy are cluster-level controls, so there is nothing to assess here.0—

Run 019fb076-5e32-770a-96f3-81fa8d2612e7 · every finding is also locatable in findings.md, and the complete scoring record (with exit codes + durations) in sidecar.json.

Issues: 2 · Warnings: 22 · Recommendations: 5 · Info: 4 — Appendix A · all findings · full markdown report.
Generated by Watchdog — deterministic code-health analysis. 30-07-2026 @ 00:39 UTC.

Downloadable artifacts

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

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