Public report — ExpenseTracker, published 19 Jun 2026.
Concrete security findings (CVE IDs, secret matches, dependency versions) are hidden in this version;
ask the repo owner for the full report.
35findings with an exact file:lineof 60 — the remainder are repo-wide signals (a dimension-level measurement, not a single line); open any file:line and verify
54/93dimensions across the health lenses1604 LoC · 15 projects — 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.
aekoky/ExpenseTracker carries serious risk (47%). Several issues below can materially affect reliability, security, or the cost of change and warrant near-term attention.
It is strongest in Event Sourcing (100%) — its event log is trustworthy to replay. Code Health (99%) is solid too.
The area that most needs attention is Readiness (26%) — operating, monitoring and recovering the system safely is harder. Maturity (54%) 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: authorization at every handler (Access Controls); Start an ADR log (docs/adr/) recording significant decisions… (Architecture documentation); CI workflow that builds and runs the test suite on every push/PR (CI/CD gates).
For scale: Hobby (~1,604 production lines); rebuilding it from scratch would take roughly ~0.1 person-years (~1 engineer). Approximate, ±~30%.
It builds on a genuinely strong Event Sourcing foundation (100%); the priorities above are the highest-leverage way to bring the rest up to that level.
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.
This codebase represents roughly ~0.1 person-years of build effort (about ~€3,200 to rebuild). Its weakest lens is Readiness at 26% — the part of that asset most exposed by the findings below.
How we model this: boilerplate at a scaffolding rate + logic × domain Very high (×2.0) — microservices, DDD/clean architecture, CQRS, event sourcing × a 0.7× quality factor, at €60–95/h; indicative, ±~30%. 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
Enforce authorization at every handler — call the guard method (throw-on-violation) from each one, or adopt [Authorize] so protected-by-default is demonstrable.
Of everything flagged, the best return on effort is: Enforce authorization at every handler — call the guard method (throw-on-violation) from each one, or adopt [Authorize] so protected-by-default is demonstrable. The rest can wait behind it.
Evidence: priority ranking: top of 5 ranked by impact/effort
→ Enforce authorization at every handler — call the guard method (throw-on-violation) from each one, or adopt [Authorize] so protected-by-default is demonstrable.
Architecture — bounded-context dependency graph
Each box is a bounded context (its layer projects grouped, or a project count when large); arrows show dependencies between contexts. A shared kernel is where many arrows converge.
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
A05:2021 — Security Misconfiguration
4
High / Critical
Roadmap
First, enforce authorization at every handler using the guard method or the [Authorize] attribute to ensure protected-by-default security. Second, begin an ADR log in docs/adr/ to record significant architectural decisions and their rationale. Third, implement a CI workflow that builds the code and runs the test suite on every push or pull request. Fourth, codify backups and geo-recovery in infrastructure as code, documenting RTO/RPO and restore procedures. Finally, add security response headers such as Content-Security-Policy and X-Frame-Options to strengthen the web security posture.
Ranked by impact ÷ effort. "Helps" is the estimated gain on the 0–100 health score.
Do this
Helps
Effort
Dimension
Enforce authorization at every handler — call the guard method (throw-on-violation) from each one, or adopt [Authorize] so protected-by-default is demonstrable.
Add security response headers (Content-Security-Policy, X-Frame-Options, X-Content-Type-Options) — defense in depth, even when a reverse proxy could set them.
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. 50 of 54 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 0.6 — 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 — 54 dimensions across the health lenses
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, 35 of 60 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
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.
D32 Data Compliance (PII/GDPR) — 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.
D33 JS/npm 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.
D36 Supply-chain Provenance & Signing — 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.
D37 Vulnerability-disclosure Policy — 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.
D38 OSV 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 (EF migration scaffolds, *.Designer.cs, model snapshots) is EXCLUDED — its repetition is the tool's, not the team's — so the score reflects hand-written duplication only; the generated footprint is reported separately under Solution Shape.
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.
D10 Test Quality: Assertion density is structural — it cannot tell a meaningful behavioural assertion from a trivial one, only that an assertion is present.
D11 Test Reliability: Flakiness is inferred from history/markers — Watchdog runs the suite once (for coverage), not the repeated runs under varied conditions that reveal nondeterminism, so a flaky test never recorded as failing is invisible here.
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 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").
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 (EF migrations, designer files, snapshots) is excluded — it is never the team's dead code to delete.
D18 Solution Shape: Build integrity reflects whether the solution compiled in this environment — a build that needs a private feed, a specific SDK, or a generated file absent from the repo can read as broken when it is merely unreproducible here.
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.
D20 ADR Quality: ADR quality is an LLM read of the decision records present — it cannot know about decisions made and never recorded, and its verdict is sampled and advisory.
D21 Naming Consistency: Naming quality is an LLM judgement over a bounded sample — it assesses clarity/consistency of the names it sees, not domain-correctness, and is advisory.
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.
D24 Comment Value: Comment value (WHY vs WHAT) is an LLM judgement over a bounded sample — it is advisory and cannot weigh a comment against the precise code change it was written to explain.
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.
D27 Navigability: Indirection/navigability is structural — it measures hops to follow a call, not whether that indirection buys real flexibility or just ceremony.
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.
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.
AX9 CQS / query purity: Handlers are found by interface/name convention — a query handler using neither is not seen. Mutation is a resolved write/publish invocation (SaveChanges/repository/bus), so a write hidden behind a hand-rolled wrapper, reflection, or a string-keyed service locator resolves to a non-persistence type and isn't flagged; it detects that a query writes state, not whether the write is a legitimate read-side cache update. Clean means "no resolved write/publish in a query body", not a proof of CQS purity.
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".
P5 DR & Backup: Backup/restore and disaster-recovery readiness is judged from in-repo evidence — a config that exists is not a tested restore, so the absence of positive evidence is reported as "not evidenced", never scored as present.
The LLM boundary
LLM-set scores this run (6): D19, D20, D21, D22, D24, M4 (model: Qwen/Qwen3.5-35B-A3B). 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.
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.
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.
0 method(s) exceeded the cognitive complexity threshold of 15.
✓ On the Gold path — maintain.
Detailed fixes: d2_recommendation.md.
Do you agree with this assessment?
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.
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.
Detailed fixes: d4_recommendation.md · top locations in Appendix A, every location in findings.md.
Do you agree with this assessment?
D5 · Coupling8.9 / 10Healthy✓ 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.
Off the main sequence: ExpenseTracker.Contracts · ×2
What to do
Resolve the 2 Off the main sequence finding(s) in Coupling. — One of this dimension's main actionable groups (2 warning-level).
Enforce Coupling in CI to reach Verified (currently Documented). — Hardens enforcement from Documented toward Prevented — provenance only; does not change the score.
Detailed fixes: d5_recommendation.md · top locations in Appendix A, every location in findings.md.
Do you agree with this assessment?
D6 · Cohesion (LCOM4)4.0 / 10Poor✓ 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.
Resolve the 1 Low cohesion finding(s) in Cohesion (LCOM4) — start with ApiExceptionFilterAttribute.cs. — One of this dimension's main actionable groups (1 warning-level).
Enforce Cohesion (LCOM4) in CI to reach Verified (currently Documented). — Hardens enforcement from Documented toward Verified — provenance only; does not change the score.
Detailed fixes: d6_recommendation.md · top locations in Appendix A, every location in findings.md.
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.
41 test methods: 37 unit, 4 integration, 0 BDD, 0 e2e.
Unit tests
Integration tests
BDD tests
E2E tests
✓ On the Gold path — maintain.
Detailed fixes: d9_recommendation.md · top locations in Appendix A, every location in findings.md.
Do you agree with this assessment?
D10 · Test Quality10.0 / 10Exemplary✓ Tool-verified
What it measures: Whether the tests truly assert behaviour rather than just running the code.
Method: Per-test assertions, skips, and mock references analyzed via Roslyn; structured skip-reason tags (BUG:/ENV:) separate documented deferrals from debt. Deterministic.
0 skipped, 0 zero-assertion, 2 mock references across 41 tests.
Mock framework: Moq · ×2
✓ On the Gold path — maintain.
Detailed fixes: d10_recommendation.md · top locations in Appendix A, every location in findings.md.
Do you agree with this assessment?
D11 · Test Reliability10.0 / 10Exemplary✓ Tool-verified
What it measures: Whether the tests pass reliably, with no flakiness.
Method: Suite re-run N times within tiered wall-clock budgets (unit to e2e); tests failing non-deterministically across runs flagged; guarded tests retried when #if guards detected.
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.
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.
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.
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.
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.
What it measures: Whether the solution is laid out in a sensible, conventional structure.
Method: Solution structure: project count, decomposition, shell-project detection, build success (confirmed failures cap the score); traced to actual .sln files and binaries. Deterministic.
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.
The project has a basic README that outlines features and installation, but it is incomplete and contains a syntax error in the code block. More critically, there is a severe lack of technical documentation: zero XML documentation comments across all 228 public members in the codebase, and no architecture or design documents exist. The README also lacks a clear local development setup guide beyond Docker, and the API reference section is truncated and malformed.
Resolve the 12 Low XML-doc coverage finding(s) in Documentation Quality — start with AuditService.Api.csproj, AuditService.Application.csproj, AuditService.Domain.csproj. — One of this dimension's main actionable groups (12 warning-level).
Detailed fixes: d19_recommendation.md · top locations in Appendix A, every location in findings.md.
What it measures: Whether architecture decisions are recorded well (context, decision, consequences).
Method: Per-ADR judgment by language model at low temperature with two-pass stability; confidence is share of ADRs evaluated; enforcement-field presence detected deterministically. Advisory.
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.
0 naming inconsistencies across 200 sampled symbols.
✓ On the Gold path — maintain.
Detailed fixes: d21_recommendation.md.
Do you agree with this assessment?
D22 · Internal API Consistency / 10Exemplary◐ 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.
What it measures: Whether comments are worth it — explaining WHY (valuable) rather than WHAT (redundant).
Method: Judged by language model at low temperature (0.0-0.1) on deterministically sampled inline comments with surrounding code; findings verified back to sampled comments by substring match. Advisory, sampled.
Resolve the 2 redundant comment finding(s) in Comment Value — start with Program.cs (2). — One of this dimension's main actionable groups (2 recommendation-level).
Detailed fixes: d24_recommendation.md · top locations in Appendix A, every location in findings.md.
0 of 15 projects flagged as possibly oversized/incoherent.
✓ On the Gold path — maintain.
Detailed fixes: d26_recommendation.md.
Do you agree with this assessment?
D27 · Navigability8.8 / 10Healthy✓ Tool-verified
What it measures: How far you must trace to follow a call — low indirection and co-located slices read easier.
Method: Call indirection (interface hops, cross-namespace calls, slice-locality scaled) over a sampled set of method invocations, size-aware baseline. Sampled; confidence discounted by symbol-resolution gaps.
Coverage: Slice locality from the first namespace segments, SAMPLED (≤400 methods) — not exhaustive.
92 % of calls cross a namespace and 24 % go through an interface, but 100 % of collaborators are co-located — so following a call takes several hops. Baseline: small — navigation cost is tolerated.
What to do
Improve Navigability — currently 8.8/10. — 92 % of calls cross a namespace and 24 % go through an interface, but 100 % of collaborators are co-located — so following a call takes several hops. Baseline: small — navigation cost is tolerated.
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.
What it measures: Real static-analysis (SAST) findings — likely security bugs in the code, any language.
Method: Polyglot static analysis via semgrep --config auto across the repo; severity rules (ERROR/WARNING/INFO) map to a 0-10 wide normalizer. 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).
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.
Other · Architecture — Whether any singleton service captures a scoped/transient dependency — a silent lifetime/threading bug.
Method: Roslyn scan: DI registrations parsed from AddSingleton/Scoped/Transient; each singleton checked for captured shorter-lifetime dependencies. Exhaustive, deterministic.
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.
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.
Other · Architecture — Whether singleton services avoid mutable shared instance state that concurrent callers would race on.
Method: Roslyn scan: singleton field mutations unguarded by lock or Interlocked, per type; syntax-based guard detection. Deterministic, traceable per field.
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.
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.
Other · Architecture — Whether the codebase has a recognisable, scale-appropriate structure (a named architectural style, or modular enough for its size) rather than being an ad-hoc ball of mud.
Method: Roslyn plus csproj analysis: architecture style detection (DDD, clean, vertical-slice, CQRS) and structure fitness for repo size. Deterministic.
Other · Architecture — Whether interfaces stay focused rather than fat — the Interface-Segregation principle (SOLID 'I').
Method: Roslyn scan: public interface member counts; fat-interface threshold (over 15 members) flagged per type. Deterministic, type-level.
Do you agree with this assessment?
AX8 · Test isolation10.0 / 10Exemplary✓ Tool-verified
Other · Architecture — Whether production projects stay free of references to test projects — tests may depend on production, never the reverse.
Method: Csproj graph: each production project checked for references to test projects (identified by test-framework presence, not name). Zero violations is clean. Deterministic.
Other · Architecture — Whether read (query) handlers stay side-effect-free — a query that writes persistent state or raises events breaks CQS and makes reads unsafe to retry, cache, or route to a read replica.
Method: Roslyn scan: CQRS handlers classified query-vs-command by interface (IQueryHandler/ICommandHandler/IRequestHandler<TQuery,TResult>) and name convention (*Query/Get*/Find* vs *Command); each query handler's body checked for persistent-state writes (SaveChanges/repository Add-Update) or event publishes by resolved invocation. Deterministic, type-level, exhaustive over the detected handlers.
Coverage: Population: CQRS handlers identified by IQueryHandler/ICommandHandler/IRequestHandler interface + *Query/Get*/Find*/*Command NAME convention; query purity then checked exhaustively within that set — a query handler using neither convention is invisible, and mutation is a resolved persistence/publish CALL, not full dataflow.
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C2 · Access Controls3.0 / 10Poor✓ Tool-verified
Other · Security — Whether access is authorized by default — [Authorize]/policies or imperative guard methods (throw-on-violation) called from handlers.
Method: Roslyn scan: [Authorize] usage and authorization policies, plus imperative throw-on-violation guard methods detected via syntax. Deterministic.
Authorization machinery exists but no [Authorize] usage and no imperative guard calls were found at handlers.
What to do
Enforce authorization at every handler — call the guard method (throw-on-violation) from each one, or adopt [Authorize] so protected-by-default is demonstrable.
Other · Event Sourcing — Whether Apply/When folds reconstruct state purely from the event (no DateTime.Now, Guid.NewGuid, Random or IO) so replay is reproducible.
Method: Roslyn syntax scan (event-sourcing gated): Apply/When folds checked for forbidden tokens (DateTime.Now, Guid.NewGuid, Random, IO), stripped of comments/strings. Deterministic, hard fact per fold.
Other · Code Health — Unreviewed-generation residue: shipped members still throwing NotImplementedException, and placeholder string literals left in non-test, non-generated code. Scored as a quality signature, never as a claim about authorship.
Method: Roslyn syntax scan: NotImplementedException throws and placeholder string literals in non-test, non-generated shipped code. Deterministic, code-shape signature.
Other · Code Health — Unfinished work detected by code SHAPE, not keywords: members that only throw a "not implemented" exception, methods that take inputs and return a constant, async methods that never await, dead `if (false)` / `#if false` branches, and skeleton types most of whose members are holes. A real, objective slice of technical debt.
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 an 'Architecture' / 'How it works' section to the root README — the high-level shape.
Add a README to the 15 of 15 project(s) that lack one — worth up to 2 pts.
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.
Only 6/15 projects share a common root namespace — the code's module identity is inconsistent.
What to do
Adopt a consistent root-namespace convention (a shared prefix, e.g. Acme.*); short project-file/directory names are fine as long as the RootNamespace is uniform.
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 claims the project is 'microservices-based' and 'event-driven', but the evidence shows a monolithic 'ExpenseTracker' project alongside the specific service projects, and the directory structure suggests a modular monolith or mixed architecture rather than pure microservices.
README claims 'API Gateway via Kong'. The evidence shows a 'Kong/' directory, which supports this.
README claims 'Cache: Redis'. The evidence shows a 'Redis/' directory, which supports this.
What to do
Reconcile the README with reality: README claims the project is 'microservices-based' and 'event-driven', but the evidence shows a monolithic 'ExpenseTracker' project alongside the specific service projects, and the directory structure suggests a modular monolith or mixed architecture rather than pure microservices.; README claims 'API Gateway via Kong'. The evidence shows a 'Kong/' directory, which supports this.; README claims 'Cache: Redis'. The evidence shows a 'Redis/' directory, which supports this..
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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.
Do you agree with this assessment?
P2 · Observability5.7 / 10Fair✓ Tool-verified
Readiness · Readiness — Whether the code is diagnosable in production — structured logging, tracing/metrics, health checks.
Readiness · Readiness — Whether SAST, secret/dependency scanning and performance benchmarking are wired in (presence, not runtime).
Method: Filesystem/Roslyn scan: CodeQL, Dependabot, secret-scanning, and BenchmarkDotNet presence in pipelines and projects. Exhaustive, deterministic.
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.
Deployment automation exists but no readiness/liveness probes, rolling-update strategy, lifecycle hooks or migration job were evidenced — a bad release is harder to detect and reverse.
What to do
Add readiness/liveness probes and a rolling-update (or blue/green) strategy so a bad release is caught and rolled back automatically.
Add an approval/environment gate (required reviewers / protection rules) before production promotion.
Do you agree with this assessment?
P5 · DR & Backup0.0 / 10Critical✓ Tool-verified
Readiness · Readiness — Whether disaster recovery is planned and codified — backups, geo-recovery, RTO/RPO, persistence guarantees — from IaC + container manifests + docs, never the live cloud.
Method: Filesystem scan: disaster recovery, backup, geo-recovery, RTO/RPO, persistence guarantees from IaC, manifests, and docs. Exhaustive, deterministic, never a live environment.
A persistence guard (data volume / purge-protection) was found, but no backup, geo-recovery or RTO/RPO controls were evidenced — a volume that survives a container recreate is not a tested restore from catastrophic loss.
What to do
Codify backups + geo-recovery in IaC and document RTO/RPO and the restore procedure — a persistence guard alone is not disaster recovery.
Other · Security — Transport security, security headers, secure cookies, input validation, middleware order and crypto hygiene (presence, not runtime).
No Content-Security-Policy / X-Frame-Options / X-Content-Type-Options configuration found — defense in depth, even when a reverse proxy could set them. (−2.0 on this card.)
No CookieSecurePolicy/HttpOnly/SameSite configuration found. (−1.5 on this card; skip if the app sets no cookies.)
What to do
Add security response headers (Content-Security-Policy, X-Frame-Options, X-Content-Type-Options) — defense in depth, even when a reverse proxy could set them.
Set secure cookie flags — CookieSecurePolicy.Always, HttpOnly, and SameSite (Strict/Lax) on auth/session cookies. Skip only if the app sets no cookies.
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.
Other · Code Health — Whether async methods accept a CancellationToken so work can be cancelled (adoption curve).
Method: Roslyn scan: every async method (excluding framework-fixed overrides/Blazor handlers) checked for CancellationToken parameter presence. Deterministic, adoption percentage.
Only 30/31 async methods accept a CancellationToken, so requests can't be cancelled cleanly under load or on client disconnect. In Blazor Server circuits and other short-write hosts, omitting it can be an accepted convention — judge against your hosting model.
No CancellationToken parameter — work can't be cancelled cleanly on disconnect/shutdown. — MartenUnitOfWork.cs:23
What to do
Thread a CancellationToken through async methods so work stops promptly on cancellation.
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.
Other · Code Health — Whether log calls use message templates (queryable) rather than interpolated strings.
Method: Roslyn syntax scan: every log call-site counted; interpolated-string first-argument violations flagged. Population is all log calls, not estimated. Deterministic.
Other · Code Health — Whether nullable reference types are enabled and not undermined by heavy `!` suppression.
Method: Roslyn compiler-options scan: NullableContextOptions per project; null-forgiving (!) suppression density per 1k syntax nodes. Deterministic, adoption plus suppression penalty.
~0.1 `!` suppressions per 1k syntax nodes — each one tells the compiler to trust you about null, suppressing the very safety NRTs provide.
What to do
Enable <Nullable>enable</Nullable> across all projects and resolve warnings rather than suppressing with `!`.
Do you agree with this assessment?
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.
Not included — 39 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.
AX7 Slice cohesion — not applicable — not a vertical-slice architecture
C1 Data Protection — No personal data detected in the analyzed source — no PII-typed entity/column names (Email, FirstName, DateOfBirth, …), no ASP.NET Identity / user-account model, and no stored user credentials. GDPR data-protection controls are therefore N/A here. If this is intentional, record the no-PII posture in an ADR; if the app does process personal data, name those fields conventionally so this dimension activates.
C3 Audit Trail — No personal data detected in the analyzed source — no PII-typed entity/column names (Email, FirstName, DateOfBirth, …), no ASP.NET Identity / user-account model, and no stored user credentials. GDPR data-protection controls are therefore N/A here. If this is intentional, record the no-PII posture in an ADR; if the app does process personal data, name those fields conventionally so this dimension activates.
C4 Data Retention — No personal data detected in the analyzed source — no PII-typed entity/column names (Email, FirstName, DateOfBirth, …), no ASP.NET Identity / user-account model, and no stored user credentials. GDPR data-protection controls are therefore N/A here. If this is intentional, record the no-PII posture in an ADR; if the app does process personal data, name those fields conventionally so this dimension activates.
C5 Data-Subject Rights — No personal data detected in the analyzed source — no PII-typed entity/column names (Email, FirstName, DateOfBirth, …), no ASP.NET Identity / user-account model, and no stored user credentials. GDPR data-protection controls are therefore N/A here. If this is intentional, record the no-PII posture in an ADR; if the app does process personal data, name those fields conventionally so this dimension activates.
D16 Bus Factor — single-maintainer — knowledge-concentration (bus factor) risk
D23 Boundary Type-Coupling — Bounded contexts not declared
D25 ADR Conformance — no ADRs to check
D30 Dependency Vulnerabilities — Not applicable
D32 Data Compliance (PII/GDPR) — Not applicable
D33 JS/npm Dependency Vulnerabilities — Not applicable
D34 Knowledge Freshness — early-stage repository — too little history to judge knowledge freshness
D36 Supply-chain Provenance & Signing — Not applicable
D37 Vulnerability-disclosure Policy — Not applicable
D38 OSV Dependency Vulnerabilities — Not applicable
D7 Architectural Integrity — No checkable ADRs to assess
D8 Code Coverage — Coverage not measured
DM1 Domain Modelling — applicable but skipped (2/3 markers — below the conservative bar): 2 aggregate root(s) (AggregateRoot/IAggregateRoot); a Domain/Aggregates/ValueObjects layer
ED1 Event-Driven — applicable but skipped (2/3 markers — below the conservative bar): a message-bus package; 5 CQRS handler(s)
ED5 Idempotency — no mutating command handlers or message consumers detected — idempotency check not applicable
ES2 Immutable events — no persisted event types detected — immutability check not applicable
ES3 PII in the event store — no persisted event types detected — PII-in-events check not applicable
P12 CI test-gate honesty — no CI workflow found
P6 Release Hygiene — not evidenced — no changelog, version stamp or semver release tag in the repo
P7 Outbound HTTP resilience — no outbound HTTP usage detected
P8 Schema migrations — no EF Core usage detected
P9 Domain vs controller coverage — no coverage report found on disk — run tests with `--collect:"XPlat Code Coverage"` (or in CI) to enable this cross-layer check
PF1 Benchmark discipline — Performance is assessed only for perf-relevant repos — a packaged library, one that ships benchmarks, or one already using allocation-aware APIs. This repo isn't one, so the Performance lens is not applicable and is excluded from the score.
PF2 Allocation hygiene — Performance is assessed only for perf-relevant repos — a packaged library, one that ships benchmarks, or one already using allocation-aware APIs. This repo isn't one, so the Performance lens is not applicable and is excluded from the score.
PF3 Async & latency hygiene — Performance is assessed only for perf-relevant repos — a packaged library, one that ships benchmarks, or one already using allocation-aware APIs. This repo isn't one, so the Performance lens is not applicable and is excluded from the score.
X6 Hand-rolled structured-format parsing — no data
X7 Silent fallback defaults — no data
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.
Off the main sequence: ExpenseTracker.Contracts — ExpenseTracker.Contracts: abstractness 0.00, instability 0.00, distance 1.00 — zone of pain — concrete and heavily depended-on, so it's rigid to change.
Off the main sequence: ExpenseTracker.Application — ExpenseTracker.Application: abstractness 0.00, instability 0.10, distance 0.90 — zone of pain — concrete and heavily depended-on, so it's rigid to change.
Low cohesion: ApiExceptionFilterAttribute (LCOM4 5) src/ExpenseTracker/ExpenseTracker.Api/Filters/ApiExceptionFilterAttribute.cs:8— ApiExceptionFilterAttribute's methods form 5 groups that share no state and don't call each other — a sign it may have several responsibilities. Review whether it splits into focused classes.
Coverage not measured — The test suite couldn't be built/run in-image and no coverage report is committed, so line coverage was not measured — and it is EXCLUDED from the score rather than scored on a LoC-ratio proxy. Commit the Cobertura/OpenCover/lcov report your CI already produces (anywhere in the repo), or make the suite runnable in-image, and real coverage will be measured.
redundant comment src/AuditService/AuditService.Api/Program.cs:5— "Add services to the container." — Remove. This is a standard ASP.NET Core pattern; the code `services.Add...` is self-explanatory.
redundant comment src/AuditService/AuditService.Api/Program.cs:14— "Configure the HTTP request pipeline." — Remove. This is a standard ASP.NET Core pattern; the code `app.Use...` is self-explanatory.
No ADRs found — No ADRs found at common paths; consider documenting architectural decisions in Docs/ADL/ or similar.
D23 · Boundary Type-Coupling· Bounded contexts not declared · ×1
Bounded contexts not declared — A codebase of this scale with 15 projects likely contains multiple distinct modules or concerns that require explicit boundary definitions to manage coupling effectively. Declare architecture.contexts (≥2) in config to assess cross-boundary type coupling.
no ADRs to check — No ADRs found, so conformance can't be assessed.
D30 · Dependency Vulnerabilities· Not applicable · ×1
Not applicable — the solution did not restore on the analyzer's .NET SDK (an SDK/target-framework/restore mismatch, common for an older codebase), so there was no restored dependency graph to scan for NuGet CVEs — excluded rather than scored; re-run on an SDK that can restore this solution
D32 · Data Compliance (PII/GDPR)· Not applicable · ×1
Not applicable — No PII/GDPR-handling patterns detected (p/gdpr ruleset) — no data-compliance surface to assess.
D33 · JS/npm Dependency Vulnerabilities· Not applicable · ×1
Not applicable — No JS/npm manifest or lockfile found outside bin/obj (package.json, package-lock.json, yarn.lock, pnpm-lock.yaml, bun.lockb); no JS dependencies to scan.
early-stage repository — too little history to judge knowledge freshness — early-stage repository — too little history to judge knowledge freshness (22 commit(s) sampled).
D36 · Supply-chain Provenance & Signing· Not applicable · ×1
Not applicable — No CI/build pipeline found (.github/workflows, .gitlab-ci.yml, azure-pipelines.yml, Jenkinsfile, .circleci); there is no build to attest provenance for.
D37 · Vulnerability-disclosure Policy· Not applicable · ×1
Not applicable — No vulnerability-disclosure policy file found (SECURITY.md, .github/SECURITY.md, docs/SECURITY.md, .well-known/security.txt). A coordinated-disclosure policy may live off-repo, so this is not evidenced rather than failed.
D38 · OSV Dependency Vulnerabilities· Not applicable · ×1
Not applicable — No JS/npm lockfile found outside bin/obj (package-lock.json, yarn.lock, pnpm-lock.yaml, bun.lockb); nothing for OSV to scan.
D7 · Architectural Integrity· No checkable ADRs to assess · ×1
No checkable ADRs to assess — No architecture decision records were found and the project graph is acyclic, so architectural integrity could not be assessed. Add ADRs (with `enforcement: analyzer|test`) to make the architecture's rules checkable.
D18 · Solution Shape· Build did not complete in the analyzer · ×1
Build did not complete in the analyzer — `dotnet build` reported 2 error(s) but no C# compiler diagnostic — an SDK / target-framework / restore mismatch in the analyzer environment, not a code defect (common for an older codebase whose target framework the analyzer's SDK can't build). Solution Shape is scored on structure and is NOT capped; the C# semantic analysis loads independently and is unaffected.
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.
dotnet: not applicable — the solution did not restore on the analyzer's .NET SDK (an SDK/target-framework/restore mismatch, common for an older codebase), so there was no restored dependency graph to scan for NuGet CVEs — excluded rather than scored; re-run on an SDK that can restore this solution
trivy: not applicable — No JS/npm manifest or lockfile found outside bin/obj (package.json, package-lock.json, yarn.lock, pnpm-lock.yaml, bun.lockb); no JS dependencies to scan.
provenance: not applicable — No CI/build pipeline found (.github/workflows, .gitlab-ci.yml, azure-pipelines.yml, Jenkinsfile, .circleci); there is no build to attest provenance for.
disclosure: not applicable — No vulnerability-disclosure policy file found (SECURITY.md, .github/SECURITY.md, docs/SECURITY.md, .well-known/security.txt). A coordinated-disclosure policy may live off-repo, so this is not evidenced rather than failed.
osv-scanner: not applicable — No JS/npm lockfile found outside bin/obj (package-lock.json, yarn.lock, pnpm-lock.yaml, bun.lockb); nothing for OSV to scan.
0
—
Run 019ee1f8-3e47-70d6-a6dc-6f519943d3a3 · every finding is also locatable in findings.md, and the complete scoring record (with exit codes + durations) in sidecar.json.
Appendix C — Personal-data map
Every field, property and record parameter whose name is conventional personal data — 9 field(s) across 1 category, each with an exact repo-relative file:line. This is the data inventory a compliance review starts from — right-to-erasure, retention, minimisation. Detected by name with a deliberately specific classifier (the same one the GDPR dimensions use, so CardDefinition or FileName don't trip); informational — it feeds no score.
Issues: 2 · Warnings: 20 · Recommendations: 15 · Info: 23 — Appendix A · all findings · full markdown report.
Generated by Watchdog — deterministic code-health analysis. 19-06-2026 @ 22:20 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.