Public report — MQContract, published 3 Aug 2026.
Concrete security findings (CVE IDs, secret matches, dependency versions) are hidden in this version;
ask the repo owner for the full report.
20findings with an exact file:lineof 61 — the remainder are repo-wide signals (a dimension-level measurement, not a single line); open any file:line and verify
51/97dimensions across the health lenses15180 LoC · 76 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.
roger-castaldo/MQContract 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 (98%) — the structure is clean and changes stay contained. Performance (81%) is solid too.
The area that most needs attention is Security (36%) — exposure to security and compliance incidents is elevated. Maturity (44%) 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: Encrypt sensitive data at rest (ASP.NET Core Data Protection /… (Data Protection); Record significant decisions one document per decision (Architecture documentation); Group production code under src/ (or split deliberately (Folder & project structure).
For scale: Small (~15,180 production lines); rebuilding it from scratch would take roughly ~0.3 person-years (~1 engineer). Approximate, ±~30%.
It builds on a genuinely strong Architecture foundation (98%); the priorities above are the highest-leverage way to bring the rest up to that level.
How the score is built — each lens's share of the headlineWidth 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.
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 ~0.3 person-years of build effort (about ~€45,000 to rebuild). Its weakest lens is Security at 36% — the part of that asset most exposed by the findings below.
How we model this: boilerplate at a scaffolding rate + logic × domain Low (×0.9) — library/CLI × 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
Encrypt sensitive data at rest (ASP.NET Core Data Protection / column encryption) and manage keys in a vault. Skip if delegated to infra (Postgres TDE, KMS, etc.).
Record significant decisions one document per decision — dated, stating the context, the decision and its consequences — and keep them together wherever your design docs already live (a conventional `docs/adr/` tree with `NNNN-title.md` names is the most discoverable form).
Value concentrated against a weak lens · High · Value at risk
This is a Small asset (~0.3 person-years to rebuild), and its weakest lens is Security at 36%. The operational and business risk on an asset this size concentrates there — that's where remediation buys the most protection.
→ Direct remediation budget at Security 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: Encrypt sensitive data at rest (ASP.NET Core Data Protection / column encryption) and manage keys in a vault. Skip if delegated to infra (Postgres TDE, KMS, etc.). The rest can wait behind it.
Evidence: priority ranking: top of 5 ranked by impact/effort
→ Encrypt sensitive data at rest (ASP.NET Core Data Protection / column encryption) and manage keys in a vault. Skip if delegated to infra (Postgres TDE, KMS, etc.).
Architecture — module dependency matrix
53 modules, 152 dependencies — 2 dependency cycles, shown as the red cell(s) above the diagonal. Rows and columns are the same modules, ordered so that a module only depends on ones above it. A cell means the row depends on the column, and its number is how many type pairs create that dependency. Read one thing: is anything above the diagonal? A mark there is a dependency cycle. (A cycle is all this shows — an unusual but cycle-free dependency sits below the diagonal like any other.)
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
A04:2021 — Insecure Design
4
Medium
A03:2021 — Injection
2
High / Critical
Roadmap
First, encrypt sensitive data at rest and manage keys in a vault, unless this is handled by infrastructure. Second, create a single document per significant architectural decision, including context and consequences, and store them in a standard docs/adr/ directory. Third, restructure the repository by moving production code into a src/ folder to separate it from tooling code. Fourth, update the root README to include instructions on how to run the test suite. Finally, add a static analysis step to the CI pipeline to fail the build on security regressions.
Ranked by impact ÷ effort. "Helps" is the estimated gain on the 0–100 health score.
Do this
Helps
Effort
Dimension
Encrypt sensitive data at rest (ASP.NET Core Data Protection / column encryption) and manage keys in a vault. Skip if delegated to infra (Postgres TDE, KMS, etc.).
Record significant decisions one document per decision — dated, stating the context, the decision and its consequences — and keep them together wherever your design docs already live (a conventional `docs/adr/` tree with `NNNN-title.md` names is the most discoverable form).
Add a SAST step to CI running what this repository's stack ships: CodeQL's csharp pack (it analyses VB.NET too), or a security analyzer package — or `semgrep --config=auto`, which runs on any language — so a security regression fails the build instead of landing.
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. 49 of 51 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 0.7 — 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 — 51 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, 20 of 61 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.
D5 Coupling — evaluation did not complete — Coupling not included (check did not complete) — excluded from the score.
D19 Documentation Quality — LLM provider failed — The model provider returned an unusable result, so this LLM-assisted dimension fell back to a measurement gap (confidence 0) rather than a penalty. Re-run with a reachable provider to score it.
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.
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.
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.
D10 Test Quality: Assertion density is structural — it cannot tell a meaningful behavioural assertion from a trivial one, only that an assertion is present.
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.
D16 Bus Factor: Bus-factor is a time-decayed model of commit attribution (who has recently, repeatedly worked a file), not comprehension — pairing, review and reading-without-committing spread knowledge it can't see; bot commits and shared accounts still distort it.
D17 Explicit Debt: Acknowledged-debt signals (TODO/FIXME, suppressions, dead code) are textual — undocumented debt that nobody marked, and debt that lives in design rather than annotations, is invisible. Committed machine-written code (scaffolded migrations, designer/codegen output, generated stubs) is excluded — it is never the team's dead code to delete.
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.
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.
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").
D30 Dependency Vulnerabilities: CVE matching depends on accurate package/version metadata and the advisory database — a vulnerability with no published advisory, or in code not declared as a dependency, is not seen.
D32 Data Compliance (PII/GDPR): PII/GDPR signals are heuristic pattern matches in code — they flag likely handling concerns, not legal compliance, and cannot trace where data actually flows at runtime.
D34 Knowledge Freshness: Freshness is decayed commit RECENCY, not comprehension — code read often but rarely committed reads as orphaned, and stable code that genuinely needs no changes is penalised the same as forgotten code; bot/squash commits distort it like the bus factor.
D35 Change Coupling: Change coupling is co-change in COMMITS — files split across separate commits, or coupled only through a shared config/build step, read as uncoupled, and a sweeping commit (rename/format) is excluded so it doesn't couple everything. It shows that files change together, not WHY: a high coupling can be a healthy cohesive pair as readily as a hidden leak.
AX10 Code composition: Role is inferred from namespace/folder convention, not semantics — a domain concept living in a folder named "Services" reads as application, and the split is lines-of-code, not business value. The business-logic-share score is a SOFT, FLOORED signal: it contributes to the Architecture lens but is floored at the Critical gate, so an infrastructure-heavy design (a gateway, an ETL, a driver) is legitimately low without being nuked to zero.
M4 Documentation accuracy: Onboarding quality is an LLM read of the docs/setup present — it cannot run the onboarding or measure how long a real new joiner takes; the verdict is sampled and advisory.
P6 Release Hygiene: Rollback/observability controls are inferred from repo artefacts (pipelines, dashboards-as-code) — controls configured in external tooling, with no in-repo trace, cannot be credited.
The LLM boundary
LLM-set scores this run (3): D21, D24, 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.
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.
1 method(s) exceeded the cyclomatic complexity threshold of 15; the worst was MessageContextGenerator.Generate at 21. A further 1 method(s) were over the threshold but excluded as flat dispatchers (a long switch/match over independent cases: many branches, almost no nesting), the largest being Connection.Connection.ctor at 21 — they are counted neither in the figure above nor in this dimension's score.
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.
Resolve the 1 MessageContextGenerator.Generate (cognitive 66) finding(s) in Cognitive Complexity — start with MessageContextGenerator.cs. — One of this dimension's main actionable groups (1 warning-level).
Enforce Cognitive Complexity in CI to reach Verified (currently Documented). — Hardens enforcement from Documented toward Prevented — provenance only; does not change the score.
Detailed fixes: d2_recommendation.md · top locations in Appendix A, every location in findings.md.
Do you agree with this assessment?
D3 · God Classes9.4 / 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.
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.
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.
Per-file coverage withheld — the coverage run did not finish
✓ On the Gold path — maintain.
Detailed fixes: d8_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.
1442 test methods: 1362 unit, 80 integration, 0 BDD, 0 e2e.
✓ On the Gold path — maintain.
Detailed fixes: d9_recommendation.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.
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.
Enforce Dependency Hygiene in CI to reach Verified (currently Documented). — Hardens enforcement from Documented toward Verified — provenance only; does not change the score.
Detailed fixes: d12_recommendation.md · top locations in Appendix A, every location in findings.md.
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: Whether knowledge is concentrated in too few people (the "bus factor").
Method: Living knowledge per author via time-decayed commit attribution (6-month half-life, focus weighting) across largest source files. Deterministic, avoids blame's mechanical-refactor false positives.
30 source file(s) have their living knowledge concentrated in one author (≥90% of recent, decayed contribution). The largest is Core/Connections/AConnection.cs.
Small-team knowledge concentration
✓ On the Gold path — maintain.
Detailed fixes: d16_recommendation.md · top locations in Appendix A, every location in findings.md.
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.
Dead code: BasicMessageToNameAndVersionMessage · ×5Testing/Core/Converters/BasicMessageToNameAndVersionMessage.cs:6
✓ On the Gold path — maintain.
Detailed fixes: d17_recommendation.md · top locations in Appendix A, every location in findings.md.
Do you agree with this assessment?
D18 · Solution Shape8.4 / 10Strong✓ Tool-verified
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.
76 projects, 1063 source files, 48207 hand-written lines of code (15180 production / 33027 test), 139 inter-project edges.
Thin analysable surface across projects
What to do
Resolve the 1 Thin analysable surface across projects finding(s) in Solution Shape. — One of this dimension's main actionable groups (1 recommendation-level).
Detailed fixes: d18_recommendation.md · top locations in Appendix A, every location in findings.md.
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.
2 naming inconsistencies across 200 sampled symbols.
Inconsistent naming for registering a consumer for query responses. One method is named 'SubscribeQueryResponseAsync' while the other is 'RegisterQueryResponseConsumerAsync'. These appear to serve the same conceptual purpose of setting up a handler for query responses, but use different verbs ('Subscribe' vs 'Register') and object names ('QueryResponse' vs 'QueryResponseConsumer'). · ×2
What to do
Resolve the 2 Inconsistent naming for registering a consumer for query responses. One… finding(s) in Naming Consistency. — One of this dimension's main actionable groups (2 recommendation-level).
Detailed fixes: d21_recommendation.md · top locations in Appendix A, every location in findings.md.
Do you agree with this assessment?
D24 · Comment Value / 10Strong◐ Sampled · advisory
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 1 redundant comment finding(s) in Comment Value — start with Connection.cs. — One of this dimension's main actionable groups (1 recommendation-level).
Detailed fixes: d24_recommendation.md · top locations in Appendix A, every location in findings.md.
0 of 46 projects flagged as possibly oversized/incoherent.
✓ On the Gold path — maintain.
Detailed fixes: d26_recommendation.md.
Do you agree with this assessment?
D27 · Navigability7.8 / 10Strong✓ 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.
76 % of calls cross a namespace and 3 % go through an interface, but 75 % of collaborators are co-located — so following a call takes several hops. Baseline: medium — clean/modular boundaries expected.
What to do
Improve Navigability — currently 7.8/10. — 76 % of calls cross a namespace and 3 % go through an interface, but 75 % of collaborators are co-located — so following a call takes several hops. Baseline: medium — clean/modular boundaries expected.
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 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).
High: github-actions-mutable-action-tag · ×2.github/workflows/unittests.yml:18detected by semgrep finding
What to do
Resolve the 2 High finding(s) in Static Analysis (SAST) — start with unittests.yml (2). — One of this dimension's main actionable groups (2 issue-level).
Detailed fixes: d29_recommendation.md · top locations in Appendix A, every location in findings.md.
What it measures: Whether any dependencies have known published vulnerabilities (CVEs), direct or transitive.
Method: NuGet CVE scan via dotnet list package --vulnerable including transitive; severity tally (Critical/High/Medium/Low) to 0-10 tight normalizer. Exhaustive, deterministic; degrades when absent.
No known-vulnerable NuGet packages (direct or transitive).
✓ On the Gold path — maintain.
Detailed fixes: d30_recommendation.md.
Do you agree with this assessment?
D32 · Data Compliance (PII/GDPR)6.6 / 10Adequate✓ Tool-verified
What it measures: Likely personal-data (PII / GDPR) handling concerns — logging or storing data without safeguards.
Method: Heuristic PII/GDPR pattern scan via semgrep across the repo, using Watchdog's own ruleset (personal data reaching log/console sinks, URLs and query strings, or unprotected browser storage); matches map to severity and a 0-10 wide normalizer. NotApplicable when the scan runs and detects no PII/GDPR surface (no unearned 10); reported LOUDLY as a measurement gap, never as not-applicable, if the ruleset is missing from the analyzer image. Exhaustive, advisory-leaning; degrades on parse failure.
Medium: watchdog-personal-data-in-log · ×4Samples/Messages/AnnouncementConsumer.cs:15detected by semgrep finding
What to do
Resolve the 4 Medium finding(s) in Data Compliance (PII/GDPR) — start with SampleExecution.cs (3), AnnouncementConsumer.cs. — One of this dimension's main actionable groups (4 warning-level).
Detailed fixes: d32_recommendation.md · top locations in Appendix A, every location in findings.md.
What it measures: Whether anyone still has living knowledge of each file, or it has been orphaned — last understood long ago by someone now gone quiet. The sibling of the bus factor: D16 asks who owns it, D34 asks whether anyone still knows it.
Method: File orphaning as total living-knowledge decay below one focused-commit's worth within a year, computed per-file from the D16 decay model. Exhaustive, deterministic over fixed history.
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.
No strong hidden change-coupling between production files.
✓ On the Gold path — maintain.
Detailed fixes: d35_recommendation.md.
Do you agree with this assessment?
D39 · IL Efficiency10.0 / 10Exemplary✓ Tool-verified
Method: IL instruction count per method, read from the BUILT first-party assemblies via Mono.Cecil (the target is compiled on a deep run); scored on the fraction of methods whose emitted IL body exceeds the size threshold. Sees compiler-generated bloat source can't; not-applicable when the target fails to build. 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.
What to do
The domain core is a small share of production code — check that business logic isn't leaking into the application/infrastructure layers (a thin domain is the anemic-domain smell).
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.
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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.
Do you agree with this assessment?
C1 · Data Protection0.0 / 10Critical✓ Tool-verified
Other · Security — Whether sensitive data is encrypted at rest and in transit and keys are vaulted.
Method: Roslyn plus filesystem scan: encryption presence (EF ColumnEncryption, key-vault references, HTTPS enforcement) and key-derivation KDF detection. Deterministic.
No data-protection or encryption usage (ASP.NET Data Protection, AES, column encryption, PBKDF2) was found — sensitive data at rest may be unprotected. If TDE/KMS/vault is delegated to infrastructure, ignore.
What to do
Encrypt sensitive data at rest (ASP.NET Core Data Protection / column encryption) and manage keys in a vault. Skip if delegated to infra (Postgres TDE, KMS, etc.).
Enforce HTTPS (UseHttpsRedirection / RequireHttpsMetadata) so data in transit is always encrypted.
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.
A shipped member still throws NotImplementedException — generated scaffolding that was never completed. Implement it or remove the dead surface. (×2) — SchemaValidationMiddleware.cs:169, FakePublishConnection.cs:22
A placeholder string ("john@example.com") is still in shipped code — typical of generated boilerplate that was never filled in. — Program.cs:21
What to do
Finish or delete NotImplementedException stubs and replace placeholder literals before shipping.
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.
`PublishMessageAsync` is declared `async` but never awaits anything, so it runs synchronously while pretending to be asynchronous. Drop `async` or do the real async work. — Connection.cs:172
`PingAsync` is declared `async` but never awaits anything, so it runs synchronously while pretending to be asynchronous. Drop `async` or do the real async work. — Connection.cs:199
`GetAsync` is declared `async` but never awaits anything, so it runs synchronously while pretending to be asynchronous. Drop `async` or do the real async work. — ServiceConnectionList.cs:35
What to do
Remove the dead code that still moves this score: delete dead if(false) / #if false branches and either do the real async work or drop the async keyword from fake-async methods.
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.
327 code files changed in the last 6 months but the README was not touched — it may no longer reflect the system.
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.
Add a README to the 31 of 46 project(s) that lack one — worth up to 1.3 pts.
Review the README against recent changes; refresh the parts that drifted.
Maturity · Maturity — Whether key decisions (ADRs) and the high-level shape (C4/diagrams) are written down.
Method: Filesystem scan: ADR folder/naming conventions or content, plus Mermaid/PlantUML/C4/architecture.md discovery. Exhaustive, deterministic.
No Architecture Decision Records found — no conventional ADR directory, no `NNNN-title.md` documents and nothing ADR-shaped by content. Design rationale recorded elsewhere (a design-notes tree, a mailing list, pull-request discussion) is not visible to this check and is not re-findable per decision, so a future maintainer cannot ask why one choice was made and get an answer.
No C4/PlantUML/Mermaid diagram or architecture.md — the high-level shape isn't documented.
What to do
Record significant decisions one document per decision — dated, stating the context, the decision and its consequences — and keep them together wherever your design docs already live (a conventional `docs/adr/` tree with `NNNN-title.md` names is the most discoverable form).
Add a C4 context/container diagram (Structurizr, PlantUML or Mermaid) or an architecture.md overview.
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.
Only 31/76 projects share a common root namespace — the code's module identity is inconsistent.
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.
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.
Only 2/21 service-like projects use logging (pure contract/DTO projects are excluded — they have nothing to log). Of those 21, 6 ship a process this repository operates; the rest are libraries their consumer hosts, where the logging decision belongs to the host.
What to do
Extend structured logging across the projects you operate, and give the library ones a diagnostics seam instead — an `EventSource`/`ActivitySource` the host can subscribe to, or an optional logger on your options object — rather than taking a logging dependency on your consumers' behalf.
Add a health-check endpoint (AddHealthChecks/MapHealthChecks) so orchestrators and load balancers can probe liveness/readiness.
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 CodeQL's csharp pack (it analyses VB.NET too), or a security analyzer package (or `semgrep --config=auto`, which runs on any language) as a CI step.
What to do
Add a SAST step to CI running what this repository's stack ships: CodeQL's csharp pack (it analyses VB.NET too), or a security analyzer package — or `semgrep --config=auto`, which runs on any language — 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.
Readiness · Readiness — Whether releases are traceable — a maintained changelog and explicit version stamping.
Method: Filesystem scan: changelog file presence and version tags in csproj or git tags. Exhaustive, deterministic.
No CHANGELOG/HISTORY/RELEASES file — what shipped when isn't easy to reconstruct for support or audit. (Versioning/tagging makes releases traceable, but a changelog records the what.)
What to do
Keep a changelog (e.g. Keep-a-Changelog) recording what shipped in each release.
Readiness · Performance — Whether the library protects its performance with benchmarks — a benchmark suite, allocation/memory measurement, and (ideally) a CI gate. Presence is credited as a bonus, never a deduction.
Method: Repo + source scan: BenchmarkDotNet referenced (csproj/source), [Benchmark]/[MemoryDiagnoser] attribute counts, and a benchmark step in CI — scored as a bonus ladder (absence is neutral, never a deduction). Deterministic, presence detection.
Readiness · Performance — Whether the code is written to minimise allocations so it doesn't pressure its host's memory manager — buffer/slice views over copies, object pooling, stack or value-type allocation, and buffer writers. Reward-only: credited where present, never penalised where a simpler style is fine.
Readiness · Performance — Whether asynchronous code keeps its host responsive — a library awaits with ConfigureAwait(false) (so it never captures and stalls the host's context) and avoids sync-over-async blocking (.Wait()/.GetAwaiter().GetResult()) that wastes threads and risks deadlock.
Method: Production-source scan: sync-over-async blocking (.Wait()/.GetAwaiter().GetResult()) counted everywhere, and — for a library with ≥5 awaits — the share of awaits using ConfigureAwait(false). Deterministic, syntax/text detection.
9 blocking call(s) on async work (.Wait()/.GetAwaiter().GetResult()) — these waste a thread and can deadlock in a consumer with a synchronization context.
What to do
Make the call chain async end-to-end and await it — never block on a Task with .Wait()/.GetAwaiter().GetResult() in library code.
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.
Blocking on a Task with `.Wait()`/`.GetAwaiter().GetResult()` can deadlock (and wastes a thread). Prefer awaiting it: make the caller `async` and `await` instead. Where a synchronous entry point must stay — a public sync API you cannot break, or a process entry point that must not return until the work finishes — the block belongs in ONE documented bridge and never inside code that is already async; and where it already is that bridge, give the wait a TIMEOUT so a hung task fails the call instead of hanging the process. (×8) — Utility.cs:35, Connection.cs:46, Connection.cs:38, …
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 118/191 async methods accept a CancellationToken, so in-flight work can't be stopped early when the caller gives up — whatever ends it in your host (shutdown signal, timeout, abandoned request, user cancel). Thread a token through the call chain and honour it at each await and loop; where a method genuinely cannot be interrupted, omitting it is a deliberate choice — judge against your hosting model.
No CancellationToken parameter — this work can't be stopped early once started. (×25) — ChannelMapper.cs:194, AConnection.Consumers.cs:21, AConnection.cs:67, …
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.
An empty catch block silently discards the error — failures vanish with no log and no rethrow. Log it, handle it, or don't catch it. — Subscription.cs:61
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.
~2.6 `!` suppressions per 1k syntax nodes — 200 suppression(s) across the 78405 syntax node(s) in code where nullable warnings are ENABLED, which is the only code a `!` can suppress anything in (a `!` under `#nullable disable` is inert and is not counted, and its file's nodes are not in the denominator). 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 — 46 check(s) not relevant to this codebase
These checks had nothing to measure here (no tests, no git history, the codebase is small, or the architecture style doesn't apply), so they're omitted above rather than scored low.
AC1 Text alternatives — 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
AX2 Stateful singletons — no singleton implementations detected
AX7 Slice cohesion — not applicable — not a vertical-slice architecture
AX9 CQS / query purity — no CQRS query handlers detected — query purity is not applicable to this codebase
AXB2 Runtime readiness — Advisory — this card reports evidence and never carries a score, so there is nothing missing here.
C2 Access Controls — No access-control surface detected in the analyzed source — no web/app surface to authorize (no HTTP API or web-UI project) and no authorization code at all (no [Authorize]/policies, no imperative guard methods). Access control is therefore N/A here — this is a library/CLI, which is authorized by its CALLER, not by itself. If this codebase grows request handlers, the dimension reactivates and a default-deny posture is expected then.
C3 Audit Trail — Repo shows no audit-logging mechanism (IAuditable, an immutable audit log, an EF SaveChanges interceptor) for sensitive changes — absence of evidence is not evidence of a working control. Record an audit trail in code (or document where it lives) so this dimension can be scored.
C4 Data Retention — Repo shows no data-retention / TTL / cleanup mechanism for personal data — absence of evidence is not evidence of a working control. Define retention periods and a purge/cleanup job (or TTL) in code, or document where retention is enforced, so this dimension can be scored.
C5 Data-Subject Rights — Repo shows no corroborated data-subject-rights mechanism (erasure / export-portability / consent) tied to a subject id or GDPR vocabulary — absence of evidence is not evidence of a working control. Implement erasure, data export/portability and consent tracking over the subject's records.
D11 Test Reliability — Test reliability not included
D19 Documentation Quality — LLM evaluation failed
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 — Bounded contexts not declared
D25 ADR Conformance — no ADRs to check
D31 IaC & Container Security — No Infrastructure-as-Code or container manifests found (Dockerfile, Terraform, Kubernetes/Helm, CloudFormation); nothing to 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.
D36 Supply-chain Provenance & Signing — The CI pipeline builds and tests but publishes no released artifact — no package publish, container push, GitHub release or deployment step. Supply-chain provenance, signing and SBOM attest RELEASED artifacts, so there is nothing to attest here. Add them to the release pipeline when this repo starts shipping artifacts (a published package, a container image, a deployed service or a tagged release).
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.
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 — Coupling not included (check did not complete)
D7 Architectural Integrity — no checkable ADRs and no dependency cycles — architectural integrity not assessed
DM1 Domain Modelling — not scored — this repository shows only 1 of the 3 signals this check looks for (6 value object(s))
ED1 Event-Driven — not scored — this repository shows only 1 of the 3 signals this check looks for (a message-bus package)
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
P12 CI test-gate honesty — Reported, not scored — this card publishes what the CI gate does with the test inventory rather than grading it. The findings above are its output.
P4 Deployment & Rollback — not evidenced — no deploy/rollback/approval signal in the repo; absence of evidence is not evidence of a manual release
P5 DR & Backup — not evidenced — repo shows no backup/RTO/RPO controls; absence of evidence is not evidence of a working control
P7 Outbound HTTP resilience — not applicable — this isn't a service/API/worker
P8 Schema migrations — no EF Core usage detected
P9 Domain vs controller coverage — coverage data present but no domain-layer files were identified (no /Domain//Aggregates/ paths)
S1 Web-Security Posture — No web surface detected in the analyzed source — no HTTP API or web-UI project (no controllers/minimal-API endpoints, no Razor/Blazor views) and no web middleware (HTTPS redirection, HSTS, security headers, cookies). Transport security, security headers, secure cookies, CSRF/input-validation and middleware-order controls are therefore N/A here — this is a library/CLI/worker, not a web app. Crypto hygiene was still checked and found nothing to flag. If this codebase becomes web-facing, the dimension reactivates automatically.
SC1 Supply-chain hygiene — Advisory — this card reports evidence and never carries a score, so there is nothing missing here.
X6 Hand-rolled structured-format parsing — Reported, not scored — this card publishes what it found rather than grading it. Its content is the findings and the key metric above.
X7 Silent fallback defaults — Reported, not scored — this card publishes what it found rather than grading it. Its content is the findings and the key metric above.
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.
High: github-actions-mutable-action-tag .github/workflows/unittests.yml:18— GitHub Actions step uses a mutable tag or branch reference. Tags and branch names can be silently repointed by the action owner, enabling supply-chain attacks — as seen in the trivy-action and kics-github-action compromises. Pin the reference to a full 40-character commit SHA instead, e.g. `uses: actions/checkout@<40-character SHA>`. This step references `actions/checkout@v4`; resolve the SHA it points at today with `gh api repos/actions/checkout/commits/v4 --jq .sha`.
High: github-actions-mutable-action-tag .github/workflows/unittests.yml:20— GitHub Actions step uses a mutable tag or branch reference. Tags and branch names can be silently repointed by the action owner, enabling supply-chain attacks — as seen in the trivy-action and kics-github-action compromises. Pin the reference to a full 40-character commit SHA instead, e.g. `uses: actions/setup-dotnet@<40-character SHA>`. This step references `actions/setup-dotnet@v4`; resolve the SHA it points at today with `gh api repos/actions/setup-dotnet/commits/v4 --jq .sha`.
EmptyCatchBlock Connectors/ApachePulsar/Subscription.cs:61— empty catch block — the error is discarded with nothing recorded, so a failure here leaves no trace anywhere. Narrow the catch to the exception you actually expect, record it through whatever this codebase already uses to report problems, or — where swallowing really is correct, as it often is on a teardown/dispose path where throwing would mask the original failure — write down WHY in a comment on the catch. The comment has to give the reason: a note that only restates the swallow ("ignored", "do nothing") is read as no explanation at all and leaves this row in place. Any of the three makes the decision reviewable; all three clear this row.
Dead code: BasicMessageToNameAndVersionMessage Testing/Core/Converters/BasicMessageToNameAndVersionMessage.cs:6— NamedType BasicMessageToNameAndVersionMessage — no references found in solution.
Dead code: NoChannelMessageToBasicMessage Testing/Core/Converters/NoChannelMessageToBasicMessage.cs:6— NamedType NoChannelMessageToBasicMessage — no references found in solution.
Dead code: NonContextConverter Testing/CodeGenTesting/Converters/NonContextConverter.cs:6— NamedType NonContextConverter — no references found in solution.
Dead code: NonContextMessageEncoder Testing/CodeGenTesting/Encoders/NonContextMessageEncoder.cs:6— NamedType NonContextMessageEncoder — no references found in solution.
Dead code: NonContextMessageEncryptor Testing/CodeGenTesting/Encryptors/NonContextMessageEncryptor.cs:7— NamedType NonContextMessageEncryptor — no references found in solution.
Medium: watchdog-personal-data-in-log Samples/Messages/AnnouncementConsumer.cs:15— Personal data appears to be written to a log or console sink. Under GDPR Article 5(1)(c) logs should carry the minimum needed to operate the system; prefer a pseudonymous identifier over the personal value itself.
Medium: watchdog-personal-data-in-log Samples/Messages/SampleExecution.cs:35— Personal data appears to be written to a log or console sink. Under GDPR Article 5(1)(c) logs should carry the minimum needed to operate the system; prefer a pseudonymous identifier over the personal value itself.
Medium: watchdog-personal-data-in-log Samples/Messages/SampleExecution.cs:51— Personal data appears to be written to a log or console sink. Under GDPR Article 5(1)(c) logs should carry the minimum needed to operate the system; prefer a pseudonymous identifier over the personal value itself.
Medium: watchdog-personal-data-in-log Samples/Messages/SampleExecution.cs:63— Personal data appears to be written to a log or console sink. Under GDPR Article 5(1)(c) logs should carry the minimum needed to operate the system; prefer a pseudonymous identifier over the personal value itself.
TooManyMethods: AConnection Core/Connections/AConnection.cs:0— TooManyMethods — 712 significant lines (blank, comment-only and punctuation-only lines excluded), 74 methods, declared across 3 files: Connections/AConnection.cs (46), Connections/AConnection.Consumers.cs (18), Connections/AConnection.Resilience.cs (10). To reduce it, group the members that share the same data into a smaller type of their own and delegate to it, so no single type carries every responsibility.
TooManyMethods: MessageContext Core/MessageContext.MessageTypes.cs:0— TooManyMethods — 317 significant lines (blank, comment-only and punctuation-only lines excluded), 34 methods, declared across 6 files: Core/MessageContext.MessageTypes.cs (13), Core/MessageContext.Converter.cs (7), Core/MessageContext.Encoding.cs (6), Core/MessageContext.QueryResponse.cs (5), +2 more file(s). To reduce it, group the members that share the same data into a smaller type of their own and delegate to it, so no single type carries every responsibility.
MessageContextGenerator.Generate (cyclomatic 21) Generators/MessageContextGenerator.cs:56— MessageContextGenerator.Generate has cyclomatic complexity 21 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
LLM evaluation failed — JSON parse error: Expected end of string, but instead reached end of data. Path: $.findings[0].issue | LineNumber: 0 | BytePositionInLine: 1055.
MessageContextGenerator.Generate (cognitive 66) Generators/MessageContextGenerator.cs:56— MessageContextGenerator.Generate has cognitive complexity 66 (threshold 15). To reduce it, flatten the nesting: invert conditions into early returns or guard clauses so the happy path stays at one level, and lift the deepest nested block into its own named function.
FileTooLong: Connections/AConnection.cs Core/Connections/AConnection.cs:0— FileTooLong — 510 significant lines (blank, comment-only and punctuation-only lines excluded). To reduce it, split the file along the responsibilities already in it: move each cohesive group of declarations into its own sibling file in the same module or package, so no one file has to be read whole to change one of them.
Duplicated block (8 lines × 2) Core/MessageContext.MessageTypes.cs:77— Core/MessageContext.MessageTypes.cs:77-84 | Generators/MessageContextGenerator.cs:372-379 — the copies span different directories, so extracting a shared function means choosing where it lives: put it somewhere both call sites can already reach — a location they all depend on today, or a new shared one if there is none — and call it from each site; until then, every change has to be made twice. Read the line range as the matched WINDOW rather than a finished unit: at `Core/MessageContext.MessageTypes.cs:77` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
Low cohesion: MultiServiceConnection (LCOM4 4) Core/Connections/MultiServiceConnection.cs:14— MultiServiceConnection's methods form 4 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.
Recommendation — 8 finding(s)
D21 · Naming Consistency· Inconsistent naming for registering a consumer for query responses. One method is named 'SubscribeQueryResponseAsync' while the other is 'RegisterQueryResponseConsumerAsync'. These appear to serve the same conceptual purpose of setting up a handler for query responses, but use different verbs ('Subscribe' vs 'Register') and object names ('QueryResponse' vs 'QueryResponseConsumer'). · ×2
Inconsistent naming for registering a consumer for query responses. One method is named 'SubscribeQueryResponseAsync' while the other is 'RegisterQueryResponseConsumerAsync'. These appear to serve the same conceptual purpose of setting up a handler for query responses, but use different verbs ('Subscribe' vs 'Register') and object names ('QueryResponse' vs 'QueryResponseConsumer'). — Standardize to 'RegisterQueryResponseConsumerAsync' or 'SubscribeQueryResponseAsync' consistently across all connection types. (symbols: MQContract.Connections.AConnection<TContractConnection>.MQContract.Interfaces.IBaseContractConnection.SubscribeQueryResponseAsync<TQuery, TQueryResponse>, MQContract.Connections.AConnection<TContractConnection>.MQContract.Interfaces.IConsumerContractConnection<TContractConnection>.RegisterQueryResponseConsumerAsync)
Inconsistent naming for registering a consumer for query responses. One method is named 'SubscribeQueryResponseAsync' while the other is 'RegisterQueryResponseConsumerAsync'. These appear to serve the same conceptual purpose of setting up a handler for query responses, but use different verbs ('Subscribe' vs 'Register') and object names ('QueryResponse' vs 'QueryResponseConsumer'). — Standardize to 'RegisterQueryResponseConsumerAsync' or 'SubscribeQueryResponseAsync' consistently across all connection types. (symbols: MQContract.Connections.AConnection<TContractConnection>.MQContract.Interfaces.IBaseContractConnection.SubscribeQueryResponseAsync, MQContract.Connections.AConnection<TContractConnection>.MQContract.Interfaces.IConsumerContractConnection<TContractConnection>.RegisterQueryResponseConsumerAsync)
D11 · Test Reliability· Test reliability not included · ×1
Test reliability not included — Test reliability not included — no test tier completed within its budget.
D16 · Bus Factor· Small-team knowledge concentration · ×1
Small-team knowledge concentration — 30 file(s) are concentrated to one author — the ambient state with 2 active author(s), not 30 separate risks. The signal becomes meaningful as ownership spreads; no per-file action implied now.
Thin analysable surface across projects — 5 project(s) carry only a thin slice of real code (e.g. `HiveMQSample` with 11 significant line(s)). The mean analysable-surface weight is 80 %, lowering Solution Shape by about 1.6 point(s). Consolidate thin projects or grow them into substantial, well-scoped assemblies.
D23 · Boundary Type-Coupling· Bounded contexts not declared · ×1
Bounded contexts not declared — At 152k LoC split over 76 projects the codebase is both large and multi-module, so explicit bounded contexts are warranted. Name this codebase's bounded contexts (≥2 module groups, e.g. per subsystem) so cross-boundary type coupling can be assessed. Declare them in `.codehealth/config.yaml` at the repository root (create it if absent), mapping each context name to the module-path or namespace prefixes that belong to it — e.g. `architecture:` → `contexts:` → `Billing: ["src/billing", "Acme.Billing"]`, `Catalog: ["src/catalog", "Acme.Catalog"]`.
redundant comment Connectors/RabbitMQ/Connection.cs:203— "this may throw an error is the queue already exists but checking for it fails" — trim - the check-for-queue-exists guard already says "may throw"
D5 · Coupling· Coupling not included (check did not complete) · ×1
Coupling not included (check did not complete) — Coupling could not be assessed in this run — the check did not complete, so it is not scored. This is a gap in the analyzer, not a finding about this repository.
Per-file coverage withheld — the coverage run did not finish — At least one `dotnet test` target was stopped at its time budget, so the gathered reports cover only part of the suite. The overall 96.0% is a FLOOR (everything the completed runs covered really is covered), but a per-file figure from a partial gather cannot tell an untested file from one whose test project never ran — so per-file and CRAP rows are withheld rather than published as measurements. Re-run with a longer coverage budget, or commit the Cobertura/OpenCover/lcov report your CI already produces, to get per-file rows.
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.
trivy: not applicable — No Infrastructure-as-Code or container manifests found (Dockerfile, Terraform, Kubernetes/Helm, CloudFormation); nothing to scan.
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.
provenance: not applicable — The CI pipeline builds and tests but publishes no released artifact — no package publish, container push, GitHub release or deployment step. Supply-chain provenance, signing and SBOM attest RELEASED artifacts, so there is nothing to attest here. Add them to the release pipeline when this repo starts shipping artifacts (a published package, a container image, a deployed service or a tagged release).
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.
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.
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.
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
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Run 019fc88f-9561-7751-a72b-1d5698640a30 · 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 — 8 field(s) across 2 categories, 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: 3 · Warnings: 17 · Recommendations: 8 · Info: 33 — Appendix A · all findings · full markdown report.
Generated by Watchdog — deterministic code-health analysis. 03-08-2026 @ 16:58 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.