Actuarial computation/reporting framework and tooling
Public report — Base, published 29 Jun 2026.
Concrete security findings (CVE IDs, secret matches, dependency versions) are hidden in this version;
ask the repo owner for the full report.
101findings with an exact file:lineof 125 — the remainder are repo-wide signals (a dimension-level measurement, not a single line); open any file:line and verify
47/93dimensions across the health lenses7109 LoC · 26 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.
ActuarialIntelligence/Base carries serious risk (46%). Several issues below can materially affect reliability, security, or the cost of change and warrant near-term attention.
The area that most needs attention is Readiness (37%) — operating, monitoring and recovering the system safely is harder. Maturity (47%) is the next concern — onboarding is slow — key decisions and the architecture aren't written down, so contributors have to reverse-engineer the intent.
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.
This codebase represents roughly ~0.1 person-years of build effort (about ~€12,000 to rebuild). Its weakest lens is Readiness at 37% — the part of that asset most exposed by the findings below.
How we model this: boilerplate at a scaffolding rate + logic × domain Standard (×1.1) — service/app × 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
Adopt ILogger (or Serilog) and log at meaningful points across the projects.
Value concentrated against a weak lens · High · Value at risk
This is a Small asset (~0.1 person-years to rebuild), and its weakest lens is Readiness at 37%. The operational and business risk on an asset this size concentrates there — that's where remediation buys the most protection.
→ Direct remediation budget at Readiness first — highest risk-reduction per euro on an asset this size.
Highest-leverage move · Medium · Leverage
Of everything flagged, the best return on effort is: Adopt ILogger (or Serilog) and log at meaningful points across the projects. The rest can wait behind it.
Evidence: priority ranking: top of 5 ranked by impact/effort
→ Adopt ILogger (or Serilog) and log at meaningful points across the projects.
Improving trajectory · Info · Trajectory
The headline is improving steadily (+1.8 pts/run over 13 runs) — whatever you're doing is working; keep the gate.
Evidence: trajectory: +1.8 pts/run over 13 runs
Trajectory
Where this codebase is heading — something a one-shot snapshot cannot show. 46 ▲ 7 over 13 runs.
Project dependencies, layered top-to-bottom; arrows show direction. Any dashed red edge points upward or sideways — a layering smell or cycle. A clean layered graph has none.
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
47
High / Critical
Roadmap
First, implement comprehensive logging using ILogger or Serilog across all projects to establish baseline observability. Next, enhance outbound HTTP resilience by adding standard resilience handlers to HttpClient registrations to prevent cascading failures. Then, introduce an approval gate for production deployments to ensure proper review before release. Additionally, enforce test suite execution in CI to block merges that break the build. Finally, begin documenting significant architectural decisions to improve long-term maintainability.
Ranked by impact ÷ effort. "Helps" is the estimated gain on the 0–100 health score.
Do this
Helps
Effort
Dimension
Adopt ILogger (or Serilog) and log at meaningful points across the projects.
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. 45 of 47 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 0.6 — 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 — 47 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, 101 of 125 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.
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.
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.
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.
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 (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.
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.
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.
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.
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.
P4 Deployment & Rollback: Approval/branch-protection rules live in repository settings the scan cannot see — only their in-repo evidence (config files, workflows) is checked, so a control enforced purely in the host's settings reads as "not evidenced".
The LLM boundary
LLM-set scores this run (4): D20, 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.
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.
Detailed fixes: d2_recommendation.md · top locations in Appendix A, every location in findings.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.
+ 2 more group(s) — more in Appendix A; the complete list is findings.md.
✓ On the Gold path — maintain.
Detailed fixes: d4_recommendation.md · top locations in Appendix A, every location in findings.md.
Do you agree with this assessment?
D5 · Coupling9.4 / 10Exemplary✓ Tool-verified
What it measures: Whether volatile projects sit underneath others that depend on them (so their churn ripples upward), and whether project dependencies form cycles. A widely-depended-on but stable shared/kernel project is healthy, not penalised.
Method: Dependency cycles via elementary-DFS over real .csproj references, plus Martin instability (afferent/efferent) per project. Exhaustive over the reference graph, deterministic.
Coverage: Exhaustive · type-level: afferent/efferent coupling + cycles computed over every production type — the population is all types, not a name convention.
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.
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.
7 test methods: 7 unit, 0 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.
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.
7 source file(s) have their living knowledge concentrated in one author (≥90% of recent, decayed contribution). The largest is src/BasicTTS/FormMainHelpers.cs.
Off-boarding risk: anonymized user #1
What to do
Resolve the 1 Off-boarding risk finding(s) in Bus Factor. — One of this dimension's main actionable groups (1 recommendation-level).
Detailed fixes: d16_recommendation.md · top locations in Appendix A, every location in findings.md.
Do you agree with this assessment?
D17 · Explicit Debt7.7 / 10Strong✓ Tool-verified
What it measures: Acknowledged debt left in the code — TODOs, dead code, suppressed warnings.
Method: Roslyn syntactic debt markers (suppressions/TODO/FIXME/HACK/empty-catch/commented-code/Obsolete) plus SymbolFinder dead-code analysis; weighted-debt-per-KLoC density deducted 2.0x per unit. Deterministic, exhaustive.
Resolve the 42 CommentedOutCode finding(s) in Explicit Debt — start with ConnectedInstruction.cs (11), KXQueryConnection.cs (10), Program.cs (6). — One of this dimension's main actionable groups (42 warning-level).
Enforce Explicit Debt in CI to reach Verified (currently Documented). — Hardens enforcement from Documented toward Prevented — provenance only; does not change the score.
Detailed fixes: d17_recommendation.md · top locations in Appendix A, every location in findings.md.
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.
Resolve the 1 Shell project finding(s) in Solution Shape — start with AI.TensorFlow.csproj. — One of this dimension's main actionable groups (1 recommendation-level).
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 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.
6 naming inconsistencies across 200 sampled symbols.
Inconsistent spelling of 'Memory' as 'Memmory' in the class name 'NeuralMemmory', while other related classes like 'NeuralLearner' use correct spelling.
Inconsistent naming for properties related to object dimensions and metrics. 'Height' and 'Width' are used for 'NeuralObject', but 'MarginOfAllowledError' (misspelled) is used for 'Objective'. This suggests a lack of consistent naming for object properties across different classes.
Inconsistent naming for matrix and vector types. '_3Matrix' and '_3Vector' use a leading underscore and numeric suffix, while other matrix types like '_nMatrix' and '_mnMatrix' use different patterns. This suggests a lack of consistent naming for generic or specific dimension types.
Inconsistent naming for matrix types. '_nMatrix' and '_mnMatrix' use a leading underscore and alphanumeric suffix, but '_3Matrix' does not follow this pattern.
Inconsistent naming for properties in 'HazardPdfCdfHazardTriplet'. 'populationGroupID' uses camelCase with an ID suffix, while 'cdf' is all lowercase. This inconsistency in casing and suffix usage should be addressed.
+ 1 more group(s) — more in Appendix A; the complete list is findings.md.
What to do
Resolve the 1 Inconsistent spelling of 'Memory' as 'Memmory' in the class name… finding(s) in Naming Consistency. — One of this dimension's main actionable groups (1 recommendation-level).
Resolve the 1 Inconsistent naming for properties related to object dimensions and… finding(s) in Naming Consistency. — One of this dimension's main actionable groups (1 recommendation-level).
Resolve the 1 Inconsistent naming for matrix and vector types. '_3Matrix' and… finding(s) in Naming Consistency. — One of this dimension's main actionable groups (1 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 / 10Weak◐ 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.
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).
High IaC: KSV-0014 · ×11manifests/deployment.ymldetected by trivy finding
Medium IaC: KSV-0001 · ×9manifests/deployment.ymldetected by trivy finding
Low IaC: KSV-0003 · ×27manifests/deployment.ymldetected by trivy finding
What to do
Resolve the 27 Low IaC finding(s) in IaC & Container Security — start with deployment.yaml (11), deployment.yml (11), Dockerfile (2). — One of this dimension's main actionable groups (27 recommendation-level).
Resolve the 11 High IaC finding(s) in IaC & Container Security — start with deployment.yaml (3), deployment.yml (3), Dockerfile (2). — One of this dimension's main actionable groups (11 issue-level).
Resolve the 9 Medium IaC finding(s) in IaC & Container Security — start with deployment.yml (5), deployment.yaml (4). — One of this dimension's main actionable groups (9 warning-level).
Detailed fixes: d31_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.
23 of 23 significant source file(s) are orphaned — their living knowledge has decayed to nothing, so no one currently understands them. The largest is src/ConnectedInstructionEXE/Program.cs.
Dormant codebase
What to do
Resolve the 1 Dormant codebase finding(s) in Knowledge Freshness. — One of this dimension's main actionable groups (1 recommendation-level).
Detailed fixes: d34_recommendation.md · top locations in Appendix A, every location in findings.md.
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.
What it measures: Whether the build pipeline provides supply-chain integrity — generated provenance/attestation, signed artifacts (cosign/sigstore), an SBOM, and pinned build actions. Presence of the configuration, not a runtime guarantee.
Method: Supply-chain provenance/signing read deterministically from CI/build config (.github/workflows, .gitlab-ci.yml, azure-pipelines, Jenkinsfile, .circleci) + the release surface: four signals — generated provenance/attestation (SLSA/in-toto/actions-attest), artifact signing (cosign/sigstore/gitsign), an SBOM (syft/sbom-action/*.spdx.json/*.cdx.json), and SHA-pinned build actions — scored 10·present/denom. NotApplicable without a build pipeline. Detects configuration presence, not runtime enforcement.
Resolve the 1 Unpinned build actions finding(s) in Supply-chain Provenance & Signing. — One of this dimension's main actionable groups (1 warning-level).
Resolve the 1 No build provenance finding(s) in Supply-chain Provenance & Signing. — One of this dimension's main actionable groups (1 recommendation-level).
Resolve the 1 No artifact signing finding(s) in Supply-chain Provenance & Signing. — One of this dimension's main actionable groups (1 recommendation-level).
Detailed fixes: d36_recommendation.md · top locations in Appendix A, every location in findings.md.
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 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.
`ActuarialIntelligence.Domain.Tests` is a Domain project but references `ActuarialIntelligence.Infrastructure.Interfaces`, a Infrastructure project. The clean-architecture rule is that dependencies point INWARD — the domain/application core must not depend on outer layers (infrastructure/web). Invert it: define the abstraction in the inner layer and implement it in the outer one.
`ActuarialIntelligence.Domain` is a Domain project but references `ActuarialIntelligence.Infrastructure.Data`, a Infrastructure project. The clean-architecture rule is that dependencies point INWARD — the domain/application core must not depend on outer layers (infrastructure/web). Invert it: define the abstraction in the inner layer and implement it in the outer one.
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 · 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. (×11) — TermCashflowYieldSet.cs:25, Interpolation.cs:16, ImageToModelContainer.cs:19, …
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.
`Equals` is a shipped member whose whole body throws NotImplementedException — scaffolding that was never completed. Implement it or remove the dead surface. — TermCashflowYieldSet.cs:23
`Interpolate` is a shipped member whose whole body throws NotImplementedException — scaffolding that was never completed. Implement it or remove the dead surface. — Interpolation.cs:14
`GenerateFacialExpressionsBasedOnText` is declared `async` but never awaits anything, so it runs synchronously while pretending to be asynchronous. Drop `async` or do the real async work. — MoveHelpers.cs:14
`MouthMove` is declared `async` but never awaits anything, so it runs synchronously while pretending to be asynchronous. Drop `async` or do the real async work. — MoveHelpers.cs:51
`EyesBlink` is declared `async` but never awaits anything, so it runs synchronously while pretending to be asynchronous. Drop `async` or do the real async work. — MoveHelpers.cs:62
`RandomlyMoveAngryEyes` is declared `async` but never awaits anything, so it runs synchronously while pretending to be asynchronous. Drop `async` or do the real async work. — MoveHelpers.cs:74
`MoveWorriedEyesRandomly` is declared `async` but never awaits anything, so it runs synchronously while pretending to be asynchronous. Drop `async` or do the real async work. — MoveHelpers.cs:121
`btnPlay_Click` is declared `async` but never awaits anything, so it runs synchronously while pretending to be asynchronous. Drop `async` or do the real async work. — frmMain.cs:83
A line of code has been commented out rather than removed — dead weight that rots and confuses. Delete it (version control remembers). (×22) — FormMain.cs:30, FormMain.cs:33, FormMain.cs:34, …
What to do
Finish or delete the unfinished stubs (NotImplementedException / empty / constant-returning bodies) — they are dead surface that looks live.
Clear the softer debt: remove commented-out code and dead branches, re-enable or delete skipped tests, and replace blanket warning suppressions with targeted ones.
Maturity · Maturity — Whether the repo and its projects have a README, and whether it's substantive and current.
Method: Filesystem scan: README presence, word count, and headings for depth; git history for staleness. Exhaustive across root and project dirs, deterministic.
What to do
Add a README to the 26 of 26 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.
Test projects aren't grouped under a tests/ folder — the test surface isn't separable from production code at a glance.
Only 12/26 projects share a common root namespace — the code's module identity is inconsistent.
What to do
Group test projects under tests/ (or test/, spec/) 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.
Do you agree with this assessment?
P1 · CI/CD gates8.5 / 10Strong✓ 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.
A CI pipeline exists and the word "test" appears, but no explicit test-runner invocation (dotnet test / npm test / pytest / a test job) was matched — the gate may be running tests, or "test" may be incidental (a path, "latest", a reporter). Make the test step explicit so the gate is unambiguous.
What to do
Run the test suite in CI via an explicit runner step (e.g. `dotnet test`) and gate merges on it.
Do you agree with this assessment?
P2 · Observability0.0 / 10Critical✓ 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.
What to do
Add an approval/environment gate (required reviewers / protection rules) before production promotion.
Readiness · Readiness — Whether outbound HTTP calls are wrapped in resilience (retry/timeout/circuit-breaker) so a failing dependency doesn't cascade.
Method: Roslyn scan: Polly resilience markers (Retry, CircuitBreaker, Timeout) on outbound HTTP invocations. Computed per type, deterministic.
The app makes outbound HTTP calls but no resilience handler was detected (Polly / AddStandardResilienceHandler / circuit-breaker). A slow or failing dependency will cascade — add timeouts, retries with back-off, and a circuit breaker.
What to do
Add `AddStandardResilienceHandler()` (or Polly policies) to your HttpClient registrations so a flaky dependency can't take the app down.
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.
Do you agree with this assessment?
X1 · Async correctness4.3 / 10Weak✓ Tool-verified
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). Make the caller `async` and `await` instead. (×2) — HIVEConnection.cs:18, LogAnalyticsConnectorController.cs:36
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 0/6 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. (×6) — frmMain.cs:83, MoveHelpers.cs:14, MoveHelpers.cs:51, …
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.
2/7 projects enable <Nullable>enable</Nullable>. NRTs catch a whole class of null-deref bugs at compile time.
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.
Capped at Fair by a Critical contributor — resolve it before relying on this lens.
Not included — 47 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
AX8 Test isolation — no test/production split to check
AX9 CQS / query purity — no CQRS query handlers detected — query purity is not applicable to this codebase
AXB2 Runtime readiness — no data
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.
C2 Access Controls — Partial workspace load — the web/host project 'KubernetesLogAnalyticsConnector' present on disk did not load into the C# workspace (it failed to build/restore and was silently dropped), so its authorization signals were never scanned. The surviving documents carry no evidence for this control, but absence of evidence in an INCOMPLETE document set is not evidence the control is missing — the relevant middleware/attributes most plausibly live in the dropped project. This dimension is therefore Not-evidenced (excluded from the score) rather than asserting a confident negative. Restore/build that project (see diagnostics.md) so it loads and the dimension can be scored.
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.
D11 Test Reliability — No tests discovered
D19 Documentation Quality — LLM evaluation failed
D22 Internal API Consistency — No exposed public API
D23 Boundary Type-Coupling — Bounded contexts not declared
D25 ADR Conformance — no ADRs to check
D27 Navigability — symbol resolution incomplete — navigability not assessed
D30 Dependency Vulnerabilities — 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) — No PII/GDPR-handling patterns detected (p/gdpr ruleset) — no data-compliance surface to assess.
D33 JS/npm Dependency Vulnerabilities — 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.
D37 Vulnerability-disclosure Policy — 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 — No JS/npm lockfile found outside bin/obj (package-lock.json, yarn.lock, pnpm-lock.yaml, bun.lockb); nothing for OSV to scan.
D39 IL Efficiency — The target did not build, so no IL was available to measure.
D7 Architectural Integrity — no checkable ADRs and no dependency cycles — architectural integrity not assessed
D8 Code Coverage — Coverage not measured — test suite did not build
DM1 Domain Modelling — not run — only 1/3 markers (a Domain/Aggregates/ValueObjects layer)
ED1 Event-Driven — not run — 0/3 markers found
ED5 Idempotency — no mutating command handlers or message consumers detected — idempotency check not applicable
ES1 Event Sourcing — not run — 0/3 markers found
P12 CI test-gate honesty — no data
P5 DR & Backup — not evidenced — repo shows no backup/RTO/RPO controls; absence of evidence is not evidence of a working control
P6 Release Hygiene — not evidenced — no changelog, version stamp or semver release tag in the repo
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.
SC1 Supply-chain hygiene — no data
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.
High IaC: KSV-0014 manifests/deployment.yml— Root file system is not read-only
High IaC: KSV-0118 manifests/deployment.yml— Default security context configured
High IaC: KSV-0118 manifests/deployment.yml— Default security context configured
High IaC: DS-0002 src/KubernetesLogAnalyticsConnector/Dockerfile— Image user should not be 'root'
High IaC: DS-0002 src/KubernetesLogAnalyticsConnector/Dockerfile.develop— Image user should not be 'root'
High IaC: KSV-0014 src/KubernetesLogAnalyticsConnector/charts/kubernetesloganalyticsconnector/templates/deployment.yaml— Root file system is not read-only
High IaC: KSV-0118 src/KubernetesLogAnalyticsConnector/charts/kubernetesloganalyticsconnector/templates/deployment.yaml— Default security context configured
High IaC: KSV-0118 src/KubernetesLogAnalyticsConnector/charts/kubernetesloganalyticsconnector/templates/deployment.yaml— Default security context configured
High IaC: DS-0002 src/KubernetesService/Dockerfile— Image user should not be 'root'
High IaC: DS-0002 src/KubernetesService/Dockerfile.develop— Image user should not be 'root'
High IaC: DS-0002 src/KubernetesService/Dockerfile.original— Image user should not be 'root'
Medium IaC: KSV-0001 manifests/deployment.yml— Can elevate its own privileges
Medium IaC: KSV-0012 manifests/deployment.yml— Runs as root user
Medium IaC: KSV-0013 manifests/deployment.yml— Image tag ":latest" used
Medium IaC: KSV-0104 manifests/deployment.yml— Seccomp policies disabled
Medium IaC: KSV-0117 manifests/deployment.yml— Prevent binding to privileged ports
Medium IaC: KSV-0001 src/KubernetesLogAnalyticsConnector/charts/kubernetesloganalyticsconnector/templates/deployment.yaml— Can elevate its own privileges
Medium IaC: KSV-0012 src/KubernetesLogAnalyticsConnector/charts/kubernetesloganalyticsconnector/templates/deployment.yaml— Runs as root user
Medium IaC: KSV-0104 src/KubernetesLogAnalyticsConnector/charts/kubernetesloganalyticsconnector/templates/deployment.yaml— Seccomp policies disabled
Medium IaC: KSV-0117 src/KubernetesLogAnalyticsConnector/charts/kubernetesloganalyticsconnector/templates/deployment.yaml— Prevent binding to privileged ports
Off the main sequence: ActuarialIntelligence.Infrastructure.Data — ActuarialIntelligence.Infrastructure.Data: 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: ActuarialIntelligence.Domain — ActuarialIntelligence.Domain: abstractness 0.05, instability 0.08, distance 0.87 — zone of pain — concrete and heavily depended-on, so it's rigid to change.
LLM evaluation failed — JSON parse error: Expected end of string, but instead reached end of data. Path: $.findings[1].suggestion | LineNumber: 0 | BytePositionInLine: 1242.
Coverage not measured — test suite did not build — Coverage NOT MEASURED: the repo's own test suite did not build (a C#/MSBuild compiler error in the test code), so no coverage could be collected. It is excluded from the score rather than counted as a near-zero defect. Fix the test build, or commit the Cobertura/OpenCover/lcov report your CI already produces, and real coverage will be measured.
Low IaC: KSV-0003 manifests/deployment.yml— Default capabilities: some containers do not drop all
Low IaC: KSV-0004 manifests/deployment.yml— Default capabilities: some containers do not drop any
Low IaC: KSV-0011 manifests/deployment.yml— CPU not limited
Low IaC: KSV-0015 manifests/deployment.yml— CPU requests not specified
Low IaC: KSV-0016 manifests/deployment.yml— Memory requests not specified
Low IaC: KSV-0018 manifests/deployment.yml— Memory not limited
Low IaC: KSV-0020 manifests/deployment.yml— Runs with UID <= 10000
Low IaC: KSV-0021 manifests/deployment.yml— Runs with GID <= 10000
Low IaC: KSV-0030 manifests/deployment.yml— Runtime/Default Seccomp profile not set
Low IaC: KSV-0106 manifests/deployment.yml— Container capabilities must only include NET_BIND_SERVICE
Low IaC: KSV-0110 manifests/deployment.yml— Workloads in the default namespace
Low IaC: DS-0026 src/KubernetesLogAnalyticsConnector/Dockerfile— No HEALTHCHECK defined
Low IaC: DS-0026 src/KubernetesLogAnalyticsConnector/Dockerfile.develop— No HEALTHCHECK defined
Low IaC: KSV-0003 src/KubernetesLogAnalyticsConnector/charts/kubernetesloganalyticsconnector/templates/deployment.yaml— Default capabilities: some containers do not drop all
Low IaC: KSV-0004 src/KubernetesLogAnalyticsConnector/charts/kubernetesloganalyticsconnector/templates/deployment.yaml— Default capabilities: some containers do not drop any
Low IaC: KSV-0011 src/KubernetesLogAnalyticsConnector/charts/kubernetesloganalyticsconnector/templates/deployment.yaml— CPU not limited
Low IaC: KSV-0015 src/KubernetesLogAnalyticsConnector/charts/kubernetesloganalyticsconnector/templates/deployment.yaml— CPU requests not specified
Low IaC: KSV-0016 src/KubernetesLogAnalyticsConnector/charts/kubernetesloganalyticsconnector/templates/deployment.yaml— Memory requests not specified
Low IaC: KSV-0018 src/KubernetesLogAnalyticsConnector/charts/kubernetesloganalyticsconnector/templates/deployment.yaml— Memory not limited
Low IaC: KSV-0020 src/KubernetesLogAnalyticsConnector/charts/kubernetesloganalyticsconnector/templates/deployment.yaml— Runs with UID <= 10000
Low IaC: KSV-0021 src/KubernetesLogAnalyticsConnector/charts/kubernetesloganalyticsconnector/templates/deployment.yaml— Runs with GID <= 10000
Low IaC: KSV-0030 src/KubernetesLogAnalyticsConnector/charts/kubernetesloganalyticsconnector/templates/deployment.yaml— Runtime/Default Seccomp profile not set
Low IaC: KSV-0106 src/KubernetesLogAnalyticsConnector/charts/kubernetesloganalyticsconnector/templates/deployment.yaml— Container capabilities must only include NET_BIND_SERVICE
Low IaC: KSV-0110 src/KubernetesLogAnalyticsConnector/charts/kubernetesloganalyticsconnector/templates/deployment.yaml— Workloads in the default namespace
Low IaC: DS-0026 src/KubernetesService/Dockerfile— No HEALTHCHECK defined
Off-boarding risk: anonymized user #1 — If anonymized user #1 becomes unavailable, 7 significant file(s) lose their only recent owner (largest: src/BasicTTS/FormMainHelpers.cs). Pair on, review, or document these before any departure.
Shell project: AI.TensorFlow src/AI.TensorFlow/AI.TensorFlow.csproj— `AI.TensorFlow` contributes only 0 significant line(s) — an empty/placeholder project is structural noise. Remove it or fold its contents into a real project.
Thin analysable surface across projects — 9 project(s) carry only a thin slice of real code (e.g. `ActuarialIntelligence.Infrastructure.Writers` with 14 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.
No ADRs found — No ADRs found at common paths; consider documenting architectural decisions in Docs/ADL/ or similar.
D21 · Naming Consistency· Inconsistent spelling of 'Memory' as 'Memmory' in the class name 'NeuralMemmory', while other related classes like 'NeuralLearner' use correct spelling. · ×1
Inconsistent spelling of 'Memory' as 'Memmory' in the class name 'NeuralMemmory', while other related classes like 'NeuralLearner' use correct spelling. — Rename 'NeuralMemmory' to 'NeuralMemory' to correct the spelling. (symbols: ActuarialIntelligence.Domain.NeuralMemmories.NeuralMemmory, ActuarialIntelligence.Domain.NeuralLearners.NeuralLearner)
D21 · Naming Consistency· Inconsistent naming for properties related to object dimensions and metrics. 'Height' and 'Width' are used for 'NeuralObject', but 'MarginOfAllowledError' (misspelled) is used for 'Objective'. This suggests a lack of consistent naming for object properties across different classes. · ×1
Inconsistent naming for properties related to object dimensions and metrics. 'Height' and 'Width' are used for 'NeuralObject', but 'MarginOfAllowledError' (misspelled) is used for 'Objective'. This suggests a lack of consistent naming for object properties across different classes. — Standardize property names for object dimensions (e.g., use 'Height' and 'Width' consistently, or use 'Size' or 'Dimensions'). (symbols: ActuarialIntelligence.Domain.NeuronParametrix.Objective.MarginOfAllowledError, ActuarialIntelligence.Domain.NeuronParametrix.Objective.Height, ActuarialIntelligence.Domain.NeuronParametrix.NeuralObject.Weight, ActuarialIntelligence.Domain.NeuronParametrix.NeuralObject.Width)
D21 · Naming Consistency· Inconsistent naming for matrix and vector types. '_3Matrix' and '_3Vector' use a leading underscore and numeric suffix, while other matrix types like '_nMatrix' and '_mnMatrix' use different patterns. This suggests a lack of consistent naming for generic or specific dimension types. · ×1
Inconsistent naming for matrix and vector types. '_3Matrix' and '_3Vector' use a leading underscore and numeric suffix, while other matrix types like '_nMatrix' and '_mnMatrix' use different patterns. This suggests a lack of consistent naming for generic or specific dimension types. — Consider renaming '_3Matrix' and '_3Vector' to follow the existing pattern (e.g., '_3Matrix' -> '_3Matrix' is okay, but ensure all specific dimension types follow a similar naming convention, e.g., '_nMatrix', '_mnMatrix', '_3Matrix'). (symbols: ActuarialIntelligence.Domain.ContainerObjects._3Matrix, ActuarialIntelligence.Domain.ContainerObjects._3Vector)
D21 · Naming Consistency· Inconsistent naming for matrix types. '_nMatrix' and '_mnMatrix' use a leading underscore and alphanumeric suffix, but '_3Matrix' does not follow this pattern. · ×1
Inconsistent naming for matrix types. '_nMatrix' and '_mnMatrix' use a leading underscore and alphanumeric suffix, but '_3Matrix' does not follow this pattern. — Rename '_3Matrix' to '_3Matrix' (if it's a specific type) or ensure all matrix types follow the same naming convention (e.g., '_nMatrix', '_mnMatrix', '_3Matrix'). (symbols: ActuarialIntelligence.Domain.Matrix._nMatrix, ActuarialIntelligence.Domain.Matrix._mnMatrix)
D21 · Naming Consistency· Inconsistent naming for properties in 'HazardPdfCdfHazardTriplet'. 'populationGroupID' uses camelCase with an ID suffix, while 'cdf' is all lowercase. This inconsistency in casing and suffix usage should be addressed. · ×1
Inconsistent naming for properties in 'HazardPdfCdfHazardTriplet'. 'populationGroupID' uses camelCase with an ID suffix, while 'cdf' is all lowercase. This inconsistency in casing and suffix usage should be addressed. — Standardize property naming to either camelCase or PascalCase consistently. For example, 'PopulationGroupId' and 'Cdf'. (symbols: ActuarialIntelligence.Domain.Calculator_Return_Objects.HazardPdfCdfHazardTriplet.populationGroupID, ActuarialIntelligence.Domain.Calculator_Return_Objects.HazardPdfCdfHazardTriplet.cdf)
D21 · Naming Consistency· Inconsistent naming for properties in 'Point' class. 'Yval' and 'Xval' use camelCase with a 'val' suffix, which is inconsistent with other naming conventions in the codebase. · ×1
Inconsistent naming for properties in 'Point' class. 'Yval' and 'Xval' use camelCase with a 'val' suffix, which is inconsistent with other naming conventions in the codebase. — Rename 'Yval' and 'Xval' to 'Y' and 'X' or 'YCoordinate' and 'XCoordinate' for clarity. (symbols: ActuarialIntelligence.Domain.ContainerObjects.Point<X, Y>.Yval, ActuarialIntelligence.Domain.ContainerObjects.Point<X, Y>.Xval)
D23 · Boundary Type-Coupling· Bounded contexts not declared · ×1
Bounded contexts not declared — At 7109 LoC across 26 projects the codebase is large and multi-module, so explicit bounded contexts are needed. Declare architecture.contexts (≥2) in config to assess cross-boundary type coupling.
Dormant codebase — 23 of 23 significant files have no living knowledge — the codebase as a whole is dormant, not 23 separate risks. Re-engage owners or document before change.
Build status unknown — The build did not finish within its budget on this run; structural metrics are reported, build/warning status is unknown.
D22 · Internal API Consistency· No exposed public API · ×1
No exposed public API — No intentionally-exposed types (IsPackable or .Contracts) to evaluate.
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.
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.
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 019f1567-dc72-7bc7-a538-5e901613b74c · every finding is also locatable in findings.md, and the complete scoring record (with exit codes + durations) in sidecar.json.
Issues: 12 · Warnings: 67 · Recommendations: 43 · Info: 3 — Appendix A · all findings · full markdown report.
Generated by Watchdog — deterministic code-health analysis. 29-06-2026 @ 22:02 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.