Public report — TheMicroServices, published 29 Jul 2026.
Concrete security findings (CVE IDs, secret matches, dependency versions) are hidden in this version;
ask the repo owner for the full report.
41findings with an exact file:lineof 84 — the remainder are repo-wide signals (a dimension-level measurement, not a single line); open any file:line and verify
62/107dimensions across the health lenses2358 LoC · 36 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.
habibsql/TheMicroServices carries serious risk (42%). Several issues below can materially affect reliability, security, or the cost of change and warrant near-term attention.
It is strongest in Architecture (86%) — the structure is clean and changes stay contained. Event-Driven (84%) is solid too.
Most urgent: a critical security exposure was detected (see the Security & Compliance lens). Treat it as a priority regardless of the overall grade.
The area that most needs attention is Readiness (36%) — operating, monitoring and recovering the system safely is harder. Security (38%) is the next concern — exposure to security and compliance incidents is elevated.
Leadership focus, highest impact first: CI workflow that builds and runs the test suite on every push/PR (CI/CD gates); Extend structured logging to the remaining service-like… (Observability); readiness/liveness probes and a rolling-update (or blue/green)… (Deployment & Rollback).
For scale: Small (~2,358 production lines); rebuilding it from scratch would take roughly ~0.1 person-years (~1 engineer). Approximate, ±~30%.
It builds on a genuinely strong Architecture foundation (86%); 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 ~0.1 person-years of build effort (about ~€2,800 to rebuild). Its weakest lens is Readiness at 36% — the part of that asset most exposed by the findings below.
How we model this: boilerplate at a scaffolding rate + logic × domain Very high (×2.1) — microservices, DDD/clean architecture, CQRS, domain model, event-driven integration × 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
Resolve the 1 No assertions (empty test) finding(s) in Test Quality — start with UnitTest1.cs.
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 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 Readiness first — highest risk-reduction per euro on an asset this size.
Root cause: an un-encapsulated domain · Medium · Root cause
14 findings across public setters, anemic types and primitive ids share one root cause — the domain layer doesn't protect its own invariants. Fixing the encapsulation pattern resolves them together, rather than chasing each finding.
→ Address encapsulation as one pattern (private setters + behaviour + strongly-typed ids), not 100 separate findings.
Highest-leverage move · Medium · Leverage
Of everything flagged, the best return on effort is: Add a CI workflow that builds and runs the test suite on every push/PR. The rest can wait behind it.
Evidence: priority ranking: top of 5 ranked by impact/effort
→ Add a CI workflow that builds and runs the test suite on every push/PR.
Architecture — bounded-context dependency graph
Each box is a bounded context (its layer projects grouped, or a project count when large); arrows show dependencies between contexts. A shared kernel is where many arrows converge.
At a glance — Code Health · 53% · Adequate · gated by X5
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
A06:2021 — Vulnerable & Outdated Components
15
High / Critical
A05:2021 — Security Misconfiguration
4
High / Critical
Roadmap
First, establish a continuous integration pipeline to automatically build and test every change. Next, extend structured logging across all services to ensure full production observability. Then, implement readiness and liveness probes with an automated rollback strategy to catch bad releases. Finally, enforce default-deny access controls on all endpoints and add security response headers to harden the web posture.
Ranked by impact ÷ effort. "Helps" is the estimated gain on the 0–100 health score.
Do this
Helps
Effort
Dimension
Resolve the 1 No assertions (empty test) finding(s) in Test Quality — start with UnitTest1.cs.
Protect endpoints by default-deny: [Authorize] + role/policy authorization, or imperative guard methods (throw-on-violation) called from every handler.
Add security response headers (Content-Security-Policy, X-Frame-Options, X-Content-Type-Options) — defense in depth, even when a reverse proxy could set them.
Watchdog is a deep, periodic assessment — run each sprint, monthly, or quarterly, taking the time to go wider and deeper than a quick check and surfacing in one coherent report what you'd otherwise piece together from a dozen separate tools. It scores deterministically: the same commit yields the same score, every run. 59 of 62 evaluated dimensions are computed purely by tools and static analysis (confidence 1.0); 3 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 — 62 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, 41 of 84 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
A clean run — every tool resolved and ran, and every applicable dimension was measured at full confidence. No scanner was unavailable, no analysis timed out or crashed, and nothing fell back to a degraded estimate.
When something does degrade — a missing scanner, a shallow clone, an LLM hiccup — it is named here explicitly and its exact cause recorded in diagnostics.md, never absorbed silently into the score.
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.
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.
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.
D19 Documentation Quality: Documentation quality is judged by an LLM over a bounded sample of docs — it reads what is written, not whether the docs match the running system, and it is advisory, not a measurement.
D20 ADR Quality: ADR quality is an LLM read of the decision records present — it cannot know about decisions made and never recorded, and its verdict is sampled and advisory.
D21 Naming Consistency: Naming quality is an LLM judgement over a bounded sample — it assesses clarity/consistency of the names it sees, not domain-correctness, and is advisory.
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.
D31 IaC & Container Security: IaC scanning checks Dockerfiles/Terraform/Kubernetes against best-practice rules — it cannot see the live cloud account, runtime configuration, or drift between the committed config and what is actually deployed.
D35 Change Coupling: Change coupling is co-change in COMMITS — files split across separate commits, or coupled only through a shared config/build step, read as uncoupled, and a sweeping commit (rename/format) is excluded so it doesn't couple everything. It shows that files change together, not WHY: a high coupling can be a healthy cohesive pair as readily as a hidden leak.
AX10 Code composition: Role is inferred from namespace/folder convention, not semantics — a domain concept living in a folder named "Services" reads as application, and the split is lines-of-code, not business value. The business-logic-share score is a SOFT, FLOORED signal: it contributes to the Architecture lens but is floored at the Critical gate, so an infrastructure-heavy design (a gateway, an ETL, a driver) is legitimately low without being nuked to zero.
AX9 CQS / query purity: Handlers are found by interface/name convention — a query handler using neither is not seen. Mutation is a resolved write/publish invocation (SaveChanges/repository/bus), so a write hidden behind a hand-rolled wrapper, reflection, or a string-keyed service locator resolves to a non-persistence type and isn't flagged; it detects that a query writes state, not whether the write is a legitimate read-side cache update. Clean means "no resolved write/publish in a query body", not a proof of CQS purity.
DM4 Rich vs anemic model: Behaviour is detected as state mutation inside a method body — a method that enforces an invariant by validating-and-throwing without mutating reads as a query, and mutation delegated through an interface the scan can't resolve isn't credited; entities with zero public properties still drop out of the population. It detects that state changes, not whether the rule is correct.
DM6 Domain ↔ infrastructure boundary: Infrastructure reached through a hand-rolled wrapper, a domain-named facade, reflection, or a string-keyed service locator resolves to a non-infra type and isn't seen; the body scan is symbol resolution over syntax, not full dataflow. A clean result means "no resolved infra reference in a domain body", not a proof of purity.
ED5 Idempotency: Idempotency is judged from the handler body's visible writes and guards — a guard enforced by a database unique constraint, a broker's exactly-once delivery, or a domain method whose no-op-when-applied logic the scan can't follow may read as at-risk; the at-risk candidates are confirmed by a SAMPLED LLM verdict (advisory, not exhaustive) and degrade to heuristic-only when no model is configured. It flags the at-least-once double-apply SHAPE, not a runtime proof of a duplicate effect.
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 (6): D19, D20, D21, D24, ED5, 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.
0 method(s) exceeded the cognitive complexity threshold of 15.
✓ On the Gold path — maintain.
Detailed fixes: d2_recommendation.md.
Do you agree with this assessment?
D3 · God Classes10.0 / 10Exemplary✓ Tool-verified
What it measures: Over-large classes that try to do too much ("god classes").
Method: God-class detection by line and method-count thresholds per logical type (partial classes unified), filtered for generated code and registration/contract false positives. Deterministic.
What it measures: Copy-pasted code that should be shared instead.
Method: Code duplication via token-stream sliding windows with type-aware normalization (locals masked, type names preserved), density-scored per KLoC of production code. Deterministic.
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.
Resolve the 9 Off the main sequence finding(s) in Coupling. — One of this dimension's main actionable groups (9 warning-level).
Stand up a CI pipeline, then gate Coupling in it to reach Verified (currently Documented). — This repository has no CI pipeline, so there is nothing to add a gate to yet — the pipeline comes first. Hardens enforcement from Documented toward Prevented — provenance only; does not change the score.
Detailed fixes: d5_recommendation.md · top locations in Appendix A, every location in findings.md.
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.
8 test methods: 8 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 Quality4.4 / 10Weak✓ Tool-verified
What it measures: Whether the tests truly assert behaviour rather than just running the code.
Method: Per-test assertions, skips, and mock references analyzed via Roslyn; structured skip-reason tags (BUG:/ENV:) separate documented deferrals from debt. Deterministic.
0 skipped, 2 zero-assertion, 1 mock references across 8 tests.
No assertions (empty test): Test1Src/CommonAll/Common.Core.Tests/UnitTest1.cs:8
No assertions: ShouldPublishMessageWhenValidQueueProvidedSrc/CommonAll/Common.Infrastructure.Tests/RabbitMqServiceBusTest.cs:40
Mock framework: Moq
What to do
Resolve the 1 No assertions (empty test) finding(s) in Test Quality — start with UnitTest1.cs. — One of this dimension's main actionable groups (1 issue-level).
Resolve the 1 No assertions finding(s) in Test Quality — start with RabbitMqServiceBusTest.cs. — One of this dimension's main actionable groups (1 warning-level).
Stand up a CI pipeline, then gate Test Quality in it to reach Verified (currently Documented). — This repository has no CI pipeline, so there is nothing to add a gate to yet — the pipeline comes first. Hardens enforcement from Documented toward Prevented — provenance only; does not change the score.
Detailed fixes: d10_recommendation.md · top locations in Appendix A, every location in findings.md.
What it measures: Whether dependencies are current, secure, and not bloated.
Method: Manifest scan via dotnet list package across all projects; worst-signal-per-package deduction (saturating for vulnerabilities, capped-linear for deprecation/outdated) per KLoC. Exhaustive, deterministic.
What it measures: Whether any secrets (keys, tokens, passwords) have leaked into the code.
Method: In-process native secret scanner (entropy plus signature patterns) across all tracked files; no external tool. A clean result is a measured 10, not no-data zero. Deterministic.
What it measures: Whether the licenses of third-party packages are compatible with your policy.
Method: Third-party package licenses resolved from declared package metadata and checked against the configured policy (allow/deny/copyleft). Deterministic; clean = no incompatible license found at metadata depth.
What it measures: Files that change often and are also complex — the riskiest hotspots.
Method: Per production file churn times cyclomatic complexity over a rolling window, computed from git and Roslyn/JS/Razor analysis. Exhaustive, deterministic per commit date.
What it measures: Acknowledged debt left in the code — TODOs, dead code, suppressed warnings.
Method: Roslyn syntactic debt markers (suppressions/TODO/FIXME/HACK/empty-catch/commented-code/Obsolete) plus SymbolFinder dead-code analysis; weighted-debt-per-KLoC density deducted 2.0x per unit. Deterministic, exhaustive.
0 deducted debt markers + 0 dead symbols across 2358 LoC (0.0/KLoC) → score 10.0.
✓ On the Gold path — maintain.
Detailed fixes: d17_recommendation.md.
Do you agree with this assessment?
D18 · Solution Shape7.2 / 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 3 Shell project finding(s) in Solution Shape — start with Inventory.Command.csproj, Inventory.Query.csproj, InventoryQueryHandler.csproj. — One of this dimension's main actionable groups (3 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 the project's documentation is clear, complete, and useful.
Method: Judged by language model at low temperature (0.0-0.1) on a deterministic doc sample (READMEs plus first 25 architecture docs), with two-pass stability filtering. Advisory, sampled.
The README is a thin single-file description of an event-driven microservices architecture that shows the domain (Purchase/Sales/Inventory), a three-microservice structure with CQRS and nothing-shared architecture, and high-level design. It also begins outlining a Command/Query Segregation section before being clipped mid-sentence ('So command execution flows are risky by nature...'). The visible content is thin but consistent with an outline; the absence of XML docs for Purchase.Command, Purchase.Query, Inventory.EventHandler, and Sales.QueryHandler makes this a documentation gap rather than a structural defect. It reads like a conceptual sketch rather than a complete reference.
Improve Documentation Quality — currently 4.0/10. — The README is a thin single-file description of an event-driven microservices architecture that shows the domain (Purchase/Sales/Inventory), a three-microservice structure with CQRS and nothing-shared architecture, and high-level design. It also begins outlining a Command/Query Segregation section before being clipped mid-sentence ('So command execution flows are risky by nature...'). The visible content is thin but consistent with an outline; the absence of XML docs for Purchase.Command, Purchase.Query, Inventory.EventHandler, and Sales.QueryHandler makes this a documentation gap rather than a structural defect. It reads like a conceptual sketch rather than a complete reference.
Detailed fixes: d19_recommendation.md · top locations in Appendix A, every location in findings.md.
What it measures: Whether architecture decisions are recorded well (context, decision, consequences).
Method: Per-ADR judgment by language model at low temperature with two-pass stability; confidence is share of ADRs evaluated; enforcement-field presence detected deterministically. Advisory.
What it measures: Whether names — types, methods, variables — are clear and consistent.
Method: Judged by language model at low temperature (0.0-0.1) on a deterministic random symbol sample (fixed size, not exhaustive), with disclosed confidence band. Advisory, sampled.
10 naming inconsistencies across 200 sampled symbols.
Typo in method name: 'Decerialize' should be 'Deserialize' to match standard C# naming conventions and the corresponding 'Serialize' method.
Inconsistent naming for event/line item types: 'PurchasedLineItem' vs 'ProductSoldLineItem' vs 'PurchasedLineItem'. The prefix 'Product' is missing in some, and 'Sold' vs 'Purchased' is inconsistent across similar concepts.
Inconsistent naming for domain models: 'ProductLineItem' vs 'SalesLineItem'. One uses 'Product' and the other 'Sales' for what appears to be a line item in a transaction.
Inconsistent naming for unit-related properties: 'PurchaseUnitName' vs 'UnitName'. The prefix 'Purchase' is used in one place but not others.
Inconsistent naming for user/owner properties: 'Name' vs 'Manager'. 'Name' is generic, 'Manager' is specific. If 'Manager' refers to a person, 'Owner' or 'Manager' should be consistent.
+ 5 more group(s) — more in Appendix A; the complete list is findings.md.
What to do
Resolve the 1 Typo in method name finding(s) in Naming Consistency. — One of this dimension's main actionable groups (1 recommendation-level).
Resolve the 1 Inconsistent naming for event/line item types finding(s) in Naming Consistency. — One of this dimension's main actionable groups (1 recommendation-level).
Resolve the 1 Inconsistent naming for domain models 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 / 10Adequate◐ 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 PurchaseCommandHandler.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.
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.
88 % of calls cross a namespace and 14 % go through an interface, but 100 % of collaborators are co-located — so a call's collaborators sit together and tracing stays easy. Baseline: small — navigation cost is tolerated.
What it measures: Whether any secrets were ever committed — scanned across the full git history, not just now.
Method: Git-history secret scan via gitleaks detect over full history in an isolated checkout; each match flagged High. Exhaustive; degrades cleanly when tool absent.
What it measures: Real static-analysis (SAST) findings — likely security bugs in the code, any language.
Method: Polyglot static analysis via semgrep 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).
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.
What it measures: Whether files that change together actually belong together — pairs that repeatedly co-change in git history despite having no explicit code dependency, surfacing the hidden/logical coupling (and boundaries in the wrong place) a static scan can't see.
Method: Pairwise co-occurrence over the per-commit file sets in git history (production source only — tests and generated dropped): Degree-of-Coupling = shared ÷ min individual revisions, reported above noise floors (each file ≥10 revisions, ≥5 shared commits, ≥50% strength); sweeping commits excluded. Deterministic over fixed history.
Coverage: Population: PRODUCTION source files only — test and generated files are dropped before pairing, so a class co-changing with its own test (trivially ~100%) can't drown the real production↔production coupling. Pairs ranked by Degree-of-Coupling; coupling through a build step, config, or non-source file isn't seen.
Other · Architecture — Whether any singleton service captures a scoped/transient dependency — a silent lifetime/threading bug.
Method: Roslyn scan: DI registrations parsed from AddSingleton/Scoped/Transient; each singleton checked for captured shorter-lifetime dependencies. Exhaustive, deterministic.
Other · Architecture — How the codebase splits by code ROLE — domain, application, infrastructure, test, generated. The significance map behind the knowledge/coupling weighting, and a DDD signal in its own right: a thin domain core under fat infrastructure is the anemic-domain smell, quantified.
Method: Roslyn line-count by code ROLE: every source file classified Domain/Application/Infrastructure/Test/Generated by namespace + path convention (the shared CodeRoleClassifier), then significant lines summed per role. Deterministic; the advisory score is the business-logic (domain+application) share of production code.
Coverage: Population: ALL source files, each bucketed into ONE of five roles (Domain/Application/Infrastructure/Test/Generated) by namespace + path convention — a file whose layer isn't named in the convention falls to Application (the neutral default), and the split is line-count, not semantic depth or business value.
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 singleton services avoid mutable shared instance state that concurrent callers would race on.
Method: Roslyn scan: singleton field mutations unguarded by lock or Interlocked, per type; syntax-based guard detection. Deterministic, traceable per field.
Other · Architecture — Whether the project-reference graph is acyclic (cycles block independent build/deploy and signal eroding boundaries).
Method: Project reference cycles via elementary-DFS over real .csproj references, using the engine shared with D5/D7; cyclic versus acyclic. Exhaustive, deterministic.
Other · Architecture — Whether dependencies point inward (Domain ← Application ← Infrastructure/Web) — the clean-architecture dependency rule, checked across the project graph.
Method: Layer violations by name-segment inference (Domain/Core to Application to Infrastructure/Web) over the project-reference graph. Exhaustive over all projects, deterministic.
Other · Architecture — Whether the codebase has a recognisable, scale-appropriate structure (a named architectural style, or modular enough for its size) rather than being an ad-hoc ball of mud.
Method: Roslyn plus csproj analysis: architecture style detection (DDD, clean, vertical-slice, CQRS) and structure fitness for repo size. Deterministic.
Other · Architecture — Whether interfaces stay focused rather than fat — the Interface-Segregation principle (SOLID 'I').
Method: Roslyn scan: public interface member counts; fat-interface threshold (over 15 members) flagged per type. Deterministic, type-level.
Do you agree with this assessment?
AX8 · Test isolation10.0 / 10Exemplary✓ Tool-verified
Other · Architecture — Whether production projects stay free of references to test projects — tests may depend on production, never the reverse.
Method: Csproj graph: each production project checked for references to test projects (identified by test-framework presence, not name). Zero violations is clean. Deterministic.
Other · Architecture — Whether read (query) handlers stay side-effect-free — a query that writes persistent state or raises events breaks CQS and makes reads unsafe to retry, cache, or route to a read replica.
Method: Roslyn scan: CQRS handlers classified query-vs-command by interface (IQueryHandler/ICommandHandler/IRequestHandler<TQuery,TResult>) and name convention (*Query/Get*/Find* vs *Command); each query handler's body checked for persistent-state writes (SaveChanges/repository Add-Update) or event publishes by resolved invocation. Deterministic, type-level, exhaustive over the detected handlers.
Coverage: Population: CQRS handlers identified by IQueryHandler/ICommandHandler/IRequestHandler interface + *Query/Get*/Find*/*Command NAME convention; query purity then checked exhaustively within that set — a query handler using neither convention is invisible, and mutation is a resolved persistence/publish CALL, not full dataflow.
Other · Security — Whether access is authorized by default — a framework authorization attribute/decorator or policy, or imperative guard methods (throw-on-violation) called from handlers.
Method: Roslyn scan: [Authorize] usage and authorization policies, plus imperative throw-on-violation guard methods detected via syntax. Deterministic.
No [Authorize]/policies and no imperative guard methods (throw-on-violation) were found — endpoints may be unprotected.
What to do
Protect endpoints by default-deny: [Authorize] + role/policy authorization, or imperative guard methods (throw-on-violation) called from every handler.
Other · Domain Modelling — Whether aggregates reference each other by identity (id) rather than by direct object reference — the core DDD consistency-boundary rule.
Method: Roslyn (DDD-gated): aggregate roots identified by convention; each aggregate field checked for direct references to other aggregates versus id-only. Deterministic, DDD-native.
Coverage: Population: aggregate roots identified by AggregateRoot/IAggregateRoot base/interface NAME convention; reference-by-identity then checked exhaustively within that set — a root not using those names is invisible.
`Purchase` references the aggregate root `User` directly (via `User`) — hold its `UserId` instead. — Purchase.cs:14
`StoreItem` references the aggregate root `Store` directly (via `Store`) — hold its `StoreId` instead. — StoreItem.cs:10
What to do
Reference other aggregates by their strongly-typed id, never by object reference, so each aggregate stays an independent consistency boundary.
Do you agree with this assessment?
DM4 · Rich vs anemic model3.0 / 10Weak✓ Tool-verified
Other · Domain Modelling — Whether aggregates/entities carry the behaviour that protects their invariants, rather than being data bags driven by external services.
Method: Roslyn (DDD-gated): entity method BODIES classified mutator-vs-query — only methods that mutate the entity's own declared state count as invariant-protecting behaviour, so a getter/passthrough doesn't rescue an anemic class. Deterministic, exhaustive over domain-layer entities.
Coverage: Population: entities by name/base convention; rich-vs-anemic judged by classifying each method body mutator-vs-query — logic-bearing domain types outside the convention are invisible.
`Product` is an aggregate/entity with 1 data propert(ies) but no state-changing behaviour (only data and queries) — the business logic lives in a service. — Product.cs:9
`ProductLineItem` is an aggregate/entity with 4 data propert(ies) but no state-changing behaviour (only data and queries) — the business logic lives in a service. — ProductLineItem.cs:11
`Purchase` is an aggregate/entity with 3 data propert(ies) but no state-changing behaviour (only data and queries) — the business logic lives in a service. — Purchase.cs:10
`User` is an aggregate/entity with 1 data propert(ies) but no state-changing behaviour (only data and queries) — the business logic lives in a service. — User.cs:5
`Store` is an aggregate/entity with 1 data propert(ies) but no state-changing behaviour (only data and queries) — the business logic lives in a service. — Store.cs:8
`StoreItem` is an aggregate/entity with 3 data propert(ies) but no state-changing behaviour (only data and queries) — the business logic lives in a service. — StoreItem.cs:6
`Sales` is an aggregate/entity with 2 data propert(ies) but no state-changing behaviour (only data and queries) — the business logic lives in a service. — Sales.cs:8
What to do
Move business rules onto the aggregates/entities they govern so invariants are enforced at the source, not in anemic services.
Other · Domain Modelling — Whether entities protect their state (private/init-only setters) instead of exposing public setters that bypass invariants. Softened when a rehydration framework (Marten/EF) is present.
Method: Roslyn (DDD-gated): public setters on entities detected; score softened when Marten/EF rehydration frameworks present. Deterministic, framework-aware.
Coverage: Population: entities by convention; encapsulation (setter shape) checked exhaustively within the set.
`Product` exposes publicly writable state (ProductName). — Product.cs:9
`ProductLineItem` exposes publicly writable state (Product, PurchaseUnitPrice, PurchaseQuantity). — ProductLineItem.cs:11
`Purchase` exposes publicly writable state (PurchaseDate, User, LineItems). — Purchase.cs:10
`User` exposes publicly writable state (Name). — User.cs:5
`Store` exposes publicly writable state (Manager). — Store.cs:8
`StoreItem` exposes publicly writable state (ItemName, Store, BalanceQuantity). — StoreItem.cs:6
`Sales` exposes publicly writable state (SalesDate, SalesLineItems). — Sales.cs:8
What to do
Make entity setters private/init-only; change state only through methods that enforce the invariants (Marten/EF can bind via constructor or private setters).
Other · Domain Modelling — Whether the domain layer stays free of infrastructure dependencies (EF/Marten/HTTP/ASP.NET) — the clean-architecture dependency rule.
Method: Roslyn (DDD-gated): domain-layer types scanned for infrastructure usage in member SIGNATURES and inside method/accessor BODIES — resolved calls and object-creations into EF/Marten/HTTP/Mongo/Redis/message-bus types (not just a namespace allowlist). Deterministic, symbol-resolved, exhaustive over domain-layer bodies, DDD-native.
Coverage: Domain layer identified by NAMESPACE heuristic; infrastructure then resolved by symbol in member SIGNATURES and method/accessor BODIES — rename the layer and the check evaporates.
Other · Domain Modelling — Whether clusters of primitives that travel together (a missing value object) are extracted — a low-weight suggestion, LLM-confirmed when configured.
Method: Roslyn (DDD-gated): primitive parameter clusters recurring three or more times across signatures extracted, then confirmed by language model when configured. Advisory, low-weight.
Other · Event-Driven — Whether event handlers stay asynchronous (no blocking remote HTTP/gRPC calls awaited inside a handler).
Method: Roslyn semantic scan (event-driven gated): event-handler bodies scanned for HTTP/gRPC invocations by resolved symbol type, not substring. Deterministic, semantic-resolved.
Other · Event-Driven — Whether commands have a single handler (one owner of the decision) and fan-out is modelled with events.
Method: Roslyn scan (event-driven gated): command-shaped messages identified by convention; handler count per command checked for the exactly-one rule. Deterministic, hard fact.
Other · Event-Driven — Whether state changes and message publishes are atomic (a transactional outbox) rather than a crash-unsafe dual write.
Method: Roslyn semantic scan (event-driven gated): event-handler methods scanned for DB-save plus bus-publish without a transactional outbox reference. Deterministic, semantic-resolved.
`PurchaseCommandHandler.IsStoreServiceOn` writes to the database while `PurchaseCommandHandler.Handle` publishes to the message bus in the same command-handling flow, with no outbox referenced on either path. Splitting the persist and the publish across sibling methods (or two collaborating actors) doesn't make them atomic — a crash between the two either loses the message or emits a phantom event. Use the transactional outbox pattern so the message is committed in the same transaction as the state change and dispatched afterwards. — PurchaseCommandHandler.cs:161
What to do
Adopt the transactional outbox pattern so DB writes and message publishes commit atomically — no lost or phantom events on a crash.
Other · Readiness — Whether retry-prone mutations (command handlers + message/event consumers) are idempotent so an at-least-once redelivery or client retry doesn't double-apply the effect — heuristic at-risk detection confirmed by language model, advisory.
Method: Roslyn heuristic (any mutation, ungated): command handlers and message/event consumers that mutate persistent state without a visible idempotency guard (exists/dedup check, upsert, idempotency-key/inbox, conditional/versioned write, fixed-value set) flagged as at-risk; each at-risk candidate then confirmed or cleared by a language model as genuinely non-idempotent versus naturally-idempotent. Advisory without a model (heuristic-only, degraded), per-candidate judged with one.
Coverage: Population: retry-prone mutations — command handlers (CQRS write side) + message/event consumers (IConsumer/I*EventHandler) — that mutate persistent state; runs on any repo with mutations, not only event-driven ones. The at-risk subset (no obvious guard) is a HEURISTIC candidate set, each then LLM-JUDGED non-idempotent vs safe; a handler outside those conventions, or a guard the LLM can't confirm, is bounded by the sample. Degrades to heuristic-only when no model is configured.
Other · Code Health — Unreviewed-generation residue: shipped members still throwing NotImplementedException, and placeholder string literals left in non-test, non-generated code. Scored as a quality signature, never as a claim about authorship.
Method: Roslyn syntax scan: NotImplementedException throws and placeholder string literals in non-test, non-generated shipped code. Deterministic, code-shape signature.
Other · Code Health — Unfinished work detected by code SHAPE, not keywords: members that only throw a "not implemented" exception, methods that take inputs and return a constant, async methods that never await, dead `if (false)` / `#if false` branches, and skeleton types most of whose members are holes. A real, objective slice of technical debt.
`RegisterEventHanders` takes parameters but its body is empty — it accepts inputs and does nothing. Either implement it or remove it. — Startup.cs:77
`RegisterEventHandlers` takes parameters but its body is empty — it accepts inputs and does nothing. Either implement it or remove it. — Startup.cs:89
A line of code has been commented out rather than removed — dead weight that rots and confuses. Delete it (version control remembers). (×3) — PurchaseCommandHandler.cs:48, PurchaseCommandHandler.cs:52, PurchaseCommandHandler.cs:54
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 build/run (quick start) section to the root README — the first thing a newcomer needs.
Add a 'Testing' section to the root README — how to run the test suite.
Add a README to the 36 of 36 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.
Tests aren't grouped in a dedicated test folder — the test surface isn't separable from production code at a glance.
Only 12/36 projects share a common root namespace — the code's module identity is inconsistent.
What to do
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.
Do you agree with this assessment?
P1 · CI/CD gates0.0 / 10Critical✓ Tool-verified
Readiness · Readiness — Whether an automated pipeline builds and tests every change.
Method: Filesystem scan: CI workflow files (.github/workflows, .gitlab-ci.yml, etc.) for build and test stages. Exhaustive, deterministic.
No CI workflow found (.github/workflows, azure-pipelines.yml, .gitlab-ci.yml, …) — changes aren't gated by an automated build/test.
What to do
Add a CI workflow that builds and runs the test suite on every push/PR.
Do you agree with this assessment?
P2 · Observability4.4 / 10Weak✓ 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 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 automated and safely reversible (probes, rolling updates, approval gates) — from manifests/pipeline files, not the live environment.
Method: Filesystem scan: deployment manifests/IaC (K8s YAML, Helm, Terraform) for rolling updates, probes, approval gates, migration hooks. Exhaustive, deterministic.
Deployment automation exists but no readiness/liveness probes, rolling-update strategy, lifecycle hooks or migration job were evidenced — a bad release is harder to detect and reverse.
What to do
Add readiness/liveness probes and a rolling-update (or blue/green) strategy so a bad release is caught and rolled back automatically.
Add an approval/environment gate (required reviewers / protection rules) before production promotion.
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 UseHttpsRedirection/UseHsts and no reverse-proxy signal — transport security is unverified at the app layer. (−2.0 on this card.)
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.
Enforce HTTPS at the app layer (UseHttpsRedirection / UseHsts) — only skip this if a reverse proxy demonstrably terminates TLS.
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. — Startup.cs:57
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 0/3 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. (×3) — Startup.cs:74, PurchaseCommandHandler.cs:161, PurchaseCommandHandler.cs:188
What to do
Thread a CancellationToken through async methods so work stops promptly on cancellation.
Other · Code Health — Whether exceptions are handled rather than silently swallowed or rethrown with lost stack traces.
Method: Roslyn syntax scan: every catch clause counted; empty catches and bare rethrows flagged. Population is all catch clauses, not estimated. Deterministic, hard fact.
Other · Code Health — Whether log calls use message templates (queryable) rather than interpolated strings.
Method: Roslyn syntax scan: every log call-site counted; interpolated-string first-argument violations flagged. Population is all log calls, not estimated. Deterministic.
Other · Code Health — Whether nullable reference types are enabled and not undermined by heavy `!` suppression.
Method: Roslyn compiler-options scan: NullableContextOptions per project; null-forgiving (!) suppression density per 1k syntax nodes. Deterministic, adoption plus suppression penalty.
0/31 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.
Not included — 45 check(s) not relevant to this codebase
These checks had nothing to measure here (no tests, no git history, the codebase is small, or the architecture style doesn't apply), so they're omitted above rather than scored low.
AC1 Text alternatives — No web markup found — accessibility is not applicable to this repository.
AC2 Forms & labels — No web markup found — accessibility is not applicable to this repository.
AC3 Page structure — No web markup found — accessibility is not applicable to this repository.
AC4 Keyboard semantics — No web markup found — accessibility is not applicable to this repository.
AC5 ARIA correctness — No web markup found — accessibility is not applicable to this repository.
AC6 Visual & motion safety — No web markup found — accessibility is not applicable to this repository.
AC7 A11y enforcement — No web markup found — accessibility is not applicable to this repository.
AX7 Slice cohesion — not applicable — not a vertical-slice architecture
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.
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 — Test runner surfaced no tests
D16 Bus Factor — early-stage repository — too few commits for a meaningful bus factor
D22 Internal API Consistency — No exposed public API
D23 Boundary Type-Coupling — Bounded contexts not declared
D25 ADR Conformance — no ADRs to check
D32 Data Compliance (PII/GDPR) — No PII/GDPR ruleset is bundled (the public p/gdpr semgrep pack was retired) — data compliance is not assessed in this scan.
D33 JS/npm Dependency Vulnerabilities — No JS/npm manifest or lockfile found outside build output (package.json, package-lock.json, yarn.lock, pnpm-lock.yaml, bun.lockb); no JS dependencies to scan.
D34 Knowledge Freshness — early-stage repository — too little history to judge knowledge freshness
D36 Supply-chain Provenance & Signing — No CI/build pipeline found (.github/.forgejo/.gitea workflows, .gitlab-ci.yml, azure-pipelines.yml, Jenkinsfile, .circleci); there is no build to attest provenance for.
D37 Vulnerability-disclosure Policy — No vulnerability-disclosure policy file found (SECURITY.md/.markdown/.rst/.txt at root or under .github/.forgejo/.gitea/docs, .well-known/security.txt). A coordinated-disclosure policy may live off-repo, so this is not evidenced rather than failed.
D38 OSV Dependency Vulnerabilities — No supported non-.NET dependency lockfile found outside build output (npm package-lock/yarn/pnpm/bun, Go go.mod, Rust Cargo.lock, Maven pom.xml, Gradle lockfiles, Python requirements.txt/poetry.lock/Pipfile.lock/pdm.lock, PHP composer.lock, Ruby Gemfile.lock, Elixir mix.lock, Dart pubspec.lock, Swift Package.resolved); nothing for OSV to scan. A NuGet-only repo stays NotApplicable — .NET CVEs are D30's domain.
D39 IL Efficiency — The target did not build, so no IL was available to measure.
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.
D7 Architectural Integrity — no checkable ADRs and no dependency cycles — architectural integrity not assessed
D8 Code Coverage — Coverage not measured — analyzer environment
DM2 Strongly-typed ids — no id-bearing domain types detected — strongly-typed-id adoption not assessable
DM3 Integration-event coupling — no integration events detected — coupling check not applicable
ED3 Event naming — no domain or integration events detected — event-naming check not applicable
ES1 Event Sourcing — not run — 0/3 markers found
P12 CI test-gate honesty — no CI workflow found
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 — produce a coverage report in a standard format (Cobertura — `dotnet test --collect:"XPlat Code Coverage"` with a `coverlet.collector` PackageReference) into the repo working tree before the scan — a CI step is the usual place, since the artefact is commonly gitignored, or wire coverage collection into CI, to enable this cross-layer check
PF1 Benchmark discipline — Performance 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 CVE: MongoDB.Driver 2.11.0 — MongoDB.Driver 2.11.0 (transitive) has a High advisory; affects 29 projects — one upgrade fixes all. https://github.com/advisories/[GHSA redacted] — upgrade to 2.19.0
High CVE: Newtonsoft.Json 11.0.2 — Newtonsoft.Json 11.0.2 (transitive) has a High advisory; affects 4 projects — one upgrade fixes all. https://github.com/advisories/[GHSA redacted] — upgrade to 13.0.1
High CVE: System.Net.Http 4.3.0 — System.Net.Http 4.3.0 (transitive) has a High advisory; affects 29 projects — one upgrade fixes all. https://github.com/advisories/[GHSA redacted] — upgrade to 4.3.4
High CVE: System.Text.RegularExpressions 4.3.0 — System.Text.RegularExpressions 4.3.0 (transitive) has a High advisory; affects 29 projects — one upgrade fixes all. https://github.com/advisories/[GHSA redacted] — upgrade to 4.3.1
High CVE: Newtonsoft.Json 10.0.1 — Newtonsoft.Json 10.0.1 (transitive) has a High advisory. https://github.com/advisories/[GHSA redacted] — upgrade to 13.0.1
High CVE: Newtonsoft.Json 9.0.1 — Newtonsoft.Json 9.0.1 (transitive) has a High advisory; affects 4 projects — one upgrade fixes all. https://github.com/advisories/[GHSA redacted] — upgrade to 13.0.1
High CVE: System.Net.Http 4.1.0 — System.Net.Http 4.1.0 (transitive) has a High advisory. https://github.com/advisories/[GHSA redacted] — upgrade to 4.3.4
High CVE: System.Security.Cryptography.X509Certificates 4.1.0 — System.Security.Cryptography.X509Certificates 4.1.0 (transitive) has a High advisory. https://github.com/advisories/[GHSA redacted]
High IaC: DS-0002 Src/InventoryMicroservice/Inventory.Api.Grpc/Dockerfile— Image user should not be 'root'
High IaC: DS-0002 Src/SalesMicroservice/SalesWebApi/Dockerfile— Image user should not be 'root'
D10 · Test Quality· No assertions (empty test) · ×1
No assertions (empty test): Test1 Src/CommonAll/Common.Core.Tests/UnitTest1.cs:8— Test method has an empty body — it asserts nothing and exercises no code.
Off the main sequence: Purchase.Core — Purchase.Core: 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: Purchase.DTO — Purchase.DTO: 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: Inventory.DTO — Inventory.DTO: 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: Sales.Core — Sales.Core: 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: Sales.DTO — Sales.DTO: 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: Common.Infrastructure — Common.Infrastructure: abstractness 0.00, instability 0.07, distance 0.93 — zone of pain — concrete and heavily depended-on, so it's rigid to change.
Off the main sequence: Inventory.Domain — Inventory.Domain: abstractness 0.00, instability 0.08, distance 0.92 — zone of pain — concrete and heavily depended-on, so it's rigid to change.
Off the main sequence: Sales.Domain — Sales.Domain: abstractness 0.00, instability 0.20, distance 0.80 — zone of pain — concrete and heavily depended-on, so it's rigid to change.
Off the main sequence: Purchase.Command — Purchase.Command: abstractness 0.00, instability 0.29, distance 0.71 — zone of pain — concrete and heavily depended-on, so it's rigid to change.
Medium CVE: MailKit 2.6.0 — MailKit 2.6.0 (transitive) has a Medium advisory; affects 17 projects — one upgrade fixes all. https://github.com/advisories/[GHSA redacted] — upgrade to 4.16.0
Medium CVE: MimeKit 2.6.0 — MimeKit 2.6.0 (transitive) has a Medium advisory; affects 17 projects — one upgrade fixes all. https://github.com/advisories/[GHSA redacted] — upgrade to 4.15.1
Medium CVE: MongoDB.Driver 2.11.0 — MongoDB.Driver 2.11.0 (transitive) has a Medium advisory; affects 29 projects — one upgrade fixes all. https://github.com/advisories/[GHSA redacted]
Medium CVE: SharpCompress 0.23.0 — SharpCompress 0.23.0 (transitive) has a Medium advisory; affects 29 projects — one upgrade fixes all. https://github.com/advisories/[GHSA redacted] — upgrade to 0.29
Medium CVE: SharpCompress 0.23.0 — SharpCompress 0.23.0 (transitive) has a Medium advisory; affects 29 projects — one upgrade fixes all. https://github.com/advisories/[GHSA redacted] — upgrade to 0.48.0
No assertions: ShouldPublishMessageWhenValidQueueProvided Src/CommonAll/Common.Infrastructure.Tests/RabbitMqServiceBusTest.cs:40— Test method exercises code but verifies nothing — add an assertion.
Coverage not measured — analyzer environment — Coverage NOT MEASURED: the analyzer environment could not build/run the test suite (a target framework / SDK band or targeting pack the analyzer image doesn't carry). This is OUR limitation, not a defect in the repo — coverage is excluded from the score rather than counted as a near-zero. We track the analyzer-image gap so it can be closed; in the meantime, commit the Cobertura/OpenCover/lcov report your CI already produces and real coverage will be read.
Shell project: Inventory.Command Src/InventoryMicroservice/InventoryCommand/Inventory.Command.csproj— `Inventory.Command` contributes only 9 significant line(s) — an empty/placeholder project is structural noise. Remove it or fold its contents into a real project.
Shell project: Inventory.Query Src/InventoryMicroservice/InventoryQuery/Inventory.Query.csproj— `Inventory.Query` contributes only 8 significant line(s) — an empty/placeholder project is structural noise. Remove it or fold its contents into a real project.
Shell project: InventoryQueryHandler Src/InventoryMicroservice/InventoryQueryHandler/InventoryQueryHandler.csproj— `InventoryQueryHandler` contributes only 7 significant line(s) — an empty/placeholder project is structural noise. Remove it or fold its contents into a real project.
Low IaC: DS-0026 Src/InventoryMicroservice/Inventory.Api.Grpc/Dockerfile— No HEALTHCHECK defined
Low IaC: DS-0026 Src/SalesMicroservice/SalesWebApi/Dockerfile— No HEALTHCHECK defined
D11 · Test Reliability· Test runner surfaced no tests · ×1
Test runner surfaced no tests — Test reliability not scored — the test tier(s) ran but surfaced none of this repository's 8 test method(s) to the runner, so flakiness couldn't be exercised. This is an analysis-environment limitation, not a finding about the tests.
early-stage repository — too few commits for a meaningful bus factor — early-stage repository — too few commits for a meaningful bus factor (2 author(s) across 33 commit(s) sampled).
Thin analysable surface across projects — 16 project(s) carry only a thin slice of real code (e.g. `Sales.Core` with 11 significant line(s)). The mean analysable-surface weight is 64 %, lowering Solution Shape by about 2.9 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· Typo in method name · ×1
Typo in method name: 'Decerialize' should be 'Deserialize' to match standard C# naming conventions and the corresponding 'Serialize' method. — Rename 'Decerialize' to 'Deserialize' (symbols: Common.Core.JsonSerializer.Decerialize<T>(string), Common.Core.JsonSerializer)
Inconsistent naming for event/line item types: 'PurchasedLineItem' vs 'ProductSoldLineItem' vs 'PurchasedLineItem'. The prefix 'Product' is missing in some, and 'Sold' vs 'Purchased' is inconsistent across similar concepts. — Standardize to 'ProductPurchasedLineItem' and 'ProductSoldLineItem' or consistent 'ProductPurchasedEvent'/'ProductSoldEvent' patterns. (symbols: Common.Core.Events.PurchasedLineItem, Common.Core.Events.ProductPurchasedEvent, Common.Core.Events.ProductSoldEvent, Common.Core.Events.ProductSoldLineItem)
Inconsistent naming for domain models: 'ProductLineItem' vs 'SalesLineItem'. One uses 'Product' and the other 'Sales' for what appears to be a line item in a transaction. — Use 'LineItem' consistently, e.g., 'PurchaseLineItem' and 'SalesLineItem', or 'ProductLineItem' and 'SalesLineItem' if they represent different entities. (symbols: Purchase.Domain.Model.ProductLineItem, Purchase.Domain.Model.Purchase, Sales.Domain.SalesLineItem)
Inconsistent naming for unit-related properties: 'PurchaseUnitName' vs 'UnitName'. The prefix 'Purchase' is used in one place but not others. — Standardize to 'UnitName' or 'PurchaseUnitName' consistently across all contexts. (symbols: Purchase.Command.LineItemCommand.PurchaseUnitName, Sales.Command.SalesProduct.UnitName, Sales.Domain.SalesLineItem.UnitName, Sales.Command.SalesProduct.UnitName, Purchase.Command.LineItemCommand.PurchaseUnitName)
Inconsistent naming for user/owner properties: 'Name' vs 'Manager'. 'Name' is generic, 'Manager' is specific. If 'Manager' refers to a person, 'Owner' or 'Manager' should be consistent. — Use 'Owner' or 'Manager' consistently if they refer to the same concept of a person associated with the entity. (symbols: Purchase.Domain.Model.User.Name, Inventory.Domain.Store.Manager)
Inconsistent naming for email-related classes: 'EmailSettings' vs 'EmailParams'. One implies configuration, the other data transfer. — Rename 'EmailParams' to 'EmailData' or 'EmailSettings' to 'EmailConfiguration' to clarify the distinction. (symbols: Common.Core.EmailSettings, Common.Core.EmailParams)
Inconsistent naming for constants classes: 'MessageQueues' (plural) vs 'MessageQueue' (singular). — Use 'MessageQueue' or 'MessageQueues' consistently across all namespaces. (symbols: Purchase.Core.Constants.MessageQueues, Inventory.Api.Constants.MessageQueue)
Inconsistent naming for search/query methods: 'SearchPurchases' vs 'FindSoldProductInfo'. One uses 'Search' and the other 'Find', and the parameters differ significantly. — Standardize to 'Search' or 'Find' for all repository query methods. (symbols: Purchase.Repository.PurchaseRepository.SearchPurchases, Sales.Repository.SalesRepository.FindSoldProductInfo)
D21 · Naming Consistency· Inconsistent naming for bus interfaces and implementations · ×1
Inconsistent naming for bus interfaces and implementations: 'CommandBus' vs 'QueryBus'. While distinct, the prefix 'Common.Core' vs 'Common.Infrastructure' is consistent, but the term 'Bus' is used. However, 'ICommandBus' and 'IQueryBus' are consistent. The issue is the implementation classes 'CommandBus' and 'QueryBus' vs 'EventBus'. — Ensure all bus implementations follow the same naming pattern, e.g., 'CommandBus', 'QueryBus', 'EventBus'. (symbols: Common.Core.ICommandBus, Common.Core.IQueryBus, Common.Infrastructure.CommandBus, Common.Infrastructure.QueryBus)
D21 · Naming Consistency· Inconsistent naming for product ID · ×1
Inconsistent naming for product ID: 'ProductId' vs 'PurchaseId'. While they might be different, the context suggests 'ProductId' is used in DTOs and events, but 'PurchaseId' is used in other events. — Use 'ProductId' consistently for product identifiers across DTOs and events. (symbols: Purchase.DTO.PurchaseItemDTO.ProductId, Common.Core.Events.ProductPurchasedEvent.LineItems, Common.Core.Events.ProductPurchasedEvent.PurchaseId)
D23 · Boundary Type-Coupling· Bounded contexts not declared · ×1
Bounded contexts not declared — At 2358 LoC across 36 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 namespace prefixes that belong to it — e.g. `architecture:` → `contexts:` → `Billing: ["Acme.Billing"]`, `Catalog: ["Acme.Catalog"]`.
early-stage repository — too little history to judge knowledge freshness — early-stage repository — too little history to judge knowledge freshness (33 commit(s) sampled).
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.
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 — No CI/build pipeline found (.github/.forgejo/.gitea workflows, .gitlab-ci.yml, azure-pipelines.yml, Jenkinsfile, .circleci); there is no build to attest provenance for.
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
—
Run 019faf7b-92f8-7df6-a9c9-d41afdd8a6d2 · 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 — 2 field(s) across 1 category, each with an exact repo-relative file:line. This is the data inventory a compliance review starts from — right-to-erasure, retention, minimisation. Detected by name with a deliberately specific classifier (the same one the GDPR dimensions use, so CardDefinition or FileName don't trip); informational — it feeds no score.
Issues: 13 · Warnings: 16 · Recommendations: 22 · Info: 33 — Appendix A · all findings · full markdown report.
Generated by Watchdog — deterministic code-health analysis. 29-07-2026 @ 20:05 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.