Public report of serengil/deepface, published 20 October 2026. It has the same score, grades and number of
findings as the full report. The details of security findings (where they are in the code, which known
vulnerabilities and which passwords or keys were found) are left out, so nobody can use them against the project.
Public
Executive reportMeasured under the Code Assurance Index, an open standard for code quality, using its published scoring rules (rubric version 2026.10.5). Watchdog signed this survey and filed it in the standard's public register, where anyone can look it up by its survey ID. Check this survey →
Signed and filed in the standard's public register, 20 October 2026, 18:56 UTC · Survey ID cd_a7b02cc53c6c42928646222a8fc4d8a0
serengil/deepface
Measured 20 October 2026, 18:51 UTC, at code version 8cd7e08
Supply-chain provenance & signing is not measured: Supply-chain provenance is not scored (this pipeline releases no artifact), but its build steps or GitHub Actions workflows have integrity defects, or nothing watches its dependency set for published advisories.
CAI scoreAdequate
CriticalWeakAdequateStrongExemplary
Down 4 since the last survey
The score is built from these lenses. Each lens grades one area of the code, such as how easy it is to change or how well it is protected. The weakest lens counts most, so one weak area pulls the score down instead of being averaged away.
A medium codebase: 22,541 lines of product code in 1 project, tests not counted · Estimated rebuild effort: about 0.2 person-years · Weakest lens: Production Readiness, 35
Findings by how serious they are
17 critical120 serious18 minor
15 dimensions are left out of the score. For each one, Watchdog either couldn't measure it in this code, or found no evidence of the safeguard it looks for, such as a tested backup restore. They count neither for nor against the score, and Limitations lists each one with its reason.
This survey was produced by
Watchdog
Producer
Canine Development
Analyzer
Watchdog engine 1.0.0
Measured
20 October 2026, 18:51 UTC
A measurement, not a certificate. The Code Assurance Index records a number
together with the evidence it was worked out from, so anyone can recalculate it, but it doesn't certify or guarantee
the code. The standard was written by Canine Development, which also builds Watchdog. It is open to any tool that
implements it, and today Watchdog is the only one that does.
The score is deterministic, which means the same code always gets the same score. Watchdog works it out with tools that follow published rules, and an AI model never decides it, so anyone who applies the same rules to the same code gets the same number. How it is done is explained under Methodology ▸
54of 58 dimensions measured by tools. The other 4 are advisory notes from an AI model, which explain the code but don't count towards the score.
140of 155 findings point to an exact file and line, so you can open each one in the code. The other 15 concern the repository as a whole, such as missing documentation.
58dimensions across 5 lenses, read from 22,541 lines of product code in 1 project.
serengil/deepface scores 52 out of 100 and is rated Adequate, while four of its five lenses score higher still: Architecture at 87, Code Health at 69 and Maturity at 61, all rated Adequate, and Security and Compliance at 77, rated Strong. The project hasn't declared a stable release yet, so its lenses are rated against a lower bar than a released product's, while the score itself is worked out as for any repository.
The score is lower than those four because the weakest lens counts most, and here that is Production Readiness, which scores 35 and is rated Weak. Its Security & performance tooling dimension scores 0 out of 10, and until that reaches 4, Production Readiness can't be rated higher than Adequate. The first actions are to resolve the 3 Banned license finding(s) in License Compliance and to resolve the 1 No ADRs found finding(s) in ADR Quality, and if Production Readiness rose to 70, the score would rise from 52 to about 67.
17 findings are critical: nine in Static Analysis (SAST), five in IaC & Container Security and three in License Compliance.
Watchdog couldn't measure 15 dimensions in this code, among them Code Coverage, Test Reliability and Dependency Hygiene, so they are left out of the score. The product code is 22,541 lines, which would take an estimated 0.2 person-years, around €28,000, to rebuild.
A lens is capped at Adequate while one of its dimensions scores below 4 out of 10, however well the rest of the lens does. That way a serious problem in one place always shows in the lens's rating.
How the score is built: what each lens counts forThe lenses don't count equally. The weakest lens counts most, the next weakest a little less, and so on, so a weak area pulls the score down instead of being averaged away. In the bar below, each part's width shows how much that lens counts and its colour shows its rating. Improving the widest part moves the score most, and as that lens improves, it counts for less, because another lens becomes the weakest.
Watchdog compares every finding with the previous survey's. A finding that has only moved to another line in the same file counts as the same finding, so only new findings appear here. Advisory notes from the AI model are left out, because the model words them afresh in every survey.
Fixed since the last survey: 13 findings
These 13 critical or serious findings were in the previous survey, on 4 October 2026, and are gone from this one.
Dependency Vulnerabilities · Medium CVE: REDACTED requirements.txt
IaC & Container Security · High IaC: DS-0029 REDACTED
Supply-chain Provenance & Signing REDACTED
Rebuild cost: about €28,000 · Estimate
Cost to rebuild
€9,100 to €46,000, for 152 to 484 hours of work, about 0.2 person-years for one engineer
Difficulty of the problem
Standard, judged from the kind of software and how it is built. Code that solves a harder problem, such as payments or medical bookings, costs more per line to write.
Quality factor
×0.7 at a score of 52. Better code takes more work to write, so the factor is ×1.0 at a score of 70, lower below it and higher above it.
Size
Medium: 22,541 lines of product code
How the estimate is made: Watchdog prices the hours a team would need to write this code again, at €60 to €95 an hour. Declarations and boilerplate are priced at a fast rate and logic at a slower one, and the logic is then multiplied by the difficulty of the problem and by the quality factor. The estimate can be about 30% off either way, and it moves most with the hourly rate and the difficulty. For this code, every line is priced as straight logic, which keeps the estimate on the low side.
Top priorities
The three actions that raise the score most for the work they take. The full ranked list is in the Roadmap.
1
Resolve the 3 Banned license finding(s) in License Compliance.
Resolve the 1 No ADRs found finding(s) in ADR Quality.
Raises the score by about 4 points · Low effort · ADR Quality
3
Add a SAST step to CI running what this repository's stack ships: bandit, `semgrep --config=p/python`, or CodeQL's python pack — so a security regression fails the build instead of landing.
Each conclusion below combines several dimensions into one point that no single dimension shows, and lists the measurements it rests on.
Where the investment is most exposed · Value at risk
This code would cost about €28,000 to build again, about 0.2 person-years of work. Its weakest lens is Production Readiness, at 35 (Weak). Production Readiness is about whether the product is ready to run in production and to change without breaking. Because the weakest lens counts most in the score, work on Production Readiness also moves the score most.
Based on: the rebuild estimate · the Production Readiness grade · how much each lens counts
What to do: Start with the Production Readiness actions in Top priorities.
If you do one thing first · First action
Resolve the 3 Banned license finding(s) in License Compliance. Of all the actions in this report, it raises the score most for the work it takes: about 13 points for low effort, because it clears the 3 License Compliance findings. The next survey will show the change in the score.
Based on: the ranked actions in Top priorities, by score gain and effort
Architecture: which parts of the code depend on which
The code is divided into modules, groups of code that each handle one part of the product, and a module depends on another when it uses code from it. The table lists the modules both down the side and across the top, in an order where each module should only use modules listed above it. A number in a cell means the module on that row uses the module in that column, and it counts the pairs of building blocks (types) that connect them. The one thing to look for is a mark above the diagonal line. It means two or more modules use each other in a loop (a dependency cycle), so none of them can be changed, tested or released on its own.
102 modules, with 82 dependencies between them. Every module uses only modules listed above it, so there are no loops.
The table shows the 40 modules with the most connections; 62 more are left out to keep it readable.
Which modules use which. The module on each row uses the module in each column, and the number counts the pairs of types that connect them. A cell above the diagonal is part of a loop.
api.src.dependencies.container uses api.src.dependencies.variables, so a change to api.src.dependencies.variables can break api.src.dependencies.container, while a change to api.src.dependencies.container can't break api.src.dependencies.variables.
Position
Below the diagonal: api.src.dependencies.container uses a module listed above it, which is the healthy direction.
models.demography.onnx.OnnxDemography uses models.Demography, so a change to models.Demography can break models.demography.onnx.OnnxDemography, while a change to models.demography.onnx.OnnxDemography can't break models.Demography.
Position
Below the diagonal: models.demography.onnx.OnnxDemography uses a module listed above it, which is the healthy direction.
models.demography.pytorch.TorchDemography uses models.Demography, so a change to models.Demography can break models.demography.pytorch.TorchDemography, while a change to models.demography.pytorch.TorchDemography can't break models.Demography.
Position
Below the diagonal: models.demography.pytorch.TorchDemography uses a module listed above it, which is the healthy direction.
models.demography.tf.Age uses models.Demography, so a change to models.Demography can break models.demography.tf.Age, while a change to models.demography.tf.Age can't break models.Demography.
Position
Below the diagonal: models.demography.tf.Age uses a module listed above it, which is the healthy direction.
models.demography.tf.Emotion uses models.Demography, so a change to models.Demography can break models.demography.tf.Emotion, while a change to models.demography.tf.Emotion can't break models.Demography.
Position
Below the diagonal: models.demography.tf.Emotion uses a module listed above it, which is the healthy direction.
models.demography.tf.Gender uses models.Demography, so a change to models.Demography can break models.demography.tf.Gender, while a change to models.demography.tf.Gender can't break models.Demography.
Position
Below the diagonal: models.demography.tf.Gender uses a module listed above it, which is the healthy direction.
models.demography.tf.Race uses models.Demography, so a change to models.Demography can break models.demography.tf.Race, while a change to models.demography.tf.Race can't break models.Demography.
Position
Below the diagonal: models.demography.tf.Race uses a module listed above it, which is the healthy direction.
models.face_detection.CenterFace uses models.Detector, so a change to models.Detector can break models.face_detection.CenterFace, while a change to models.face_detection.CenterFace can't break models.Detector.
Position
Below the diagonal: models.face_detection.CenterFace uses a module listed above it, which is the healthy direction.
models.face_detection.Dlib uses models.Detector, so a change to models.Detector can break models.face_detection.Dlib, while a change to models.face_detection.Dlib can't break models.Detector.
Position
Below the diagonal: models.face_detection.Dlib uses a module listed above it, which is the healthy direction.
models.face_detection.FastMtCnn uses models.Detector, so a change to models.Detector can break models.face_detection.FastMtCnn, while a change to models.face_detection.FastMtCnn can't break models.Detector.
Position
Below the diagonal: models.face_detection.FastMtCnn uses a module listed above it, which is the healthy direction.
models.face_detection.MediaPipe uses models.Detector, so a change to models.Detector can break models.face_detection.MediaPipe, while a change to models.face_detection.MediaPipe can't break models.Detector.
Position
Below the diagonal: models.face_detection.MediaPipe uses a module listed above it, which is the healthy direction.
models.face_detection.MtCnn uses models.Detector, so a change to models.Detector can break models.face_detection.MtCnn, while a change to models.face_detection.MtCnn can't break models.Detector.
Position
Below the diagonal: models.face_detection.MtCnn uses a module listed above it, which is the healthy direction.
models.face_detection.OpenCv uses models.Detector, so a change to models.Detector can break models.face_detection.OpenCv, while a change to models.face_detection.OpenCv can't break models.Detector.
Position
Below the diagonal: models.face_detection.OpenCv uses a module listed above it, which is the healthy direction.
models.face_detection.RetinaFace uses models.Detector, so a change to models.Detector can break models.face_detection.RetinaFace, while a change to models.face_detection.RetinaFace can't break models.Detector.
Position
Below the diagonal: models.face_detection.RetinaFace uses a module listed above it, which is the healthy direction.
models.facial_recognition.onnx.OnnxFacialRecognition uses models.FacialRecognition, so a change to models.FacialRecognition can break models.facial_recognition.onnx.OnnxFacialRecognition, while a change to models.facial_recognition.onnx.OnnxFacialRecognition can't break models.FacialRecognition.
Position
Below the diagonal: models.facial_recognition.onnx.OnnxFacialRecognition uses a module listed above it, which is the healthy direction.
models.facial_recognition.pytorch.TorchFacialRecognition uses models.FacialRecognition, so a change to models.FacialRecognition can break models.facial_recognition.pytorch.TorchFacialRecognition, while a change to models.facial_recognition.pytorch.TorchFacialRecognition can't break models.FacialRecognition.
Position
Below the diagonal: models.facial_recognition.pytorch.TorchFacialRecognition uses a module listed above it, which is the healthy direction.
modules uses modules.database.types, so a change to modules.database.types can break modules, while a change to modules can't break modules.database.types.
Position
Below the diagonal: modules uses a module listed above it, which is the healthy direction.
api.src.grpc_server uses api.src.dependencies.variables, so a change to api.src.dependencies.variables can break api.src.grpc_server, while a change to api.src.grpc_server can't break api.src.dependencies.variables.
Position
Below the diagonal: api.src.grpc_server uses a module listed above it, which is the healthy direction.
api.src.grpc_server uses api.src.dependencies.container, so a change to api.src.dependencies.container can break api.src.grpc_server, while a change to api.src.grpc_server can't break api.src.dependencies.container.
Position
Below the diagonal: api.src.grpc_server uses a module listed above it, which is the healthy direction.
models.demography.onnx.Age uses models.demography.onnx.OnnxDemography, so a change to models.demography.onnx.OnnxDemography can break models.demography.onnx.Age, while a change to models.demography.onnx.Age can't break models.demography.onnx.OnnxDemography.
Position
Below the diagonal: models.demography.onnx.Age uses a module listed above it, which is the healthy direction.
models.demography.onnx.Emotion uses models.demography.onnx.OnnxDemography, so a change to models.demography.onnx.OnnxDemography can break models.demography.onnx.Emotion, while a change to models.demography.onnx.Emotion can't break models.demography.onnx.OnnxDemography.
Position
Below the diagonal: models.demography.onnx.Emotion uses a module listed above it, which is the healthy direction.
models.demography.onnx.Gender uses models.demography.onnx.OnnxDemography, so a change to models.demography.onnx.OnnxDemography can break models.demography.onnx.Gender, while a change to models.demography.onnx.Gender can't break models.demography.onnx.OnnxDemography.
Position
Below the diagonal: models.demography.onnx.Gender uses a module listed above it, which is the healthy direction.
models.demography.onnx.Race uses models.demography.onnx.OnnxDemography, so a change to models.demography.onnx.OnnxDemography can break models.demography.onnx.Race, while a change to models.demography.onnx.Race can't break models.demography.onnx.OnnxDemography.
Position
Below the diagonal: models.demography.onnx.Race uses a module listed above it, which is the healthy direction.
models.demography.pytorch.Age uses models.demography.pytorch.TorchDemography, so a change to models.demography.pytorch.TorchDemography can break models.demography.pytorch.Age, while a change to models.demography.pytorch.Age can't break models.demography.pytorch.TorchDemography.
Position
Below the diagonal: models.demography.pytorch.Age uses a module listed above it, which is the healthy direction.
models.demography.pytorch.Emotion uses models.demography.pytorch.TorchDemography, so a change to models.demography.pytorch.TorchDemography can break models.demography.pytorch.Emotion, while a change to models.demography.pytorch.Emotion can't break models.demography.pytorch.TorchDemography.
Position
Below the diagonal: models.demography.pytorch.Emotion uses a module listed above it, which is the healthy direction.
models.demography.pytorch.Gender uses models.demography.pytorch.TorchDemography, so a change to models.demography.pytorch.TorchDemography can break models.demography.pytorch.Gender, while a change to models.demography.pytorch.Gender can't break models.demography.pytorch.TorchDemography.
Position
Below the diagonal: models.demography.pytorch.Gender uses a module listed above it, which is the healthy direction.
models.demography.pytorch.Race uses models.demography.pytorch.TorchDemography, so a change to models.demography.pytorch.TorchDemography can break models.demography.pytorch.Race, while a change to models.demography.pytorch.Race can't break models.demography.pytorch.TorchDemography.
Position
Below the diagonal: models.demography.pytorch.Race uses a module listed above it, which is the healthy direction.
models.facial_recognition.pytorch.ArcFace uses models.facial_recognition.pytorch.TorchFacialRecognition, so a change to models.facial_recognition.pytorch.TorchFacialRecognition can break models.facial_recognition.pytorch.ArcFace, while a change to models.facial_recognition.pytorch.ArcFace can't break models.facial_recognition.pytorch.TorchFacialRecognition.
Position
Below the diagonal: models.facial_recognition.pytorch.ArcFace uses a module listed above it, which is the healthy direction.
models.facial_recognition.pytorch.DeepID uses models.facial_recognition.pytorch.TorchFacialRecognition, so a change to models.facial_recognition.pytorch.TorchFacialRecognition can break models.facial_recognition.pytorch.DeepID, while a change to models.facial_recognition.pytorch.DeepID can't break models.facial_recognition.pytorch.TorchFacialRecognition.
Position
Below the diagonal: models.facial_recognition.pytorch.DeepID uses a module listed above it, which is the healthy direction.
models.facial_recognition.pytorch.Facenet uses models.facial_recognition.pytorch.TorchFacialRecognition, so a change to models.facial_recognition.pytorch.TorchFacialRecognition can break models.facial_recognition.pytorch.Facenet, while a change to models.facial_recognition.pytorch.Facenet can't break models.facial_recognition.pytorch.TorchFacialRecognition.
Position
Below the diagonal: models.facial_recognition.pytorch.Facenet uses a module listed above it, which is the healthy direction.
models.facial_recognition.pytorch.FbDeepFace uses models.facial_recognition.pytorch.TorchFacialRecognition, so a change to models.facial_recognition.pytorch.TorchFacialRecognition can break models.facial_recognition.pytorch.FbDeepFace, while a change to models.facial_recognition.pytorch.FbDeepFace can't break models.facial_recognition.pytorch.TorchFacialRecognition.
Position
Below the diagonal: models.facial_recognition.pytorch.FbDeepFace uses a module listed above it, which is the healthy direction.
1 pair of types, where a type in models.facial_recognition.pytorch.GhostFaceNet uses a type in models.facial_recognition.pytorch.TorchFacialRecognition.
models.facial_recognition.pytorch.GhostFaceNet uses models.facial_recognition.pytorch.TorchFacialRecognition, so a change to models.facial_recognition.pytorch.TorchFacialRecognition can break models.facial_recognition.pytorch.GhostFaceNet, while a change to models.facial_recognition.pytorch.GhostFaceNet can't break models.facial_recognition.pytorch.TorchFacialRecognition.
Position
Below the diagonal: models.facial_recognition.pytorch.GhostFaceNet uses a module listed above it, which is the healthy direction.
models.facial_recognition.pytorch.OpenFace uses models.facial_recognition.pytorch.TorchFacialRecognition, so a change to models.facial_recognition.pytorch.TorchFacialRecognition can break models.facial_recognition.pytorch.OpenFace, while a change to models.facial_recognition.pytorch.OpenFace can't break models.facial_recognition.pytorch.TorchFacialRecognition.
Position
Below the diagonal: models.facial_recognition.pytorch.OpenFace uses a module listed above it, which is the healthy direction.
models.facial_recognition.pytorch.VGGFace uses models.facial_recognition.pytorch.TorchFacialRecognition, so a change to models.facial_recognition.pytorch.TorchFacialRecognition can break models.facial_recognition.pytorch.VGGFace, while a change to models.facial_recognition.pytorch.VGGFace can't break models.facial_recognition.pytorch.TorchFacialRecognition.
Position
Below the diagonal: models.facial_recognition.pytorch.VGGFace uses a module listed above it, which is the healthy direction.
models.demography.pytorch uses models.demography.pytorch.Age, so a change to models.demography.pytorch.Age can break models.demography.pytorch, while a change to models.demography.pytorch can't break models.demography.pytorch.Age.
Position
Below the diagonal: models.demography.pytorch uses a module listed above it, which is the healthy direction.
models.demography.pytorch uses models.demography.pytorch.Emotion, so a change to models.demography.pytorch.Emotion can break models.demography.pytorch, while a change to models.demography.pytorch can't break models.demography.pytorch.Emotion.
Position
Below the diagonal: models.demography.pytorch uses a module listed above it, which is the healthy direction.
models.demography.pytorch uses models.demography.pytorch.Gender, so a change to models.demography.pytorch.Gender can break models.demography.pytorch, while a change to models.demography.pytorch can't break models.demography.pytorch.Gender.
Position
Below the diagonal: models.demography.pytorch uses a module listed above it, which is the healthy direction.
models.demography.pytorch uses models.demography.pytorch.Race, so a change to models.demography.pytorch.Race can break models.demography.pytorch, while a change to models.demography.pytorch can't break models.demography.pytorch.Race.
Position
Below the diagonal: models.demography.pytorch uses a module listed above it, which is the healthy direction.
models.facial_recognition.pytorch uses models.facial_recognition.pytorch.ArcFace, so a change to models.facial_recognition.pytorch.ArcFace can break models.facial_recognition.pytorch, while a change to models.facial_recognition.pytorch can't break models.facial_recognition.pytorch.ArcFace.
Position
Below the diagonal: models.facial_recognition.pytorch uses a module listed above it, which is the healthy direction.
models.facial_recognition.pytorch uses models.facial_recognition.pytorch.DeepID, so a change to models.facial_recognition.pytorch.DeepID can break models.facial_recognition.pytorch, while a change to models.facial_recognition.pytorch can't break models.facial_recognition.pytorch.DeepID.
Position
Below the diagonal: models.facial_recognition.pytorch uses a module listed above it, which is the healthy direction.
models.facial_recognition.pytorch uses models.facial_recognition.pytorch.Facenet, so a change to models.facial_recognition.pytorch.Facenet can break models.facial_recognition.pytorch, while a change to models.facial_recognition.pytorch can't break models.facial_recognition.pytorch.Facenet.
Position
Below the diagonal: models.facial_recognition.pytorch uses a module listed above it, which is the healthy direction.
models.facial_recognition.pytorch uses models.facial_recognition.pytorch.FbDeepFace, so a change to models.facial_recognition.pytorch.FbDeepFace can break models.facial_recognition.pytorch, while a change to models.facial_recognition.pytorch can't break models.facial_recognition.pytorch.FbDeepFace.
Position
Below the diagonal: models.facial_recognition.pytorch uses a module listed above it, which is the healthy direction.
models.facial_recognition.pytorch uses models.facial_recognition.pytorch.GhostFaceNet, so a change to models.facial_recognition.pytorch.GhostFaceNet can break models.facial_recognition.pytorch, while a change to models.facial_recognition.pytorch can't break models.facial_recognition.pytorch.GhostFaceNet.
Position
Below the diagonal: models.facial_recognition.pytorch uses a module listed above it, which is the healthy direction.
models.facial_recognition.pytorch uses models.facial_recognition.pytorch.OpenFace, so a change to models.facial_recognition.pytorch.OpenFace can break models.facial_recognition.pytorch, while a change to models.facial_recognition.pytorch can't break models.facial_recognition.pytorch.OpenFace.
Position
Below the diagonal: models.facial_recognition.pytorch uses a module listed above it, which is the healthy direction.
models.facial_recognition.pytorch uses models.facial_recognition.pytorch.VGGFace, so a change to models.facial_recognition.pytorch.VGGFace can break models.facial_recognition.pytorch, while a change to models.facial_recognition.pytorch can't break models.facial_recognition.pytorch.VGGFace.
Position
Below the diagonal: models.facial_recognition.pytorch uses a module listed above it, which is the healthy direction.
At a glance — Code Health · 69% · Adequate · gated by X4 ·
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
A02:2025 — Security Misconfiguration
15
High / Critical
A05:2025 — Injection
12
High / Critical
A03:2025 — Software Supply Chain Failures
2
Medium
Roadmap
First, resolve the three banned license findings and establish architectural decision records to ensure compliance and traceability. Next, integrate a static analysis step into the CI pipeline to block security regressions before they merge. Finally, codify backup and geo-recovery procedures in infrastructure as code while documenting the restore process to guarantee disaster readiness.
Ranked by impact ÷ effort. "Helps" is the estimated gain on the 0–100 health score.
Do this
Helps
Effort
Dimension
Resolve the 3 Banned license finding(s) in License Compliance.
Add a SAST step to CI running what this repository's stack ships: bandit, `semgrep --config=p/python`, or CodeQL's python pack — so a security regression fails the build instead of landing.
Record your significant design decisions. Write one short document per decision, giving its date, the situation, the decision and its consequences, and keep them together with your other design documents. A docs/adr folder with each file named by number and title is the easiest place to find them.
Add a `healthcheck:` to the served compose service — probing the endpoint it already answers on where it has one — with `depends_on: condition: service_healthy` on whatever waits for it, and keep the deployed image tag immutable and recorded so rolling back is re-pointing at the previous tag rather than rebuilding.
Every finding carries one of four grades. Three say how serious it is. The fourth says this
survey could not settle it — and it is a grade, not a gap.
Critical — 17
A definite problem that already costs you something and drags the score down: a
missing authorisation check, a dependency with a known exploit, a build that does not reproduce. Failure here
tends to cause failures elsewhere.
Serious — 120
Likely wrong, but not failing yet. It degrades
the codebase over a longer horizon and can cause failures elsewhere — not urgent this week, not something to
carry for two years either.
Minor — 18
Recorded, with no effect on how the codebase functions.
Present so the survey is complete, not because it needs doing.
Could not be resolved — 15
Something this survey could not settle
from the outside, and which could be critical or serious. Either a control was required and no
positive evidence of it exists in the repository — a backup job that nothing shows was ever restored from proves
nothing about restores — or our own analysis could not run over that part of the tree. This is not a clean
result. These are excluded from the score rather than awarded a pass, so the number on the cover neither
rewards nor penalises them: if you act on this survey without resolving them, you carry that risk yourself. Each
one is named under Limitations.
Methodology & how to trust this report
Watchdog is a deep, periodic assessment — run each sprint, monthly, or quarterly, taking the time to go wider and deeper than a quick check and surfacing in one coherent report what you'd otherwise piece together from a dozen separate tools. It scores deterministically: the same commit yields the same score, every run. 54 of 58 evaluated dimensions are computed purely by tools and static analysis (confidence 1.0); 4 documentation/naming judgement(s) are LLM-assisted and labelled advisory. Overall confidence is 0.8 — 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 — 58 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, 140 of 155 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.
D8 Code Coverage — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. Coverage NOT MEASURED: test source is present (.py) but the built-in coverage collector has no runner for this repository's ecosystem — so this suite was never executed by it. Not scored — this is a gap in the analyzer's language coverage, not a defect in the repo. To have real coverage read, produce a coverage report in a standard format (`coverage run -m pytest` then `coverage xml`) and commit it — a hosted scan measures a clone of the repository, so a report that exists only in a working tree, a CI runner's or your own, never reaches it; the artefact is commonly gitignored, so `git add -f` that one file (or un-ignore its path) and commit it alongside the code it measures. You can widen what we reach: optional: produce a coverage report in a standard format (`coverage run -m pytest` then `coverage xml`) and commit it — a hosted scan measures a clone of the repository, so a report that exists only in a working tree, a CI runner's or your own, never reaches it; the artefact is commonly gitignored, so `git add -f` that one file (or un-ignore its path) and commit it alongside the code it measures — then the real number is read on the next scan.
D11 Test Reliability — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. Test source is present (.py) and this repository declares a Python test suite (repository root, 48 test files), but it was not re-run: its dependency install outgrew the 8 GB scratch ceiling (8.158 GB measured) and was stopped; its suite was not run. Not scored — this is a gap in the analyzer's language coverage, not a finding about this repository.
D12 Dependency Hygiene — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. Not scored — 30 shipped Python distribution(s) were read, but the outdated signal needs pypi.org, and no declaration here carries an exact pin to ask about — a floor or a range installs the newest release it admits and cannot be behind one, so this dimension's own question is only partly answered. NOT a finding that these dependencies are current or healthy.
D22 Internal API Consistency — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. D22 identifies the intentionally-exposed surface from `IsPackable` and `.Contracts` project names, MSBuild conventions read off the loaded project set. This target exposed no such projects, so the probe never ran; this says nothing about whether the repository has a public API. This repository commits no C#/VB source at all, so there was never an MSBuild project set to read these conventions off. That is OUR side and it is an ADMISSION gap, not an environment fault: no published-package marker D22 reads admitted any project here. D22 DOES collect the public API of every language in this tree, and admits a project only when its manifest makes that ecosystem's publication act in a form the marker recognises — so what is missing is not a collector but an admission rule for the publication act this repository makes, if it makes one. The remedy is a better discriminator for that marker — not a collector, and no change to the scan image can close it. The marker's rules are measured to refuse applications, so one refused repository is the rule working as calibrated; several are the evidence that it is too strict.
D27 Navigability — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. Navigability is taken over call sites whose callee resolved. For C# that population includes calls into referenced assemblies; for this language only calls declared inside the repository count, so the abstention threshold reads the repository's use of its standard library as a resolution failure. That is a limit of this analyzer, not a build problem in this repository.
AX6 Interface segregation — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. This check is computed over the public interfaces this run's compilations declare, and none was loaded, so it had nothing to read. That is a gap in this analyzer's language reach — not a finding that the repository is free of what this check looks for.
AXR1 Runtime accessibility — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. Render strategy: boot via DockerCompose (REDACTED). compose up failed (exit 1 — a container did not start, exited, or never became healthy) — services.pgvector: container name "postgres" is already in use by service {}"; runtime evidence skipped.
C1 Data Protection — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. These personal data controls are read from declarative annotations, request middleware, entity/column names and guard methods in a C# source model, and none was loaded on this run, so there was nothing to gather. That is a gap in this analyzer's language reach — not a finding that the repository lacks personal data controls.
GD1 Unfinished & placeholder code — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. This check reads C# syntax, F#, JavaScript/TypeScript, Dart, Ruby and Scala source only, and no C#, F#, JavaScript/TypeScript, Dart, Ruby or Scala was found, and this repository's Python is not read yet, so it had nothing of this repository's product to read. That is a gap in this analyzer's language reach — not a finding that the repository is free of what this check looks for.
IC1 Incompleteness & stubs — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. This check reads C# syntax, JavaScript/TypeScript, Java, Dart, Ruby and Scala source only, and no C#, JavaScript/TypeScript, Java, Dart, Ruby or Scala was found, and this repository's Python is not read yet, so it had nothing of this repository's product to read. That is a gap in this analyzer's language reach — not a finding that the repository is free of what this check looks for.
S1 Web-Security Posture — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. These web-security controls are read from declarative annotations, request middleware, entity/column names and guard methods in a C# source model, and none was loaded on this run, so there was nothing to gather. That is a gap in this analyzer's language reach — not a finding that the repository lacks web-security controls.
X16 Unfloored truncation loop — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. This check reads C# syntax, and JavaScript/TypeScript, Java, PHP, Dart, Ruby, Scala and Kotlin source only, and no C# was loaded and no JavaScript/TypeScript, Java, PHP, Dart, Ruby, Scala or Kotlin was found in this repository, so it had nothing of this repository's product to read. That is a gap in this analyzer's language reach — not a finding that the repository is free of what this check looks for.
X18 Disposal-pattern correctness — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. This check reads C# syntax, and JavaScript/TypeScript, Java, Kotlin, Dart, Ruby, Scala and PHP source only, and no C# was loaded and no JavaScript/TypeScript, Java, Kotlin, Dart, Ruby, Scala or PHP was found in this repository, so it had nothing of this repository's product to read. That is a gap in this analyzer's language reach — not a finding that the repository is free of what this check looks for.
X29 Per-element action decided by a fixed element — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. This check reads C# syntax, and JavaScript/TypeScript, Java, Rust, Dart, Ruby, Scala and Kotlin source only, and no C# was loaded and no JavaScript/TypeScript, Java, Rust, Dart, Ruby, Scala or Kotlin was found in this repository, so it had nothing of this repository's product to read. That is a gap in this analyzer's language reach — not a finding that the repository is free of what this check looks for.
X5 Nullable reference types — not measured this run — Watchdog could not measure this here. That is a gap on our side — a collector, parser or image we have not built yet — and it is neither a defect in this repository nor evidence that the check would have passed. This check reads C# syntax and MSBuild project configuration, TypeScript source and its tsconfig files, Java source and its package and module declarations, PHP source and its PHPStan and Psalm configuration, Dart source and its pubspec language version, Ruby source and its Sorbet and Steep configuration, Scala source and its build's explicit-nulls, WartRemover and Scalafix settings, and Kotlin source only, and no C# was loaded, no TypeScript was found, and this repository's Python is not read yet, so it had nothing of this repository's product to read. That is a gap in this analyzer's language reach — not a finding that the repository is free of what this check looks for.
Repo exclusion declarations: 1 pattern(s) declared (.gitattributes linguist-generated/vendored, .editorconfig generated_code) excluded 0 source file(s) from code-quality scoring. Declarations are the repo's own visible statement that a tree is machine-written or vendored — auditable in any diff, honored by GitHub the same way.
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 — an in-process token-stream comparison over sliding windows, with type-aware normalization — so it finds copy-paste, not semantic duplication expressed differently. Committed machine-written code (scaffolded migrations, designer/codegen output, protobuf/OpenAPI stubs, model snapshots) is EXCLUDED — its repetition is the tool's, not the team's — so the score reflects hand-written duplication only.
D6 Cohesion (LCOM4): LCOM4 cohesion is syntactic — it infers connectivity from which methods touch which fields/methods by name, not from real runtime behaviour or intent.
D9 Test Distribution: The test-pyramid shape is inferred from project/folder naming and references, with a single test host bucketed per-file by its path tier and content signals — a suite that names tiers unconventionally and gives no per-file signal can still be mis-bucketed.
D13 Secret Scanning: Secret detection is signature- and entropy-based on the current tree — a secret that does not match a known pattern, or one already rotated, will not be flagged (a clean scan is "nothing matched", not "no secrets exist").
D14 License Compliance: License compatibility is checked against declared package metadata and a policy — mislabelled or missing license metadata, and obligations that depend on how you distribute, are not resolved here.
D15 Churn × Complexity Hotspots: Churn hotspots come from git history — a freshly imported or squashed repository has no churn signal, and recent rewrites can mask a historically risky file.
D16 Bus Factor: Bus-factor is a time-decayed model of commit attribution (who has recently, repeatedly worked a file), not comprehension — pairing, review and reading-without-committing spread knowledge it can't see; bot commits and shared accounts still distort it.
D17 Explicit Debt: Acknowledged-debt signals (TODO/FIXME, suppressions, dead code) are textual — undocumented debt that nobody marked, and debt that lives in design rather than annotations, is invisible. Committed machine-written code (scaffolded migrations, designer/codegen output, generated stubs) is excluded — it is never the team's dead code to delete.
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. Its critique rows are drawn from a closed category vocabulary and each row means the same thing in every run, so two scans can be compared row by row; the SET that fires is still a sample, and does not repeat exactly. Measured on one frozen input, six scans at one engine SHA: 2-5 critique rows per scan, 8 distinct rows across the six, 3 of those 8 seen in only one scan. So a D19 row is evidence about the documentation, but a COUNT of D19 rows is not a quantity — never read a change in it as an improvement or a regression.
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.
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").
D30 Dependency Vulnerabilities: CVE matching depends on accurate package/version metadata and on the advisory databases — a vulnerability with no published advisory, or in code not declared as a dependency, is not seen. Coverage needs a RESOLVED graph: an unpinned requirements.txt, or a pom without a resolved build, yields partial coverage rather than a clean verdict. An ecosystem the analyzer cannot scan is reported as unmeasured, never as clean.
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.
D43 Malicious Dependencies: Only packages some vulnerability database has already NAMED as malicious are seen — a compromise published in the last hours, or never reported at all, is invisible here, and this dimension reading 10 is not evidence that a dependency is trustworthy. There is no typosquat or dependency-confusion analysis: a package nobody has reported is simply absent from the feeds. Coverage is the dependency scan's: an ecosystem that could not be scanned is disclosed as unmeasured, never as clean.
D44 Platform End-of-Life: The support table is FROZEN, so it goes out of date by losing RECALL: a release that ended support after the table was written is missed until the table is refreshed, and this dimension reading 10 is not evidence that a platform is current. Only platforms the repository DECLARES in a place this pass reads are seen — a runtime named only in a REDACTED (D31's subject), in a CI workflow (D29's), or in a file this pass does not parse (go.mod, a Gemfile ruby directive) is invisible here, which is why a repository declaring none of them abstains rather than scoring. Only frameworks with a PUBLISHED support policy are tracked: React, Flask and Express publish none, so their age cannot be judged and their absence from a report is not a statement that they are supported.
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".
P5 DR & Backup: Backup/restore and disaster-recovery readiness is judged from in-repo evidence — a config that exists is not a tested restore, so the absence of positive evidence is reported as "not evidenced", never scored as present.
P6 Release Hygiene: Rollback/observability controls are inferred from repo artefacts (pipelines, dashboards-as-code) — controls configured in external tooling, with no in-repo trace, cannot be credited.
The LLM boundary
LLM-set scores this run (4): D19, D21, D26, M4 (model: Local LLM). For these, a model reads a bounded sample and sets the numeric score; each names its own sample and method on its card. They 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.
+ 5 more group(s) — more in Appendix A; the complete list is findings.md.
What to do
Bring the 10 bodies over 15 down to 15 or less in Cyclomatic Complexity — start with datastore.search (cyclomatic 43), recognition.find (cyclomatic 42), detection.extract_faces (cyclomatic 33). — This score is capped by its worst body, so a finding fixed alone moves it by almost nothing — the next one down takes its place. Refactoring these 10 together lifts Cyclomatic Complexity from 8.1 to about 10.0/10, projected with the scoring formula itself and assuming each lands exactly at 15.
Enforce Cyclomatic Complexity in CI to reach Verified (currently Documented). — Hardens enforcement from Documented toward Prevented — provenance only; does not change the score.
Detailed fixes: d1_recommendation.md · top locations in Appendix A, every location in findings.md.
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.
+ 12 more group(s) — more in Appendix A; the complete list is findings.md.
What to do
Bring the 1 body over 60 down to 60 or less in Cognitive Complexity — start with datastore.search (cognitive 84). — Refactoring it lifts Cognitive Complexity from 6.8 to about 7.1/10.
Enforce Cognitive Complexity in CI to reach Verified (currently Documented). — Hardens enforcement from Documented toward Prevented — provenance only; does not change the score.
Detailed fixes: d2_recommendation.md · top locations in Appendix A, every location in findings.md.
Do you agree with this assessment?
D3 · God Classes9.0 / 10Strong✓ 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.
Resolve the 5 FileTooLong finding(s) in God Classes — start with Facenet.py, weaviate.py, datastore.py. — One of this dimension's main actionable groups (5 warning-level).
Enforce God Classes in CI to reach Verified (currently Documented). — Hardens enforcement from Documented toward Prevented — provenance only; does not change the score.
Detailed fixes: d3_recommendation.md · top locations in Appendix A, every location in findings.md.
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.
62 duplicated block group(s) detected. A further 2 rows report members as variants of one another; they aggregate block groups already counted above and are not themselves counted.
+ 41 more group(s) — more in Appendix A; the complete list is findings.md.
What to do
Resolve the 4 Duplicated block (9 lines × 2) finding(s) in Code Duplication — start with OpenFace.py (3), Facenet.py. — One of this dimension's main actionable groups (4 warning-level).
Resolve the 4 Duplicated block (5 lines × 2) finding(s) in Code Duplication — start with FasNetBackbone.py (2), OnnxFacialRecognition.py, OpenFace.py. — One of this dimension's main actionable groups (4 warning-level).
Resolve the 3 Duplicated block (18 lines × 2) finding(s) in Code Duplication — start with VGGFace.py, pgvector.py, verification.py. — One of this dimension's main actionable groups (3 warning-level).
Enforce Code Duplication in CI to reach Verified (currently Documented). — Hardens enforcement from Documented toward Verified — provenance only; does not change the score.
Detailed fixes: d4_recommendation.md · top locations in Appendix A, every location in findings.md.
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.
319 test methods: 288 unit, 31 integration, 0 BDD, 0 e2e. The Python suite contributes 319 test function(s) across 48 file(s) declaring at least one — every `def test…` in a file pytest or unittest would collect, which is those frameworks' own definition of a case; a parametrize table counts once, so this is a floor. Its tier split is read from file names and paths only.
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.
3 of 130 shipped Python distribution(s) use a banned license. Licences were resolved from PyPI over the distributions a consumer installs — this repository's 30 declared runtime requirement(s) closed transitively over each distribution's published `requires_dist` (100 reached that way). Requirements it states ONLY under an extra, a PEP 735 dependency group, a Poetry dev group or a dev-named requirements file are excluded: pip does not install any of them for a consumer. ★ This repository commits no dependency lockfile that this pass reads, so each licence is the one PyPI publishes for the distribution's CURRENT release rather than for a pinned version. 20 of them publish no licence on PyPI this pass can read; that is missing data, not a violation, and none of them is charged.
Banned license: ultralytics · ×3
What to do
Resolve the 3 Banned license finding(s) in License Compliance. — One of this dimension's main actionable groups (3 issue-level).
Enforce License Compliance in CI to reach Verified (currently Documented). — Hardens enforcement from Documented toward Verified — provenance only; does not change the score.
Detailed fixes: d14_recommendation.md · top locations in Appendix A, every location in findings.md.
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.
Resolve the 4 Hotspot finding(s) in Churn × Complexity Hotspots — start with recognition.py, datastore.py, weaviate.py. — One of this dimension's main actionable groups (4 warning-level).
Detailed fixes: d15_recommendation.md · top locations in Appendix A, every location in findings.md.
Do you agree with this assessment?
D16 · Bus Factor6.5 / 10Adequate✓ Tool-verified
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.
29 source file(s) have their living knowledge concentrated in one author (≥90% of recent, decayed contribution). The largest is deepface/modules/database/neo4j.py. Counted over 82 of the 138 production source files in this repository: the rest are under the ~2,400-byte size floor this dimension measures over.
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.
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.
1 deducted task-comment markers across 22541 LoC (0.0/KLoC) → score 10.0. Task comments only: this repository's language is read without a compiler, so D17's suppression, dead-code and commented-out-code arms did not run and this score counts fewer marker kinds than a .NET repository's would.
TodoCommenttests/unit/test_commons.py:364
What to do
Resolve the 1 TodoComment finding(s) in Explicit Debt — start with test_commons.py. — One of this dimension's main actionable groups (1 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.
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 repository's root README (deepface) and benchmarks/README.md are the only documentation present. The deepface README is a well-structured overview with badges, an executive summary of what the project does, and links to tests, blog, YouTube, Patreon, GitHub sponsors, and buy-me-a-coffee; it also includes a full document outline (deepface; Installation; runs deepface on onnxruntime by default; or to run deepface on tensorflow; manage backend deep learning framework if you have many; export DEEPFACE_BACKEND_ENGINE=onnx; register images into the database; identify a given image with a registered face; perform exact search; building index is required for ann search on non-vector dbs (e.g. postgres, mongo), but skipped for native vector dbs; perform approximate nearest neighbor search; anti spoofing test in face detection; anti spoofing test in real time analysis; running the service directly; running the service via docker; install grpc dependencies; generate stubs from proto file; run the grpc server; Extended Applications; Support; Citation) and a clip marker. benchmarks/README.md is an internal benchmarks directory README that documents itself, not the repository — it is well written for its purpose but does not address the root project's overview, installation, or usage.
What to do
Improve Documentation Quality — currently 8.0/10. — The repository's root README (deepface) and benchmarks/README.md are the only documentation present. The deepface README is a well-structured overview with badges, an executive summary of what the project does, and links to tests, blog, YouTube, Patreon, GitHub sponsors, and buy-me-a-coffee; it also includes a full document outline (deepface; Installation; runs deepface on onnxruntime by default; or to run deepface on tensorflow; manage backend deep learning framework if you have many; export DEEPFACE_BACKEND_ENGINE=onnx; register images into the database; identify a given image with a registered face; perform exact search; building index is required for ann search on non-vector dbs (e.g. postgres, mongo), but skipped for native vector dbs; perform approximate nearest neighbor search; anti spoofing test in face detection; anti spoofing test in real time analysis; running the service directly; running the service via docker; install grpc dependencies; generate stubs from proto file; run the grpc server; Extended Applications; Support; Citation) and a clip marker. benchmarks/README.md is an internal benchmarks directory README that documents itself, not the repository — it is well written for its purpose but does not address the root project's overview, installation, or usage.
Detailed fixes: d19_recommendation.md.
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D20 · ADR Quality0.0 / 10Critical✓ Tool-verified
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.
1 of 1 build units (Python) flagged as possibly oversized/incoherent.
Projects may be oversized for their cohesion
What to do
Resolve the 1 Projects may be oversized for their cohesion finding(s) in Project Cohesion. — One of this dimension's main actionable groups (1 recommendation-level).
Detailed fixes: d26_recommendation.md · top locations in Appendix A, every location in findings.md.
What it measures: Whether any secrets were ever committed — scanned across the full git history, not just now.
Method: Secret scan via TWO gitleaks detect passes in an isolated checkout — the full git history, then a second --no-git pass over the working tree as it stands — merged and de-duplicated by (rule, file, line); each match flagged High. Both invocations are recorded in the audit trail. Exhaustive; when the tool is absent, or when its output cannot be parsed into the expected shape, the dimension is WITHHELD as an explicit measurement gap on our side — unscored and excluded from the lens, never a hedged middling score.
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).
12 finding(s): 0 critical, 9 high, 3 medium, 0 low. 6 unpinned-GitHub-Actions row(s) are reported here but scored by D36 (supply-chain provenance), which measures that posture as `pinned_actions` — one pinning decision is charged once, not once per lens. semgrep hit a parse error in 1 file(s) — `REDACTED` (lines 3–50) — so no absence of findings in the named regions is evidence of anything; rows reported elsewhere in those files are real. Fix the syntax error (or exclude the file deliberately) and re-scan to cover them.
REDACTED
REDACTED
REDACTED
What to do
Resolve the 3 REDACTED finding(s) in Static Analysis (SAST) — start with REDACTED (3). — One of this dimension's main actionable groups (3 issue-level).
Resolve the 3 REDACTED finding(s) in Static Analysis (SAST) — start with REDACTED (2), REDACTED. — One of this dimension's main actionable groups (3 warning-level).
No action in Static Analysis (SAST) — all 6 REDACTED finding(s) are reported here at file:line but scored by D36 (supply-chain provenance), so none is charged to this dimension. — One of this dimension's main actionable groups (6 issue-level, 0 of them charged here).
Detailed fixes: d29_recommendation.md · top locations in Appendix A, every location in findings.md.
What it measures: Whether any dependency has a known published vulnerability (CVE), direct or transitive, in ANY ecosystem the repository declares — Dart pub, Elixir and Erlang via Hex, Go modules, Java and Kotlin via Maven/Gradle, JavaScript/npm, .NET/NuGet, PHP/Composer, Python/PyPI, RubyGems, Rust/Cargo and Swift.
Method: Dependency-CVE scan across every ecosystem the repository declares, scored ONCE. Three sources are unioned and deduplicated by advisory identity (rule id + alias closure, CVE<->GHSA) scoped to package+version, keeping the worst severity: `osv-scanner --recursive` over osv.dev for Dart pub, Elixir/Hex (and Erlang, whose `rebar.lock` syft first converts to a CycloneDX SBOM the scanner reads, with rows attributed back to the lock), Go, Java and Kotlin via Maven/Gradle (and Scala, whose sbt build's pinned direct declarations are written into a CycloneDX SBOM the scanner reads, with rows attributed back to the build file), npm, PHP/Composer, Python/PyPI, RubyGems, Rust/Cargo and Swift; `trivy fs --scanners vuln` for npm lockfiles; and `dotnet list package --vulnerable --include-transitive` for NuGet (with per-advisory collapse of the project x target-framework fan-out), plus a DECLARED-dependency arm that resolves a published gem's gemspec against rubygems.org where no Gemfile.lock is committed. `SeverityScore(c,h,m,l, normalizer 8.0)`. NotApplicable only when NO ecosystem is readable; if any applicable ecosystem could not be scanned the findings are REPORTED and the score is withheld. Supersedes the npm and OSV arms, retired 2026-09-05.
Resolve the 2 Medium CVE finding(s) in Dependency Vulnerabilities — start with REDACTED, REDACTED. — One of this dimension's main actionable groups (2 warning-level).
Detailed fixes: d30_recommendation.md · top locations in Appendix A, every location in findings.md.
Resolve the 5 High IaC finding(s) in IaC & Container Security — start with REDACTED (3), REDACTED (2). — One of this dimension's main actionable groups (5 issue-level).
Resolve the 9 Medium IaC finding(s) in IaC & Container Security — start with REDACTED (6), REDACTED (3). — One of this dimension's main actionable groups (9 warning-level).
Resolve the 1 Low IaC finding(s) in IaC & Container Security — start with REDACTED. — One of this dimension's main actionable groups (1 recommendation-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.
Every significant source file has living knowledge — recently and meaningfully worked. Counted over 82 of the 138 production source files in this repository: the rest are under the ~2,400-byte size floor this dimension measures over.
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. A non-source file is never a coupling PARTICIPANT either: documentation, schemas, config and data files are dropped with the rest, so a code↔docs pair — a command and the reference page that restates it — is not reported however strongly the two co-change; nor is coupling that runs THROUGH a build step or config file.
Resolve the 1 Change coupling clique finding(s) in Change Coupling — start with Age.py. — One of this dimension's main actionable groups (1 warning-level).
Detailed fixes: d35_recommendation.md · top locations in Appendix A, every location in findings.md.
What it measures: Whether any dependency the repository declares is published as MALICIOUS rather than merely vulnerable — a package that is an attacker's work, in any ecosystem osv-scanner reads. Scored apart from D30 because the answer is binary: there is no safe version to upgrade to, and the fix is to remove the package and rotate every credential it could have read.
Method: The same dependency scan D30 reads, partitioned on the scanner's own classification rather than rescanned: a row is MALICIOUS when its id is in the `MAL-` space (the ossf/malicious-packages feed) OR its `database_specific.cwe_ids` carries `CWE-506` ("Embedded Malicious Code"). Both channels are structural; the summary text is deliberately NOT read, because a malicious-package record whose summary says only "Critical severity vulnerability" is a real shape ([GHSA redacted]) and a text matcher misses it. Scored BINARY: any surviving row is 0, whatever its severity and however many CVEs sit beside it — a hostile dependency is not a quantity. Applicability and degradation are D30's: NotApplicable only when no ecosystem is readable, and an unscannable ecosystem degrades rather than reading clean. SCORED, not informational.
What it measures: Whether anyone still ships security patches for the platform this repository RUNS ON — the runtime it pins and the framework majors its own constraints hold it to. Separate from D12 because the question differs: a current Django on an end-of-life Python is perfectly up to date and completely unsupported, and the fix is a migration rather than a version bump. What the repository says it merely SUPPORTS is never charged.
Method: End-of-life PLATFORM read from the repository's own declarations and graded against a FROZEN, dated table of vendor support dates — no network, no feed, no API, so this dimension answers identically inside a closed scan fence. Two subjects: a RUNTIME the project pins (a single or all-end-of-life TargetFramework, a .nvmrc or .python-version, a requires-python CAP) and a FRAMEWORK major a dependency constraint cannot move off (a caret, tilde or exact version; `vue@^2.7.16` pins Vue 2). A FLOOR is deliberately never charged — `requires-python = ">=3.8"` states what a package SUPPORTS, not what it runs on — and a multi-target project is charged only when EVERY target is out of support. Runtime 4.0/product capped 8.0, framework 1.5 capped 4.5. The table is safe to freeze because a statement about support that ended in the past cannot become false: it loses recall as it ages, never precision, and a test asserts every entry predates the freeze date. Disjoint from D31 (a container image's OS layer) and D29 (the toolchain a CI workflow installs). Abstains when the repository declares no platform this pass reads — never scores it clean.
0 end-of-life runtime(s) and 0 end-of-life framework(s), read from 0 platform declaration(s) and 30 dependency declaration(s). This dimension reads what the repository says about ITSELF — a pinned target framework, a version file, a capped requires-python, a Rust toolchain pin, a framework major a constraint cannot move off. A FLOOR is deliberately never charged: `requires-python = ">=3.8"` states what the package SUPPORTS, not what it runs on, and a well-maintained library declares exactly that while running its own CI on a current release. The end-of-life facts are FROZEN and dated, so this dimension needs no network and answers identically inside a closed scan fence; as the table ages it loses recall and never precision, because a statement about support that ended in the past cannot become false. The OS layer of a container image is D31's question and the toolchain a CI workflow installs is D29's; this row is neither.
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. How each file's role is decided, because the split is only as good as that: a generated name or a build-output tree makes it Generated, a test project makes it Test, and otherwise the file's NAMESPACE and PATH words are matched against fixed vocabularies in a fixed ORDER — domain, then infrastructure, then application — so a file whose words hit two layers is counted under the earlier one. A production file matching none of them counts as application, so that share reads 'application or unclassified' rather than a measured application layer. Roles come from naming convention, never from what the code does. On this repository the split was taken from the source tree on disk rather than from a loaded .NET workspace, so a file's role is decided by its PATH segments alone — no declared namespace was available to add to the evidence — and generated output is excluded from the census entirely rather than counted as a generated share.
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, but most of the rest matched no layer vocabulary at all — so this is not yet an anemic-domain finding. The namespace/path convention could not place that code, which makes the composition above a statement about the naming, not about the design. Name the layers (or check that the repository's conventions differ from the ones this check knows) before reading a thin domain into it.
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.
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C2 · Access Controls7.0 / 10Strong✓ Tool-verified
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.
Authorization IS enforced here (by route guards, auth middleware or checks that refuse a caller) — this is NOT a claim that endpoints are unprotected. What's missing is a NAMED rule set: who may do what is encoded inline where it is checked, so the rules can't be listed, reviewed or tested apart from the code that applies them. Recommendation, not a defect.
What to do
Name the access rules — DRF permission classes, django-guardian or django-rules permissions, a casbin or oso policy, or roles and permissions declared once — and consult them where access is checked (permission_classes, @permission_required, Depends), so the rule set is reviewable and testable on its own.
Maturity · Maturity — Whether the repo and its projects have a README, and whether it's substantive and current.
Method: Filesystem scan: README presence, word count, and headings for depth; git history for staleness. Exhaustive across root and project dirs, deterministic.
What to do
Add an 'Architecture' / 'How it works' section to the root README — the high-level shape.
Maturity · Maturity — Whether key decisions (ADRs) and the high-level shape (C4/diagrams) are written down.
Method: Filesystem scan: ADR folder/naming conventions or content, plus Mermaid/PlantUML/C4/architecture.md discovery. Exhaustive, deterministic.
No Architecture Decision Records found — no conventional ADR directory, no numbered `NNNN-title` documents in any markup this check reads, and nothing ADR-shaped by content. Design rationale recorded elsewhere (a design-notes tree, a mailing list, pull-request discussion) is not visible to this check and is not re-findable per decision, so a future maintainer cannot ask why one choice was made and get an answer.
What to do
Record your significant design decisions. Write one short document per decision, giving its date, the situation, the decision and its consequences, and keep them together with your other design documents. A docs/adr folder with each file named by number and title is the easiest place to find them.
Maturity · Maturity — Whether the repo is organised deliberately — src/test separation and consistent project naming.
Method: Filesystem scan: src/test folder separation and namespace-prefix consistency (majority RootNamespace agreement). Exhaustive across projects, deterministic.
Production code isn't grouped under a src/ folder — it's spread across several top-level directories, so there's no one place that says 'this is the product'.
What to do
Group production code under src/ (or split deliberately, e.g. backend/ + frontend/) so production and tooling code aren't mixed at the root.
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.
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P1 · CI/CD gates8.0 / 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 but no build step was matched — changes may merge without the build ever running. A build step may be invoked directly as a command, or declared as a task that a runner named in the pipeline resolves.
What to do
Add an explicit build step to your CI pipeline — your stack's own build command, or, if the pipeline delegates to a task runner, a build task that runner executes in CI — so every change is built before merge.
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P10 · Library API & versioning10.0 / 10Exemplary○ Nothing flagged
Readiness · Readiness — For a library: a deliberate (small) public API surface and explicit semantic versioning so consumers can depend on it safely.
Method: Roslyn scan: public API surface area and semantic-versioning markers (SemVer attributes, changelog entries) for libraries; off .NET, a library is the ecosystem's publication act (an npm package that is not private and names an entry point, a PyPI distribution with a build system, a Rust library crate, a Maven/Gradle module that publishes, a Go module with no package main, a gemspec, a Composer library, a SwiftPM library product, a pub.dev or Hex package), its surface is the share of types the language model records as public (Rust, Swift, Java, Kotlin, Go, Dart; not measured where the model records no type visibility or, as in TypeScript, only module-level export), and its version is read from the manifest, a semver CHANGELOG, release tooling or semver git tags. Exhaustive, deterministic.
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P2 · Observability7.0 / 10Strong✓ 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 bandit, `semgrep --config=p/python`, or CodeQL's python pack as a CI step. What was searched, so you can tell an absence from a miss: the 2426 CI workflow file(s) in this repository, and the scanner and linter configuration checked in beside them. A scan that runs outside CI, one configured in your forge's web UI rather than in a committed file, or a tool whose name is none of those this check carries, is not seen — if that is your case the row is wrong, and saying so is more useful than adding a second scanner.
What to do
Add a SAST step to CI running what this repository's stack ships: bandit, `semgrep --config=p/python`, or CodeQL's python pack — so a security regression fails the build instead of landing.
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 is orchestrated by compose, but no service declares a `healthcheck:` and nothing pins a previous image to fall back to — the runtime can tell that the container is up, not that it is serving, so a bad release is harder to detect and reverse.
What to do
Add a `healthcheck:` to the served compose service — probing the endpoint it already answers on where it has one — with `depends_on: condition: service_healthy` on whatever waits for it, and keep the deployed image tag immutable and recorded so rolling back is re-pointing at the previous tag rather than rebuilding.
Add an approval/environment gate (required reviewers / protection rules) before production promotion.
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P5 · DR & Backup0.0 / 10Critical✓ Tool-verified
Readiness · Readiness — Whether disaster recovery is planned and codified — backups, geo-recovery, RTO/RPO, persistence guarantees — from IaC + container manifests + docs, never the live cloud.
Method: Filesystem scan: disaster recovery, backup, geo-recovery, RTO/RPO, persistence guarantees from IaC, manifests, and docs. Exhaustive, deterministic, never a live environment.
A persistence guard (data volume / purge-protection) was found, but no backup, geo-recovery or RTO/RPO controls were evidenced — a volume that survives a container recreate is not a tested restore from catastrophic loss.
What to do
Codify backups + geo-recovery in IaC and document RTO/RPO and the restore procedure — a persistence guard alone is not disaster recovery.
Readiness · Readiness — Whether releases are traceable — a maintained changelog and explicit version stamping.
Method: Filesystem scan: changelog file presence and version tags in csproj or git tags. Exhaustive, deterministic.
No CHANGELOG/HISTORY/RELEASES file — what shipped when isn't easy to reconstruct for support or audit. (Versioning/tagging makes releases traceable, but a changelog records the what.)
What to do
Keep a changelog (e.g. Keep-a-Changelog) recording what shipped in each release.
Readiness · Readiness — Whether outbound HTTP calls are wrapped in resilience (retry/timeout/circuit-breaker) so a failing dependency doesn't cascade.
Method: Source scan: outbound HTTP clients and what bounds them — resilience handlers (Polly, AddStandardResilienceHandler) on .NET; on Go, the JVM, Python, JavaScript/TypeScript, Ruby, PHP, Rust, Elixir, Swift, Dart and Erlang, a timeout, deadline, retry or breaker beside each call, or a process-wide client default (a framework-wide deadline such as Drupal core's, Laravel's or actix's awc counts). Exhaustive, deterministic.
Other · Code Health — Whether the code avoids sync-over-async (deadlock-prone blocking on tasks) and async void.
Method: Roslyn syntax scan: async methods scanned for .Wait()/.GetAwaiter().GetResult() and async-void outside event handlers. Deterministic, hard fact per invocation.
Other · Code Health — Whether any branch is dead by construction — a switch arm whose label can never equal a case-normalised subject, or an `else if` whose predicate the arm above has already swallowed.
Method: Roslyn syntax + semantics: switch labels compared against the subject's own case normaliser, and if/else-if chains checked for a literal an earlier arm's containment test already swallows. Deterministic, provable per finding. Advisory.
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X13 · Undrained process stream10.0 / 10Exemplary○ Nothing flagged
Other · Code Health — Whether a child process that has BOTH standard streams redirected drains both — reading one to the end while the other is never read deadlocks once the child fills the unread pipe.
Method: Roslyn syntax + semantics: ProcessStartInfo launches with both streams redirected, checked for a drain of each stream across the enclosing type. On a repository with no .NET source it reads JavaScript/TypeScript off the engine’s own token stream (test, vendored and minified paths not) with the same rule: a `spawn` imported from `child_process` whose stdout and stderr are both pipes (no options, no `stdio`, or `stdio` of `'pipe'`), bound to a local that never leaves its scope, where exactly one of the two streams is read, the other never, and the child’s `close`/`exit` (or the read stream’s end) is awaited; a shell redirect in the call’s arguments or a `kill` of the child suppresses it. The same rule reads Python’s `subprocess.Popen`, Dart’s `Process.start`, Ruby’s `Open3.popen3`, and Scala’s JDK process starts (`new ProcessBuilder(…).start()` with no `redirectErrorStream`/`redirectError`/`redirectOutput`/`inheritIO`, `Runtime.getRuntime.exec`) where one of `getInputStream`/`getErrorStream` is read and the other never while `waitFor()` is called or the read stream is read to its end. Deterministic, provable per finding. Advisory.
Other · Security — Whether a hand-rolled public/private IP check can be walked past — a method that unwraps IPv4-mapped IPv6 but returns the opposite verdict for the same host written as IPv4-compatible, 6to4 or NAT64.
Method: Roslyn syntax + semantics: methods that unwrap IPv4-mapped IPv6 and hand-roll IPv4 range carve-outs, checked for whether the IPv6 branch also accounts for the IPv4-compatible, 6to4 and NAT64 embeddings. Deterministic, provable per finding. Advisory.
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X15 · Unvalidated length from an untrusted reader10.0 / 10Exemplary○ Nothing flagged
Other · Security — Whether a length read out of the stream being parsed is bounded before it is allocated or read — an unchecked count taken from the input lets the input choose the allocation.
Method: Roslyn syntax + semantics: integer lengths read from a BinaryReader and spent on a bulk read or an array allocation, checked for any comparison or bounding call on the value anywhere in the method. Deterministic, provable per finding. Advisory.
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X17 · Uncapped recursion over a caller-supplied document3.5 / 10Weak✓ Tool-verified
Other · Security — Whether a walk that recurses through a JSON/XML tree handed in by its caller bounds how deep it will go — an uncapped walk lets the document's nesting choose the stack depth, and the resulting StackOverflowException cannot be caught.
Method: Roslyn syntax + semantics: methods that take a JSON/XML document node and call themselves with a child of it, reachable from an externally-callable member of the same type that accepts a document, checked for any depth parameter, descent counter or threaded arithmetic anywhere in the walk. On a repository with no .NET source it reads TypeScript off the engine’s own token stream (test, vendored, generated and minified paths and `.d.ts` not) with the same rule: a function or method with a parameter typed as a document value — `unknown`, `any`, `object`, `Record<string, unknown|any>`, a JSON alias (`JsonValue`, `JSONObject`, …) or a DOM node global the file does not rebind — that calls itself (bare, or through `this` for a method, or hands itself to a call over the value as in `value.map(walk)`) with something it took out of that value and never through an ancestor accessor such as `closest()` or `parentElement`, that is exported or reached from an exported function (a public method of the same exported class) taking such a value, and that names no depth, level, nesting, recursion, remaining or budget anywhere and threads no `+`/`-` arithmetic through a self-call. Plain JavaScript is not read: with no annotation nothing tells a parsed document from a tree the code built itself. Deterministic, provable per finding. Advisory.
`to_native` is public and takes `obj: Any` from its caller, and it calls itself at line 25 (`to_native`) with a piece of `obj` (and at line 27, line 29, line 31) — and nowhere in `to_native` is there a depth parameter, a descent counter, or any other cap. The walk therefore spends one stack frame per LEVEL of the value it is handed, so the nesting of the caller's document — not this code — decides how deep the stack goes. Python's parsers accept far more nesting than its stack allows: `json.loads` turns 3,000 bytes of brackets into 1,500 levels and `xml.etree.ElementTree` parses tens of thousands, while a walk like this one raises `RecursionError` once it passes the interpreter's default limit of 1,000 frames. Wherever that exception lands, the work fails: a request errors, and outside any `try` the worker or the program exits with it. Raising `sys.setrecursionlimit` only moves the number the document has to beat. Thread a depth argument through `to_native` and reject the value past a fixed maximum, or rewrite the descent with an explicit stack. — deepface/api/src/modules/core/service.py:25
What to do
Each finding names the externally-callable member that accepts the document and the self-call that descends, and states that no depth parameter or counter exists anywhere in the walk. Confirm from the two, then thread a depth argument through the walk and reject documents past a fixed maximum, or rewrite it with an explicit stack.
Other · Code Health — Whether a method that temporarily changes state belonging to the whole process — the working directory, an environment variable — puts it back on EVERY path: a restore reached only when nothing throws leaks the change to the rest of the process.
Method: Roslyn syntax + semantics: method bodies that write the process working directory or an environment variable and write it back in the same body, checked for whether that restore sits in a `finally`/`catch` or only on the straight-line path. On a repository with no .NET source the same rule reads production JavaScript/TypeScript off the engine’s own token stream (test, vendored and minified paths not): `process.chdir`, `process.env.NAME =`/`["NAME"] =` and `delete process.env.NAME`, and Deno’s `Deno.chdir`/`Deno.env.set`/`Deno.env.delete`, paired per function body (a nested function or arrow is its own body, and module top-level code is none), where the last write puts back a local the body captured from the same global or deletes a variable the first write set, with at least one statement between them; a write in a `catch`/`finally` of that body silences it. Deterministic, provable per finding. Advisory.
Other · Code Health — Whether a `when` guard is free of side effects — a guard that increments a counter or assigns while deciding whether its arm matches applies that change during PATTERN MATCHING, on an arm that may not be selected, and skips it entirely when a short-circuit to its left answers first.
Method: Roslyn syntax: `when` guards on case labels and switch-expression arms, read for a mutation (`++`/`--`/assignment) sitting in a position the guard's own `&&`/`||`/`??`/`?:`/`?.` can skip. Deterministic, provable per finding. Advisory.
Other · Code Health — Whether a method that TAKES a lock or semaphore and gives it back from a flag-guarded `finally` returns the value that flag implies — reporting success while the guard hands the primitive back admits a second caller the exclusion was there to keep out, and reporting failure while the guard keeps it leaves nothing to ever give it back.
Method: Roslyn syntax: `try` statements whose `finally` releases a synchronisation primitive under a bare local-bool guard, where the method also TOOK that same primitive before the `try`, checked for a `return` of a bool literal whose value disagrees with the flag state the method's own straight-line assignments put it in. Deterministic, provable per finding. Advisory.
Other · Code Health — Whether the work a diagnostic log line costs is paid only when that line is wanted — C# evaluates a call's arguments BEFORE the call, so a trace/debug message joined or projected out of a collection is built in full on every pass, and then discarded by a sink the shipped configuration leaves switched off.
Method: Roslyn syntax: log calls at a diagnostic level (a `Log`-prefixed method naming Trace/Debug/Verbose, or a bare `Debug`/`Trace`/`Verbose` on a receiver named for a logger), whose argument list is read for a call whose cost scales with a sequence — a LINQ operator, a materialisation, `string.Join`, a serializer — with no enclosing level check or conditional-compilation region. On a repository with no .NET source it reads JavaScript/TypeScript off the engine’s own token stream (test, vendored and minified paths not) with the same rule: a `debug`/`trace`/`verbose` call on a receiver named for a logger, or a `log`-prefixed method naming the level, whose argument calls an array operator (`map`/`filter`/`reduce`/`sort`/…), `Array.from`, `Object.keys/values/entries`, `JSON.stringify`/`util.inspect` over anything but a literal, or an array `join` — outside any arrow or function passed as an argument, which the logger calls only when the level is on — with no enclosing `if`, `&&` or `?:` whose condition names a level, a level string, or the `NODE_ENV`/`__DEV__`/`DEV` build switch. Deterministic, provable per finding. Advisory.
Do you agree with this assessment?
X24 · Document value interpolated into markup unescaped10.0 / 10Exemplary○ Nothing flagged
Other · Security — Whether text read out of the document being converted is escaped before it is written into generated markup — a value the document's author chose, interpolated into an attribute the surrounding literal delimits, can close that attribute and open another.
Method: Roslyn semantic model over the whole compilation: a string-typed `Value`/`InnerText`/`InnerXml`/`Text` member declared inside `DocumentFormat.OpenXml` or `System.Xml` is a taint SOURCE, propagated through assignments, returns, arguments, tuple elements and string composition to its transitive closure, then read at interpolated-string holes that sit in a markup position the surrounding literal itself delimits. Escaper/encoder calls and enclosing validator conditions cut the flow. Flow- and container-insensitive by construction. A second arm needs no provenance at all and reports a type that CONTRADICTS ITSELF — the same expression escaped at one delimited markup hole and interpolated raw at another hole in the same markup position of the same type, which the type's own escaping proves is a defect without knowing where the value came from. On a repository with no .NET source it reads JavaScript/TypeScript off the token stream with the same rule: a DOM read of raw document text (`getAttribute`, `textContent`, `innerText`, `nodeValue`) is the source, propagated through local bindings and string composition, and judged at template-literal and concatenation holes in the same two delimited markup positions; escapers and validating conditions cut it, and documentation-site, test, vendored and minified scripts are not read. Deterministic, provable per finding. Advisory.
Other · Code Health — Whether a value the caller is invited to supply is the value the type actually uses — a constructor parameter stored in a private field that nothing ever reads while the default it was given is spelled out a second time at the site that should have read it, a keyed lookup that falls back to a different setting than the one its key names while the same type falls back to the matching one for that same key, or a culture-sensitive parse given no format provider by a type that feeds its own settable culture to the same kind of parse elsewhere, or a number an entry point writes to standard output under the host's culture while the same entry point formats with the invariant culture elsewhere. Either way, every caller who supplies a value silently gets something else.
Method: Roslyn syntax: private instance fields of a non-partial type assigned in a constructor from one of its own parameters with a `??` fallback, checked for whether anything in the type body reads the field and whether that same fallback expression is spelled out again outside the constructor; and `??` fallbacks onto a member access from a lookup call carrying exactly one string literal, grouped by that key across the type and checked for a fallback member whose folded name disagrees with the key while a sibling site for the same key agrees with it. On a repository with no .NET source the first two arms read JavaScript/TypeScript off the engine’s own token stream (tests included, bundles and vendored paths not): a `#x`, `private` or `private` parameter-property instance field filled in the constructor from a parameter (or one member of one) through `??`/`||` or a parameter default, never read anywhere in the file by name, whose constructed default is spelled again in the class body; and `lookup("key") ?? s.member` grouped by key per class, or per module outside every class. The culture arm has no JavaScript counterpart: its parses take no locale. Deterministic, provable per finding. Advisory.
Other · Code Health — Whether a value handed from a callback to the body that waits on it crosses on something built to be crossed — a `Queue<T>`/`List<T>`/`Dictionary<K,V>` written inside an event handler and read back outside it is mutated by two flows at once, and the semaphore or completion source beside it orders how MANY items exist while leaving the collection's own head, tail and backing array unprotected.
Method: Roslyn syntax: method, accessor, local-function and lambda bodies that declare BOTH a non-thread-safe generic collection (`Queue`/`Stack`/`List`/`Dictionary`/`HashSet`/`Sorted*`/`LinkedList`) and a synchronisation primitive (`SemaphoreSlim`/`TaskCompletionSource`/`ManualResetEvent(Slim)`/`AutoResetEvent`/`CountdownEvent`) as locals, then read for a `+=`-registered lambda that raises that primitive while the body outside every lambda waits on it — and, in that scope, a mutating call on the collection inside the lambda paired with a mention of it outside. Any `lock` in the scope abstains it. On a repository with no .NET source the same handoff is read in Java off the engine’s own token stream (test source sets, vendored and demonstration paths not): an `ArrayList`/`LinkedList`/`ArrayDeque`/`PriorityQueue`/`Hash*`/`LinkedHash*`/`Tree*` local and a `CountDownLatch`/`Semaphore`/`CompletableFuture` local, a lambda or anonymous class that raises the primitive and mutates the collection, and a wait outside it; any `synchronized` or `lock()` abstains the body. Because each of those primitives orders what the callback wrote before raising it, only a touch that provably overlaps the callback is convicted: one after the registration and before the next wait, or one in a loop registered-before, waiting on every pass and not declaring the collection. Deterministic, provable per finding. Advisory.
Do you agree with this assessment?
X27 · Collection changed while being enumerated10.0 / 10Exemplary○ Nothing flagged
Other · Code Health — Whether a `for` loop leaves the `dict`, `set` or `deque` it is walking alone — CPython's iterators over those check that the collection has not changed size since the loop began, so a body that removes from (or empties) the very collection the loop is walking makes the next step raise `RuntimeError: dictionary changed size during iteration` and the remaining items are never seen.
Method: Roslyn syntax + semantics: `foreach` statements whose body calls a structural mutator (`Add`/`Remove`/`Clear`/`Insert`/…) on the very expression the loop is enumerating. Two arms. ARM A — the source is a concrete fragile BCL collection, or a live `Keys`/`Values` view over one, and the mutator resolves to that same collection's own member; concurrent and immutable collections and arrays are outside the population by construction, since their enumerators survive a structural change. ARM B — the source is an argument-less accessor CALL on a receiver whose body is in source: the accessor must return a stored field VERBATIM and a sibling member must structurally change that same field, both read off the implementations rather than from the members' names. A mutation the loop provably exits immediately after (`break`/`return`/`throw`/`goto`), or one written inside a nested loop or a lambda, is counted and never reported. Deterministic, provable per finding. Advisory.
Do you agree with this assessment?
X28 · Index access outside its own emptiness guard10.0 / 10Exemplary○ Nothing flagged
Other · Code Health — Whether a condition that tests a value for emptiness indexes that same value only where the test holds — an `||` written one parenthesis too far to the left leaves an index access outside the guard beside it, so the empty case the guard exists to anticipate reaches the index and throws.
Method: Roslyn syntax only, no semantic model: the OUTERMOST `&&`/`||` of every boolean condition, read for a symbol the condition tests for emptiness (`string.IsNullOrEmpty`/`IsNullOrWhiteSpace`, a `Length`/`Count` comparison against a literal, `Any()`, a `Length`/`Count` pattern, or a comparison against `""`) and ALSO indexes. Each `symbol[...]` access is placed by a boolean-reachability walk from the access up to the outermost connective: an access is COVERED when some enclosing step has it in the right operand and the left operand, under the truth value that step forces, proves the symbol non-empty — a recursion over `&&`/`||` whose true- and false-directions are asymmetric. A finding needs BOTH an uncovered access and a covered one on the same symbol in the same condition, which is the agreeing twin that separates a misplaced parenthesis from an unrelated length test. Bare index accesses with no emptiness test in the condition are neither counted nor reported; a non-identifier receiver and a lambda nested inside the condition are outside the population. Deterministic, provable per finding. Advisory.
Other · Code Health — Whether exceptions are handled rather than silently swallowed or rethrown with lost stack traces.
Method: Roslyn syntax scan: every catch clause counted; empty catches and bare rethrows flagged. Population is all catch clauses, not estimated. Deterministic, hard fact.
An empty catch block silently discards the error — failures vanish with no log and no rethrow. Log it, handle it, or don't catch it. — deepface/models/face_detection/OpenCv.py:64
What to do
Swallowed exception (empty catch)
Do you agree with this assessment?
X30 · Support guard that admits what it rejects10.0 / 10Exemplary○ Nothing flagged
Other · Code Health — Whether a guard written as a NEGATED `||` says what its author meant — `!(a || b || x != k)` is `!a && !b && x == k` by De Morgan, so a bail-out that mixes capabilities the code needs with a fault it refuses turns inside out: it fires only where the capabilities are ABSENT, and lets every value the fault term names walk straight into the body that cannot handle it.
Method: Roslyn syntax only, no semantic model: every logical-not whose operand is a parenthesised `||` chain of two or more disjuncts, flattened (a left-nested `a || b || c` read once would see `(a || b)` as one disjunct). A site enters the population on that shape alone. A finding additionally needs the disjuncts to DISAGREE in polarity: at least one bare boolean read — an identifier or member access, never an invocation, which is a predicate rather than a capability flag — and at least one `x != <constant>`, the only form that negates into an exact-value pin (`== null` negates into a looser requirement and is outside the fault set). Consistently-polarised disjunctions, all-fault or all-capability, are counted and never reported; a negated `&&` is outside the population entirely. No same-receiver gate: it was measured to cost a real defect and remove no false positive. Deterministic, provable per finding. Advisory.
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. On a repository with no .NET source it reads JavaScript/TypeScript off the engine’s own token stream (test, vendored and minified paths not) with the same rule: a `trace`/`debug`/`info`/`warn`/`error`/`fatal`/`verbose`/`silly`/`http`/`log` call on a receiver named for a logger, in a package whose own or an enclosing `package.json` declares a logger that carries values as fields (pino, pino-http, nestjs-pino, Fastify, winston, bunyan, tslog, LogTape, roarr), whose first deciding argument is a template literal with substitutions; an object-literal fields argument is stepped over, a plain string message clears the call. Python is read the same way: a `debug`/`info`/`warning`/`error`/`exception`/`critical`/`log` call on a logger in a file that imports the standard library’s `logging` or structlog (loguru, which formats at the call, is not judged), whose message is a whole f-string with a non-constant field; a `%`-formatted, `.format()`-ed or concatenated message is not convicted, as in C#. PHP is read the same way: a PSR-3 level call (`emergency`…`debug`, `log`) on a logger (`$logger`, `$this->logger`, Laravel’s `Log::`, `logger()`, `\Drupal::logger()`) in a package whose `composer.json` requires a PSR-3 logger or a framework that hands one out (psr/log, Monolog, Laravel, Symfony, Drupal, CakePHP), or a file that imports one, whose message is a double-quoted string interpolating a variable; a single-quoted message clears the call, and `sprintf` or `.` concatenation is not convicted, as in C#. Dart is read the same way: a `trace`/`debug`/`info`/`warn`/`error`/`fatal` call on Sentry’s structured logger (`Sentry.logger`) whose message is a whole string literal interpolating a value (`$name`, `${…}`); the `Sentry.logger.fmt` template API clears the call. Dart that logs only through loggers with one message and no fields (package:logging, package:logger, talker, dart:developer) is not applicable rather than a reach gap. Ruby is read the same way: a `trace`/`debug`/`info`/`warn`/`error`/`fatal` call on the console gem’s `Console` (declared by a Gemfile, gemspec or Gemfile.lock, or required) or, where SemanticLogger, Ougai or dry-logger is declared or required, on a receiver named for a logger (`logger`, `@logger`, `Rails.logger`, `log`), whose first deciding argument is, as the whole argument, a double-quoted string interpolating a non-constant value (`#{…}`, `#@ivar`); a plain literal clears the call, keyword arguments are the fields, and a block, `%`-format or `+` concatenation is not convicted. Ruby that logs only through the standard Logger (Rails.logger) is not applicable rather than a reach gap. Deterministic.
`debug` at line 57 is handed an f-string as its message, so the logger receives one finished string — the values in it cannot be queried, filtered or aggregated as fields, every occurrence reads as a different message, and the string is built even when the record is filtered out. Pass the values beside a constant message instead: `logger.debug("order %s moved to %s", order_id, status)` with the standard library's `logging`, which keeps the message and its arguments apart on the record, or `log.debug("order moved", order_id=order_id, status=status)` with structlog. — deepface/commons/package_utils.py:57
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.
Unscored — 3 check(s) recorded observations but carry no score
These checks ran and found something, but they do not carry a score — either by design (an advisory check reports evidence rather than grading it) or because they could not be scored here. They are excluded from the score for that reason, not because there was nothing to see.
D36 Supply-chain Provenance & Signing — 1 observation(s) recorded · The CI pipeline builds and tests but publishes no released artifact — no package publish, container push, GitHub release or deployment step. Supply-chain provenance, signing and SBOM attest RELEASED artifacts, so there is nothing to attest here. Add them to the release pipeline when this repo starts shipping artifacts (a published package, a container image, a deployed service or a tagged release). Build integrity and workflow-token hygiene are reported below: they describe what the CI runs and the token it runs with, neither of which is affected by whether the pipeline ships an artifact.
X10 Duplicated predicate — 4 observation(s) recorded · Advisory — this card reports evidence and never carries a score.
X7 Silent fallback defaults — 1 observation(s) recorded · Advisory — this card reports evidence and never carries a score.
Not included — 59 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 — Not applicable: this repository's Python imports no dependency-injection container and defines no container of its own — nothing that both registers and resolves bindings, and nothing that names two lifetimes — so nothing holds one lifetime's instance while handing out another's.
AX5 Architecture & structure — not assessed — architecture style/structure is computed from a project graph (projects, types, module namespaces) that was not loaded for this repository, because the repository is written in a language this check does not yet model, or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository
AX6 Interface segregation — not assessed — interface segregation is computed over a type surface that was not loaded for this repository, because the repository is written in a language this check does not yet model, or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository
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 — Advisory — this card reports evidence and never carries a score, so there is nothing missing here.
AXR1 Runtime accessibility — compose up failed (exit 1 — a container did not start, exited, or never became healthy) — services.pgvector: container name "postgres" is already in use by service {}"; runtime evidence skipped This is a statement about this run, not a statement about your application: nothing here says the surface is inaccessible, only that it was never rendered.
C1 Data Protection — Not assessed: these personal data controls are read from a source model (declarative annotations, request middleware, entity/column names, guard methods) that was not loaded for this repository — because the repository is written in a language this check does not yet model, or because its projects failed to load. Absence of an idiom this check recognises is NOT evidence that this repository lacks personal data controls: it may implement them entirely in its own ecosystem. This is a gap in the analyzer's language coverage, not a finding about this repository.
C3 Audit Trail — No personal data detected in a persisted data model — no PII-named field (email, first_name, date_of_birth, phone, …) or stored credential in a Django, SQLAlchemy, SQLModel, Peewee, Tortoise or MongoEngine model, a migration or a SQL schema — and no database or data-store client (Django ORM, SQLAlchemy, psycopg, PyMongo, …) in the source either, so this repository keeps no sensitive data whose changes an audit trail would record. If it does (through a store configured outside this repository, for instance), the trail belongs to wherever that data is stored.
C4 Data Retention — No personal data detected in a persisted data model — no PII-named field (email, first_name, date_of_birth, phone, …) or stored credential on a Django, SQLAlchemy, SQLModel, Peewee, Tortoise or mongoengine model, in a migration or in a SQL schema — and no database or data-store client (django.db, sqlalchemy, psycopg2, pymongo, sqlite3, …) imported either, so this repository keeps no personal data whose lifetime a retention limit would bound.
C5 Data-Subject Rights — No personal data detected in a persisted data model — no PII-named field (email, first_name, date_of_birth, phone, …) or stored credential on a Django, SQLAlchemy, SQLModel, Peewee, Tortoise or mongoengine model, in a migration or in a SQL schema — and no database or data-store client (django.db, sqlalchemy, psycopg2, pymongo, sqlite3, …) imported either, so this repository keeps no personal data for a data subject to erase, export or consent over.
D10 Test Quality — ~6066 lines of test source are present (.py) but no test cases reached the test census for this repository — no test root we could resolve declared them, or the files we read declare no cases in a test framework we recognise — so skipped/assertion-free tests couldn't be counted. Not scored — this is a gap in the analyzer, not a finding about this repository.
D11 Test Reliability — Test reliability not included — the .py suite was found but not re-run
D12 Dependency Hygiene — Not scored — 30 shipped Python distribution(s) were read, but the outdated signal needs pypi.org, and no declaration here carries an exact pin to ask about — a floor or a range installs the newest release it admits and cannot be behind one, so this dimension's own question is only partly answered. NOT a finding that these dependencies are current or healthy.
D18 Solution Shape — D18 scores the shape of a .NET solution; this repository has no .NET solution or project files, so the dimension does not apply.
D22 Internal API Consistency — The exposed public-API surface could not be collected — no C#/VB projects loaded.
D23 Boundary Type-Coupling — No bounded-context organisation was detected either — neither a context-shaped layout nor 2+ sibling source directories each declaring an aggregate root. Declaring this codebase's bounded contexts (≥2) would let cross-boundary type coupling be assessed. Declare them in `.codehealth/config.yaml` at the repository root (create it if absent), mapping each context name to the module-path or namespace prefixes that belong to it — e.g. `architecture:` → `contexts:` → `Billing: ["src/billing", "Acme.Billing"]`, `Catalog: ["src/catalog", "Acme.Catalog"]`.
D24 Comment Value — No inline comments to assess — comment value is not applicable here.
D25 ADR Conformance — no ADRs to check
D27 Navigability — D27 counts a call into the standard library or a dependency as unresolved for this repository's language (.py), so too few call sites counted as resolved to assess navigability. Not scored: this is a gap in the analyzer, not a verdict about this repository.
D32 Data Compliance (PII/GDPR) — No personal data was found crossing a boundary the PII/GDPR ruleset checks — nothing written to a log or console sink, placed in a URL or query string, or persisted to browser storage. That is a clean result for the LEAK surface only: this ruleset detects personal data escaping, it does not inventory the personal data a repository holds, so it is not evidence that this repository has no personal-data surface. The personal-data map (Appendix C) and the C1-C5 compliance cards are what speak to that.
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.
D39 IL Efficiency — D39 measures the IL emitted by a .NET build; this repository has no .NET solution or project files, so the dimension does not apply.
D40 Network Egress Confinement — No Kubernetes/orchestration workloads found in the repository manifests; network egress policy is a cluster-native control that may live at the platform/firewall layer, so there is nothing to assess here.
D41 Kernel & Syscall Confinement — No Kubernetes/orchestration workloads found in the repository manifests; seccomp/AppArmor/SELinux confinement is a workload-level control, so there is nothing to assess here.
D42 Runtime Threat Enforcement — No Kubernetes/orchestration workloads found in the repository manifests; runtime threat-detection and admission-control policy are cluster-level controls, so there is nothing to assess here.
D5 Coupling — Not applicable — this Python build ships 1 production module(s), so there is no coupling BETWEEN modules to measure. (Its test and non-production modules are not part of the shipped graph.)
D7 Architectural Integrity — no checkable ADRs, and no project-reference graph for the cycle pass to read — so this dimension makes no claim about dependency cycles in either direction (where this repository's language has an import-cycle lens, cycles are reported there). Architectural integrity not assessed
D8 Code Coverage — Coverage not included — suite not readable by the collector
DM1 Domain Modelling — not scored — this repository shows only 1 of the 3 signals this lens looks for (2 value object(s))
ED1 Event-Driven — not scored — this repository shows none of the 3 signals this lens looks for
ED5 Idempotency — no mutating command handlers or message consumers detected — idempotency check not applicable
ES1 Event Sourcing — not scored — this repository shows none of the 3 signals this lens looks for
GD1 Unfinished & placeholder code — no source files were read — this check reads C#, F#, JavaScript/TypeScript, Dart, Ruby and Scala, and none was read for this repository's product. That is a limit of the analyzer, not a finding about your code.
IC1 Incompleteness & stubs — not analysed — these correctness checks read a source model that was not loaded for this repository, because the repository is written in a language this check does not yet model, or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository
P12 CI test-gate honesty — Reported, not scored — and nothing was matched here. The coverage check applies to any stack, and the automatic-re-run check to any GitHub-Actions workflow, but the checks for excluded tests, skipped tests and sleep-based synchronisation currently recognise only some ecosystems' test-runner idioms, so on a repository built with another stack the zeros below mean 'not checked', not 'clean'.
P8 Schema migrations — no ORM, schema-migration tool or schema auto-create was found in this repository's dependency manifests or source, so there is no database schema for this check to judge
P9 Domain vs controller coverage — no coverage report found on disk — produce a coverage report in a standard format (`coverage run -m pytest` then `coverage xml`) and commit it — a hosted scan measures a clone of the repository, so a report that exists only in a working tree, a CI runner's or your own, never reaches it; the artefact is commonly gitignored, so `git add -f` that one file (or un-ignore its path) and commit it alongside the code it measures, or wire coverage collection into CI, to enable this cross-layer check
PF1 Benchmark discipline — Not applicable: no benchmark suite was found. This check searched for a pytest-benchmark or pytest-codspeed dependency with tests taking the `benchmark` fixture, an asv.conf.json with its `time_`/`mem_`/`peakmem_` benchmarks, or a pyperf Runner, and for a `*benchmark*` script that this repository's CI runs. Benchmarks are credited as a bonus, so their absence is neither scored nor deducted.
PF2 Allocation hygiene — Not applicable: Python runs on a garbage-collected runtime that gives a program no allocation-control idiom to choose on a hot path — no pools, stack allocation or value types — so allocation awareness is not something this code can be rated on.
PF3 Async & latency hygiene — Not applicable: this repository declares no async functions, so there is no asynchronous code for a blocking call to stall.
S1 Web-Security Posture — Not assessed: these web-security controls are read from a source model (declarative annotations, request middleware, entity/column names, guard methods) that was not loaded for this repository — because the repository is written in a language this check does not yet model, or because its projects failed to load. Absence of an idiom this check recognises is NOT evidence that this repository lacks web-security controls: it may implement them entirely in its own ecosystem. This is a gap in the analyzer's language coverage, not a finding about this repository.
X16 Unfloored truncation loop — not analysed — these correctness checks read a source model that was not loaded for this repository, because the repository is written in a language this check does not yet model, or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository
X18 Disposal-pattern correctness — not analysed — these correctness checks read a source model that was not loaded for this repository, because the repository is written in a language this check does not yet model, or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository
X2 Cancellation propagation — no C# was loaded; this repository's Python was read, and no request in it can be cancelled by a caller — a Python request takes no token: asyncio cancels the task and trio or anyio the scope that made it, from outside the call, and requests, httpx, aiohttp and gRPC take a timeout, not a cancellation
X20 Mistyped argument guard — This check asks whether an argument guard reports an EMPTY value as a NULL one, which needs a language that throws a null-specific argument exception — .NET's ArgumentNullException, the JVM's NullPointerException, Dart's ArgumentError.notNull. This repository contains none of those languages: the ones it is written in have a single exception for a bad argument, so there is no pair of exceptions to confuse and nothing here for it to assess. Not a gap in the analyzer and not a finding about your code.
X29 Per-element action decided by a fixed element — not analysed — these correctness checks read a source model that was not loaded for this repository, because the repository is written in a language this check does not yet model, or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository
X32 Type resolved by simple name across every loaded assembly — This check is about how a .NET program searches the assemblies loaded into its process for a type, and this repository contains no .NET source, so there is nothing here for it to assess. Not a gap in the analyzer and not a finding about your code.
X5 Nullable reference types — not analysed — these correctness checks read a source model that was not loaded for this repository, because the repository is written in a language this check does not yet model, or because its projects failed to load. This is a gap in the analyzer, not a finding about this repository
X6 Hand-rolled structured-format parsing — Advisory — this card reports evidence and never carries a score, so there is nothing missing here.
X9 Subsumed condition operand — Advisory — this card reports evidence and never carries a score, so there is nothing missing here.
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.
Banned license: ultralytics — `ultralytics` 8.4.183 is published on PyPI under `AGPL-3.0`, which this policy bans, and it is in the SHIPPED closure of this repository — so it is installed by everything that depends on this repository, not just by its test environment. A distribution offering SEVERAL licences is a choice and is only charged when every one of them is banned. Replace the package, or record an accepted exception.
Banned license: ultralytics-thop — `ultralytics-thop` 2.2.3 is published on PyPI under `AGPL-3.0`, which this policy bans, and it is in the SHIPPED closure of this repository — so it is installed by everything that depends on this repository, not just by its test environment. A distribution offering SEVERAL licences is a choice and is only charged when every one of them is banned. Replace the package, or record an accepted exception.
Banned license: ultralytics-platform — `ultralytics-platform` 0.1.90 is published on PyPI under `AGPL-3.0-only`, which this policy bans, and it is in the SHIPPED closure of this repository — so it is installed by everything that depends on this repository, not just by its test environment. A distribution offering SEVERAL licences is a choice and is only charged when every one of them is banned. Replace the package, or record an accepted exception.
FileTooLong: tf/Facenet.py deepface/models/facial_recognition/tf/Facenet.py— FileTooLong — 1582 significant lines (blank, comment-only and punctuation-only lines excluded). The bar is 500 significant lines; this is 1082 over it, 3.16× the bar. To reduce it, split the file along the responsibilities already in it: move each cohesive group of declarations into its own sibling file in the same module or package, so no one file has to be read whole to change one of them.
FileTooLong: database/weaviate.py deepface/modules/database/weaviate.py— FileTooLong — 729 significant lines (blank, comment-only and punctuation-only lines excluded). The bar is 500 significant lines; this is 229 over it, 1.46× the bar. To reduce it, split the file along the responsibilities already in it: move each cohesive group of declarations into its own sibling file in the same module or package, so no one file has to be read whole to change one of them.
FileTooLong: modules/datastore.py deepface/modules/datastore.py— FileTooLong — 711 significant lines (blank, comment-only and punctuation-only lines excluded). The bar is 500 significant lines; this is 211 over it, 1.42× the bar. To reduce it, split the file along the responsibilities already in it: move each cohesive group of declarations into its own sibling file in the same module or package, so no one file has to be read whole to change one of them.
FileTooLong: modules/streaming.py deepface/modules/streaming.py— FileTooLong — 658 significant lines (blank, comment-only and punctuation-only lines excluded). The bar is 500 significant lines; this is 158 over it, 1.32× the bar. To reduce it, split the file along the responsibilities already in it: move each cohesive group of declarations into its own sibling file in the same module or package, so no one file has to be read whole to change one of them.
FileTooLong: modules/recognition.py deepface/modules/recognition.py— FileTooLong — 522 significant lines (blank, comment-only and punctuation-only lines excluded). The bar is 500 significant lines; this is 22 over it, 1.04× the bar. To reduce it, split the file along the responsibilities already in it: move each cohesive group of declarations into its own sibling file in the same module or package, so no one file has to be read whole to change one of them.
Hotspot: deepface/modules/recognition.py deepface/modules/recognition.py:41— deepface/modules/recognition.py changed 9 times in last 90 days, and the most complex body those changes touched has cyclomatic complexity 42 in recognition.find at line 41. 3 of those changes were fix/bug commits, and the other 6 changed it for other reasons — this file is under both repair and feature pressure. Before the next change lands here, make sure the area it touches is under test, then split that area out of the file so the following change is smaller than this one — a file this often edited pays the complexity back every time. Counted over 2026-07-21..2026-10-19, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-21 21:37:28 +01:00' --until='2026-10-19 21:37:28 +01:00' --full-history --no-merges -- deepface/modules/recognition.py`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Hotspot: deepface/modules/datastore.py deepface/modules/datastore.py:232— deepface/modules/datastore.py changed 8 times in last 90 days, and the most complex body those changes touched has cyclomatic complexity 43 in datastore.search at line 232. 3 of those changes were fix/bug commits, and the other 5 changed it for other reasons — this file is under both repair and feature pressure. Before the next change lands here, make sure the area it touches is under test, then split that area out of the file so the following change is smaller than this one — a file this often edited pays the complexity back every time. Counted over 2026-07-21..2026-10-19, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-21 21:37:28 +01:00' --until='2026-10-19 21:37:28 +01:00' --full-history --no-merges -- deepface/modules/datastore.py`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Hotspot: deepface/modules/database/weaviate.py deepface/modules/database/weaviate.py:619— deepface/modules/database/weaviate.py changed 4 times in last 90 days, and the most complex body those changes touched has cyclomatic complexity 26 in weaviate.resolve_connection_details at line 619. 1 of those changes was a fix/bug commit, and the other 3 changed it for other reasons — this file is under both repair and feature pressure. Before the next change lands here, make sure the area it touches is under test, then split that area out of the file so the following change is smaller than this one — a file this often edited pays the complexity back every time. Counted over 2026-07-21..2026-10-19, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-21 21:37:28 +01:00' --until='2026-10-19 21:37:28 +01:00' --full-history --no-merges -- deepface/modules/database/weaviate.py`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Hotspot: deepface/modules/demography.py deepface/modules/demography.py:20— deepface/modules/demography.py changed 2 times in last 90 days, and the most complex body those changes touched has cyclomatic complexity 24 in demography.analyze at line 20. 1 of those changes was a fix/bug commit, and the other 1 changed it for other reasons — this file is under both repair and feature pressure. Before the next change lands here, make sure the area it touches is under test, then split that area out of the file so the following change is smaller than this one — a file this often edited pays the complexity back every time. Counted over 2026-07-21..2026-10-19, the 90 days ending at the analysed commit. Reproduce with `git log --since='2026-07-21 21:37:28 +01:00' --until='2026-10-19 21:37:28 +01:00' --full-history --no-merges -- deepface/modules/demography.py`: merges are excluded because a merge re-states changes already counted at their own commits, and history is NOT path-simplified because a change that reached the file through a merged branch is still a change to it. That command counts raw commits and can read HIGHER than this row, which counts a cherry-picked re-land, and a revert together with the commit it undoes, once each — a difference of several commits on a file whose history was re-landed or reverted inside the window.
Duplicated block (9 lines × 2) deepface/models/facial_recognition/pytorch/Facenet.py:129— deepface/models/facial_recognition/pytorch/Facenet.py:129-137 | deepface/models/facial_recognition/pytorch/Facenet.py:153-161 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Note first that the copies are not typed on the same thing: the declarations holding them bind `scale` to `float = 0.1` in one and `float = 0.2` in another, and the duplicated lines use it. The extracted unit therefore needs a parameter type that fits BOTH — their common supertype where they have one, or a new abstraction over them where they do not — and settling that is the step that comes BEFORE the extraction above. Where the two types are deliberately unrelated, the duplication is the price of that separation and the honest resolution is to record the decision rather than to extract.
Duplicated block (9 lines × 2) deepface/models/facial_recognition/tf/OpenFace.py:112— deepface/models/facial_recognition/tf/OpenFace.py:112-120 | deepface/models/facial_recognition/tf/OpenFace.py:158-166 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Note first that the copies are not typed on the same thing: `Model` names `keras.models` in one and `tensorflow.keras.models` in another — different types that share a simple name, which is why the text matched. A single extracted unit cannot be given a parameter type that fits both, so unifying those types (or introducing a shared abstraction over them) is the step that has to come BEFORE the extraction above; if they are deliberately separate, the duplication is the price of that separation and the honest resolution is to record the decision rather than to extract.
Duplicated block (9 lines × 2) deepface/models/facial_recognition/tf/OpenFace.py:194— deepface/models/facial_recognition/tf/OpenFace.py:194-202 | deepface/models/facial_recognition/tf/OpenFace.py:291-299 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Read the line range as the matched WINDOW rather than a finished unit: at `deepface/models/facial_recognition/tf/OpenFace.py:194` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
Duplicated block (9 lines × 2) deepface/models/facial_recognition/tf/OpenFace.py:232— deepface/models/facial_recognition/tf/OpenFace.py:232-240 | deepface/models/facial_recognition/tf/OpenFace.py:313-321 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Read the line range as the matched WINDOW rather than a finished unit: at `deepface/models/facial_recognition/tf/OpenFace.py:232` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
Duplicated block (5 lines × 2) deepface/models/spoofing/pytorch/FasNetBackbone.py:231— deepface/models/spoofing/pytorch/FasNetBackbone.py:231-235 | deepface/models/spoofing/pytorch/FasNetBackbone.py:268-272 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Read the line range as the matched WINDOW rather than a finished unit: at `deepface/models/spoofing/pytorch/FasNetBackbone.py:231` it does not close everything it opens, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
Duplicated block (5 lines × 2) deepface/models/spoofing/pytorch/FasNetBackbone.py:436— deepface/models/spoofing/pytorch/FasNetBackbone.py:436-440 | deepface/models/spoofing/pytorch/FasNetBackbone.py:473-477 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Read the line range as the matched WINDOW rather than a finished unit: at `deepface/models/spoofing/pytorch/FasNetBackbone.py:436` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
Duplicated block (5 lines × 2) deepface/models/facial_recognition/onnx/OnnxFacialRecognition.py:36— deepface/models/facial_recognition/onnx/OnnxFacialRecognition.py:36-40 | deepface/models/facial_recognition/pytorch/TorchFacialRecognition.py:63-67 — the copies span different directories, so extracting a shared function means choosing where it lives: put it somewhere both call sites can already reach — a location they all depend on today, or a new shared one if there is none — and call it from each site; until then, every change has to be made twice. The matched lines also transfer control out of the body holding them, which cannot survive a move into a called unit unchanged: have the extracted unit return that decision and let each site act on it.
Duplicated block (5 lines × 2) deepface/models/facial_recognition/pytorch/OpenFace.py:129— deepface/models/facial_recognition/pytorch/OpenFace.py:129-133 | deepface/models/facial_recognition/pytorch/OpenFace.py:163-167 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (18 lines × 2) deepface/models/facial_recognition/onnx/VGGFace.py:36— deepface/models/facial_recognition/onnx/VGGFace.py:36-53 | deepface/models/facial_recognition/pytorch/VGGFace.py:42-59 — the copies span different directories, so extracting a shared function means choosing where it lives: put it somewhere both call sites can already reach — a location they all depend on today, or a new shared one if there is none — and call it from each site; until then, every change has to be made twice. The `return` at the foot of the matched lines is the enclosing body's own terminal exit, not an early one: it moves with them unchanged, and each site calls the extracted unit from the position that `return` occupied — no decision has to be handed back and re-acted on.
Duplicated block (18 lines × 2) deepface/modules/database/pgvector.py:51— deepface/modules/database/pgvector.py:51-68 | deepface/modules/database/postgres.py:72-89 — before extracting anything, compare `deepface/modules/database/pgvector.py` and `deepface/modules/database/postgres.py` as WHOLE FILES: this scan already matched 4 separate duplicated blocks between them, totalling at least 83 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. If that is what happened, the fix is to keep one copy and have the other call it (or delete it), which resolves this row and its siblings together — extracting one helper per block leaves the fork in place.
Duplicated block (18 lines × 2) deepface/modules/verification.py:306— deepface/modules/verification.py:306-323 | deepface/modules/verification.py:344-365 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (16 lines × 2) deepface/api/src/modules/core/routes.py:197— deepface/api/src/modules/core/routes.py:197-212 | deepface/api/src/modules/core/routes.py:243-258 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. The matched lines also transfer control out of the body holding them, which cannot survive a move into a called unit unchanged: have the extracted unit return that decision and let each site act on it.
Duplicated block (16 lines × 2) deepface/modules/database/db2.py:405— deepface/modules/database/db2.py:405-420 | deepface/modules/database/mssql.py:392-407 — before extracting anything, compare `deepface/modules/database/db2.py` and `deepface/modules/database/mssql.py` as WHOLE FILES: this scan already matched 7 separate duplicated blocks between them, totalling at least 138 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. If that is what happened, the fix is to keep one copy and have the other call it (or delete it), which resolves this row and its siblings together — extracting one helper per block leaves the fork in place. Read the line range as the matched WINDOW rather than a finished unit: at `deepface/modules/database/db2.py:405` it does not close everything it opens, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that. The matched lines also transfer control out of the body holding them, which cannot survive a move into a called unit unchanged: have the extracted unit return that decision and let each site act on it.
Duplicated block (16 lines × 2) deepface/modules/database/mssql.py:402— deepface/modules/database/mssql.py:402-417 | deepface/modules/database/mysql.py:436-451 — before extracting anything, compare `deepface/modules/database/mssql.py` and `deepface/modules/database/mysql.py` as WHOLE FILES: this scan already matched 5 separate duplicated blocks between them, totalling at least 87 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. If that is what happened, the fix is to keep one copy and have the other call it (or delete it), which resolves this row and its siblings together — extracting one helper per block leaves the fork in place.
Duplicated block (15 lines × 2) deepface/DeepFace.py:448— deepface/DeepFace.py:448-462 | deepface/DeepFace.py:958-972 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Read the line range as the matched WINDOW rather than a finished unit: at `deepface/DeepFace.py:448` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
Duplicated block (15 lines × 2) deepface/api/src/modules/core/service.py:78— deepface/api/src/modules/core/service.py:78-92 | deepface/api/src/modules/core/service.py:143-157 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Read the line range as the matched WINDOW rather than a finished unit: at `deepface/api/src/modules/core/service.py:78` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that. The matched lines also transfer control out of the body holding them, which cannot survive a move into a called unit unchanged: have the extracted unit return that decision and let each site act on it.
Duplicated block (15 lines × 2) deepface/modules/database/neo4j.py:162— deepface/modules/database/neo4j.py:162-183 | deepface/modules/database/pinecone.py:111-125 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from both call sites, so a change lands once.
Duplicated block (12 lines × 3) deepface/api/src/modules/core/routes.py:79— deepface/api/src/modules/core/routes.py:79-90 | deepface/api/src/modules/core/routes.py:112-123 | deepface/api/src/modules/core/routes.py:149-160 — all 3 copies are in the same file, so extract the block into one function there and call it from every one of those sites — resolving only two of them leaves the rest to drift apart the first time one is edited. The matched lines also transfer control out of the body holding them, which cannot survive a move into a called unit unchanged: have the extracted unit return that decision and let each site act on it.
Duplicated block (12 lines × 3) deepface/modules/database/db2.py:285— deepface/modules/database/db2.py:285-296 | deepface/modules/database/mssql.py:272-283 | deepface/modules/database/sqlite.py:245-256 — before extracting anything, compare `deepface/modules/database/db2.py` and `deepface/modules/database/mssql.py` as WHOLE FILES: this scan already matched 7 separate duplicated blocks between them, totalling at least 138 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. If that is what happened, the fix is to keep one copy and have the other call it (or delete it), which resolves this row and its siblings together — extracting one helper per block leaves the fork in place. Read the line range as the matched WINDOW rather than a finished unit: at `deepface/modules/database/db2.py:285` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that. The `return` at the foot of the matched lines is the enclosing body's own terminal exit, not an early one: it moves with them unchanged, and each site calls the extracted unit from the position that `return` occupied — no decision has to be handed back and re-acted on.
Duplicated block (12 lines × 3) deepface/modules/database/mysql.py:314— deepface/modules/database/mysql.py:314-325 | deepface/modules/database/pgvector.py:233-244 | deepface/modules/database/postgres.py:249-260 — before extracting anything, compare `deepface/modules/database/pgvector.py` and `deepface/modules/database/postgres.py` as WHOLE FILES: this scan already matched 4 separate duplicated blocks between them, totalling at least 83 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. If that is what happened, the fix is to keep one copy and have the other call it (or delete it), which resolves this row and its siblings together — extracting one helper per block leaves the fork in place. Read the line range as the matched WINDOW rather than a finished unit: at `deepface/modules/database/mysql.py:314` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that. The `return` at the foot of the matched lines is the enclosing body's own terminal exit, not an early one: it moves with them unchanged, and each site calls the extracted unit from the position that `return` occupied — no decision has to be handed back and re-acted on.
Duplicated block (31 lines × 2) deepface/modules/database/db2.py:319— deepface/modules/database/db2.py:319-349 | deepface/modules/database/sqlite.py:279-309 — before extracting anything, compare `deepface/modules/database/db2.py` and `deepface/modules/database/sqlite.py` as WHOLE FILES: this scan already matched 7 separate duplicated blocks between them, totalling at least 158 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. If that is what happened, the fix is to keep one copy and have the other call it (or delete it), which resolves this row and its siblings together — extracting one helper per block leaves the fork in place. The matched lines also transfer control out of the body holding them, which cannot survive a move into a called unit unchanged: have the extracted unit return that decision and let each site act on it.
Duplicated block (31 lines × 2) deepface/models/demography/onnx/Gender.py:33— deepface/models/demography/onnx/Gender.py:33-63 | deepface/models/demography/onnx/Race.py:33-64 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from both call sites, so a change lands once.
Duplicated block (5 lines × 3) deepface/models/facial_recognition/tf/OpenFace.py:143— deepface/models/facial_recognition/tf/OpenFace.py:143-148 | deepface/models/facial_recognition/tf/OpenFace.py:236-240 | deepface/models/facial_recognition/tf/OpenFace.py:317-321 — all 3 copies are in the same file, so extract the block into one function there and call it from every one of those sites — resolving only two of them leaves the rest to drift apart the first time one is edited. Read the line range as the matched WINDOW rather than a finished unit: at `deepface/models/facial_recognition/tf/OpenFace.py:143` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that. Note first that the copies are not typed on the same thing: `Model` names `keras.models` in one and `tensorflow.keras.models` in another — different types that share a simple name, which is why the text matched. A single extracted unit cannot be given a parameter type that fits both, so unifying those types (or introducing a shared abstraction over them) is the step that has to come BEFORE the extraction above; if they are deliberately separate, the duplication is the price of that separation and the honest resolution is to record the decision rather than to extract.
Duplicated block (5 lines × 3) deepface/models/facial_recognition/pytorch/OpenFace.py:94— deepface/models/facial_recognition/pytorch/OpenFace.py:94-98 | deepface/models/facial_recognition/pytorch/OpenFace.py:112-116 | deepface/models/facial_recognition/pytorch/OpenFace.py:146-150 — all 3 copies are in the same file, so extract the block into one function there and call it from every one of those sites — resolving only two of them leaves the rest to drift apart the first time one is edited.
Duplicated block (21 lines × 3) deepface/models/demography/onnx/Age.py:35— deepface/models/demography/onnx/Age.py:35-55 | deepface/models/demography/pytorch/Age.py:44-64 | deepface/models/demography/tf/Age.py:47-67 — the copies span different directories, so extracting a shared function means choosing where it lives: put it somewhere all 3 call sites can already reach — a location they all depend on today, or a new shared one if there is none — and call it from each site; until then, every change has to be made 3 times. The matched lines also transfer control out of the body holding them, which cannot survive a move into a called unit unchanged: have the extracted unit return that decision and let each site act on it.
Duplicated block (21 lines × 3) deepface/models/demography/onnx/Emotion.py:50— deepface/models/demography/onnx/Emotion.py:50-70 | deepface/models/demography/pytorch/Emotion.py:56-76 | deepface/models/demography/tf/Emotion.py:67-87 — the copies span different directories, so extracting a shared function means choosing where it lives: put it somewhere all 3 call sites can already reach — a location they all depend on today, or a new shared one if there is none — and call it from each site; until then, every change has to be made 3 times. The `return` at the foot of the matched lines is the enclosing body's own terminal exit, not an early one: it moves with them unchanged, and each site calls the extracted unit from the position that `return` occupied — no decision has to be handed back and re-acted on.
Duplicated block (5 lines × 6) deepface/models/facial_recognition/pytorch/ArcFace.py:36— deepface/models/facial_recognition/pytorch/ArcFace.py:36-40 | deepface/models/facial_recognition/pytorch/DeepID.py:33-37 | deepface/models/facial_recognition/pytorch/FbDeepFace.py:35-39 | deepface/models/facial_recognition/pytorch/GhostFaceNet.py:51-55 | deepface/models/facial_recognition/pytorch/OpenFace.py:39-43 | deepface/models/facial_recognition/pytorch/VGGFace.py:35-39 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from all 6 call sites, so a change lands once.
Duplicated block (5 lines × 6) deepface/models/facial_recognition/tf/Facenet.py:105— deepface/models/facial_recognition/tf/Facenet.py:105-109 | deepface/models/facial_recognition/tf/Facenet.py:110-114 | deepface/models/facial_recognition/tf/Facenet.py:115-119 | deepface/models/facial_recognition/tf/Facenet.py:121-125 | deepface/models/facial_recognition/tf/Facenet.py:126-130 | deepface/models/facial_recognition/tf/Facenet.py:131-135 — all 6 copies are in the same file, so extract the block into one function there and call it from every one of those sites — resolving only two of them leaves the rest to drift apart the first time one is edited. Note first that the copies are not typed on the same thing: `Activation` names `keras.layers` in one and `tensorflow.keras.layers` in another — different types that share a simple name, which is why the text matched. A single extracted unit cannot be given a parameter type that fits both, so unifying those types (or introducing a shared abstraction over them) is the step that has to come BEFORE the extraction above; if they are deliberately separate, the duplication is the price of that separation and the honest resolution is to record the decision rather than to extract. ★ These copies have DRIFTED, and that is worth reading before extracting anything: just after the matched lines, `deepface/models/facial_recognition/tf/Facenet.py:120` calls `MaxPooling2D` and `deepface/models/facial_recognition/tf/Facenet.py:110` does not — after which the two agree again for 4 more lines. One of those two behaviours is the intended one and the other is what a copy-paste left behind, so decide which BEFORE unifying them: extracting the shared part will silently settle it, and if the copy that skips the call is the wrong one, that bug is already live.
datastore.search (cyclomatic 43) deepface/modules/datastore.py:232— datastore.search has cyclomatic complexity 43 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
recognition.find (cyclomatic 42) deepface/modules/recognition.py:41— recognition.find has cyclomatic complexity 42 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
detection.extract_faces (cyclomatic 33) deepface/modules/detection.py:47— detection.extract_faces has cyclomatic complexity 33 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
weaviate.resolve_connection_details (cyclomatic 26) deepface/modules/database/weaviate.py:619— weaviate.resolve_connection_details has cyclomatic complexity 26 (threshold 15). To reduce it, split the body: these branches sit side by side rather than nested inside one another, so extracting each one on its own would leave a function per branch. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.
demography.analyze (cyclomatic 24) deepface/modules/demography.py:20— demography.analyze has cyclomatic complexity 24 (threshold 15). To reduce it, break up the iteration: give each loop body a named function, and split a multi-phase loop into one function per phase so no single body carries the whole pipeline.
datastore.identify (cyclomatic 21) deepface/modules/datastore.py:611— datastore.identify has cyclomatic complexity 21 (threshold 15). To reduce it, split the body: these branches sit side by side rather than nested inside one another, so extracting each one on its own would leave a function per branch. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.
streaming.analysis (cyclomatic 20) deepface/modules/streaming.py:28— streaming.analysis has cyclomatic complexity 20 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
representation.represent (cyclomatic 18) deepface/modules/representation.py:21— representation.represent has cyclomatic complexity 18 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
verification.find_confidence (cyclomatic 17) deepface/modules/verification.py:546— verification.find_confidence has cyclomatic complexity 17 (threshold 15). To reduce it, split the body: these branches sit side by side rather than nested inside one another, so extracting each one on its own would leave a function per branch. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages.
detection.extract_face (cyclomatic 16) deepface/modules/detection.py:345— detection.extract_face has cyclomatic complexity 16 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
TodoComment tests/unit/test_commons.py:364— # TODO: find a way to check write is called — source code is not a task system: move the work to your tracker and leave a reference instead (e.g. `# REF: #123`), so the task is planned where tasks live and the ticket links back to the code.
datastore.search (cognitive 84) deepface/modules/datastore.py:232— datastore.search has cognitive complexity 84 (threshold 15). Drivers by points: if/else 18 (46 pts), loops 7 (18 pts), ternaries 4 (10 pts), boolean chains 8, error handling 1 (2 pts) (nesting depth added 46). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body. This file is where this pass's cognitive complexity CONCENTRATES: deepface/modules/datastore.py holds 4 of the 17 methods over the threshold — including the worst — and 87 of the 277 points over it (31%), 2× the next-largest file (deepface/modules/detection.py at 43). No single row can show this, because each is measured only against the threshold: reducing this one file moves this dimension further than any other file in the repository.
recognition.find (cognitive 58) deepface/modules/recognition.py:41— recognition.find has cognitive complexity 58 (threshold 15). Drivers by points: if/else 28 (43 pts), boolean chains 7, loops 4 (6 pts), ternaries 2 (nesting depth added 17). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
detection.extract_faces (cognitive 51) deepface/modules/detection.py:47— detection.extract_faces has cognitive complexity 51 (threshold 15). Drivers by points: if/else 22 (37 pts), boolean chains 7, loops 4 (7 pts) (nesting depth added 18). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
demography.analyze (cognitive 37) deepface/modules/demography.py:20— demography.analyze has cognitive complexity 37 (threshold 15). Drivers by points: loops 7 (18 pts), if/else 7 (12 pts), boolean chains 5, ternaries 1 (2 pts) (nesting depth added 17). To reduce it, break up the iteration: give each loop body a named function, and split a multi-phase loop into one function per phase so no single body carries the whole pipeline.
streaming.analysis (cognitive 36) deepface/modules/streaming.py:28— streaming.analysis has cognitive complexity 36 (threshold 15). Drivers by points: if/else 11 (26 pts), ternaries 4 (7 pts), boolean chains 2, loops 1 (nesting depth added 18). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
weight_utils.download_all_models_in_one_shot (cognitive 28) deepface/commons/weight_utils.py:169— weight_utils.download_all_models_in_one_shot has cognitive complexity 28 (threshold 15). Drivers by points: if/else 4 (11 pts), loops 5 (9 pts), ternaries 2 (6 pts), error handling 1 (2 pts) (nesting depth added 16). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
CassandraClient.insert_embeddings (cognitive 28) deepface/modules/database/cassandra.py:432— CassandraClient.insert_embeddings has cognitive complexity 28 (threshold 15). Drivers by points: if/else 6 (17 pts), loops 5 (10 pts), boolean chains 1 (nesting depth added 16). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
weaviate.resolve_connection_details (cognitive 27) deepface/modules/database/weaviate.py:619— weaviate.resolve_connection_details has cognitive complexity 27 (threshold 15). Drivers by points: if/else 14 (18 pts), boolean chains 8, loops 1 (nesting depth added 4). To reduce it, split the body: most of this score is breadth rather than depth — checks laid out side by side rather than stacked — so group the statements between the checks into named steps and move each step into its own function. Some of it IS depth: where a check sits inside another whose only job is to reach it, merge the two into one condition, and where an else follows a branch that already returns, drop the trailing else and let the rest of the body continue at one level.
representation.represent (cognitive 27) deepface/modules/representation.py:21— representation.represent has cognitive complexity 27 (threshold 15). Drivers by points: if/else 10 (17 pts), loops 3 (4 pts), boolean chains 3, ternaries 2 (3 pts) (nesting depth added 9). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
datastore.__assign_attributes (cognitive 24) deepface/modules/datastore.py:1083— datastore.__assign_attributes has cognitive complexity 24 (threshold 15). Drivers by points: if/else 6 (17 pts), loops 3 (6 pts), boolean chains 1 (nesting depth added 14). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body. This file is where this pass's cognitive complexity CONCENTRATES: deepface/modules/datastore.py holds 4 of the 17 methods over the threshold — including the worst — and 87 of the 277 points over it (31%), 2× the next-largest file (deepface/modules/detection.py at 43). No single row can show this, because each is measured only against the threshold: reducing this one file moves this dimension further than any other file in the repository.
detection.extract_face (cognitive 22) deepface/modules/detection.py:345— detection.extract_face has cognitive complexity 22 (threshold 15). Drivers by points: if/else 10 (17 pts), boolean chains 5 (nesting depth added 7). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
datastore.identify (cognitive 21) deepface/modules/datastore.py:611— datastore.identify has cognitive complexity 21 (threshold 15). Drivers by points: if/else 9 (10 pts), boolean chains 7, ternaries 3, loops 1 (nesting depth added 1). To reduce it, split the body: this score is breadth rather than depth — many checks laid out side by side rather than nested inside one another, so inverting conditions into early returns has nothing left to flatten. Group the statements between the checks into named steps and move each step into its own function, so the body reads as a short sequence of named stages. This file is where this pass's cognitive complexity CONCENTRATES: deepface/modules/datastore.py holds 4 of the 17 methods over the threshold — including the worst — and 87 of the 277 points over it (31%), 2× the next-largest file (deepface/modules/detection.py at 43). No single row can show this, because each is measured only against the threshold: reducing this one file moves this dimension further than any other file in the repository.
RedisClient.insert_embeddings (cognitive 19) deepface/modules/database/redis.py:163— RedisClient.insert_embeddings has cognitive complexity 19 (threshold 15). Drivers by points: if/else 4 (10 pts), loops 5 (9 pts) (nesting depth added 10). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
verification.find_confidence (cognitive 19) deepface/modules/verification.py:546— verification.find_confidence has cognitive complexity 19 (threshold 15). Drivers by points: if/else 9, ternaries 4 (8 pts), boolean chains 2 (nesting depth added 4). To reduce it, split the body: most of this score is breadth rather than depth — checks laid out side by side rather than stacked — so group the statements between the checks into named steps and move each step into its own function. Some of it IS depth: where a check sits inside another whose only job is to reach it, merge the two into one condition, and where an else follows a branch that already returns, drop the trailing else and let the rest of the body continue at one level.
datastore.__register (cognitive 18) deepface/modules/datastore.py:144— datastore.__register has cognitive complexity 18 (threshold 15). Drivers by points: if/else 4 (7 pts), ternaries 3 (6 pts), loops 2 (3 pts), boolean chains 2 (nesting depth added 7). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body. This file is where this pass's cognitive complexity CONCENTRATES: deepface/modules/datastore.py holds 4 of the 17 methods over the threshold — including the worst — and 87 of the 277 points over it (31%), 2× the next-largest file (deepface/modules/detection.py at 43). No single row can show this, because each is measured only against the threshold: reducing this one file moves this dimension further than any other file in the repository.
weight_utils.download_weights_if_necessary (cognitive 17) deepface/commons/weight_utils.py:23— weight_utils.download_weights_if_necessary has cognitive complexity 17 (threshold 15). Drivers by points: if/else 7 (11 pts), error handling 1 (2 pts), ternaries 2, boolean chains 1, loops 1 (nesting depth added 5). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
weaviate.resolve_collection_options (cognitive 16) deepface/modules/database/weaviate.py:706— weaviate.resolve_collection_options has cognitive complexity 16 (threshold 15). Drivers by points: if/else 8 (11 pts), boolean chains 5 (nesting depth added 3). To reduce it, split the body into named stages: move each independent step or branch into its own named function so the body reads as a short sequence of named calls rather than one long body.
Change coupling clique: Age.py, Emotion.py, Gender.py, Race.py deepface/models/demography/tf/Age.py— 4 files — `deepface/models/demography/tf/Age.py`, `deepface/models/demography/tf/Emotion.py`, `deepface/models/demography/tf/Gender.py`, `deepface/models/demography/tf/Race.py` — all change together with no explicit dependency: a fully-connected co-change clique, not 6 separate couplings. They share one concern (thin parallel siblings over a common abstraction), so extract the shared part into ONE unit and the whole clique's coupling clears at once — you do not need to break each pair individually.
D4 · Code Duplication· Edited copy of a member (10 corresponding lines) · ×1
Edited copy of a member (10 corresponding lines) deepface/models/spoofing/pytorch/FasNetBackbone.py:230— deepface/models/spoofing/pytorch/FasNetBackbone.py:230-239 | deepface/models/spoofing/pytorch/FasNetBackbone.py:267-277 — These two members are one piece of code written twice and then edited apart: 10 consecutive lines correspond almost exactly, broken only by small local edits. Most of that correspondence is NOT reported as duplicated blocks below — the edits cut it into fragments and only the largest of them clear the block floor, so the rows below understate it. The repair is at the members' grain — factor the shared implementation into one the two call with their differences as parameters or as an injected step, or, where the difference is systematic (an extra return value, one transport against another), generate one from the other. Left alone, the next edit has to be made twice and the two will drift further apart.
D4 · Code Duplication· Members sharing a duplicated core (9 members, 50+ identical tokens) · ×1
Members sharing a duplicated core (9 members, 50+ identical tokens) deepface/modules/database/cassandra.py:433— deepface/modules/database/cassandra.py:433-545 | deepface/modules/database/db2.py:234-308 | deepface/modules/database/mssql.py:221-295 | deepface/modules/database/mysql.py:263-335 | deepface/modules/database/oracle.py:251-328 | deepface/modules/database/pgvector.py:169-252 | deepface/modules/database/postgres.py:199-268 | deepface/modules/database/redis.py:164-242 | deepface/modules/database/sqlite.py:194-268 — These 9 members share a duplicated core: a run of at least 50 identical tokens appears in every one of them. That run is NOT broken out as duplicated-block rows below — it is what admitted this row, and the blocks below cover only the part of it that clears the block floor, so they understate the correspondence. Read the members as one construct written 9 times. The repair is at the members' grain — factor the shared implementation out once and have all of them call it with their differences as parameters or as an injected step, or, where the difference is systematic, generate them from one template. Extracting the individual blocks below is not the same fix: it leaves every body in place and the next edit still has to be made 9 times.
Duplicated block (44 lines × 5) deepface/modules/database/db2.py:234— deepface/modules/database/db2.py:234-277 | deepface/modules/database/mssql.py:221-264 | deepface/modules/database/mysql.py:263-306 | deepface/modules/database/oracle.py:251-294 | deepface/modules/database/sqlite.py:194-237 — before extracting anything, compare `deepface/modules/database/db2.py` and `deepface/modules/database/mssql.py` as WHOLE FILES: this scan already matched 7 separate duplicated blocks between them, totalling at least 138 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. If that is what happened, the fix is to keep one copy and have the other call it (or delete it), which resolves this row and its siblings together — extracting one helper per block leaves the fork in place. Read the line range as the matched WINDOW rather than a finished unit: at `deepface/modules/database/db2.py:234` it does not close everything it opens, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
Duplicated block (43 lines × 2) deepface/modules/database/pgvector.py:210— deepface/modules/database/pgvector.py:210-252 | deepface/modules/database/postgres.py:226-268 — before extracting anything, compare `deepface/modules/database/pgvector.py` and `deepface/modules/database/postgres.py` as WHOLE FILES: this scan already matched 4 separate duplicated blocks between them, totalling at least 83 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. If that is what happened, the fix is to keep one copy and have the other call it (or delete it), which resolves this row and its siblings together — extracting one helper per block leaves the fork in place. The matched lines also transfer control out of the body holding them, which cannot survive a move into a called unit unchanged: have the extracted unit return that decision and let each site act on it.
Duplicated block (40–41 lines × 2) deepface/modules/database/cassandra.py:433— deepface/modules/database/cassandra.py:433-472 | deepface/modules/database/redis.py:164-204 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from both call sites, so a change lands once. ★ These copies have DRIFTED, and that is worth reading before extracting anything: just before the matched lines, `deepface/modules/database/cassandra.py:430` calls `join` and `deepface/modules/database/redis.py:161` does not — after which the two agree again for 3 more lines. One of those two behaviours is the intended one and the other is what a copy-paste left behind, so decide which BEFORE unifying them: extracting the shared part will silently settle it, and if the copy that skips the call is the wrong one, that bug is already live.
Duplicated block (28–30 lines × 3) deepface/models/demography/tf/Age.py:73— deepface/models/demography/tf/Age.py:73-102 | deepface/models/demography/tf/Gender.py:71-100 | deepface/models/demography/tf/Race.py:69-96 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from all 3 call sites, so a change lands once. The `return` at the foot of the matched lines is the enclosing body's own terminal exit, not an early one: it moves with them unchanged, and each site calls the extracted unit from the position that `return` occupied — no decision has to be handed back and re-acted on.
Duplicated block (3–26 lines × 3) deepface/models/facial_recognition/tf/OpenFace.py:62— deepface/models/facial_recognition/tf/OpenFace.py:62-64 | deepface/models/facial_recognition/tf/OpenFace.py:170-195 | deepface/models/facial_recognition/tf/OpenFace.py:216-233 — all 3 copies are in the same file, so extract the block into one function there and call it from every one of those sites — resolving only two of them leaves the rest to drift apart the first time one is edited. Read the line range as the matched WINDOW rather than a finished unit: at `deepface/models/facial_recognition/tf/OpenFace.py:170` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that. Note that the copies do not run to the end of the range shown: their LAST lines are different code, not the same code under different names — the matched region ends inside that line. Extract the lines above it, and read the last line of each site separately. Note first that the copies are not typed on the same thing: `Model` names `keras.models` in one and `tensorflow.keras.models` in another — different types that share a simple name, which is why the text matched. A single extracted unit cannot be given a parameter type that fits both, so unifying those types (or introducing a shared abstraction over them) is the step that has to come BEFORE the extraction above; if they are deliberately separate, the duplication is the price of that separation and the honest resolution is to record the decision rather than to extract.
Duplicated block (26 lines × 2) deepface/modules/database/db2.py:203— deepface/modules/database/db2.py:203-228 | deepface/modules/database/sqlite.py:163-188 — before extracting anything, compare `deepface/modules/database/db2.py` and `deepface/modules/database/sqlite.py` as WHOLE FILES: this scan already matched 7 separate duplicated blocks between them, totalling at least 158 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. If that is what happened, the fix is to keep one copy and have the other call it (or delete it), which resolves this row and its siblings together — extracting one helper per block leaves the fork in place. Read the line range as the matched WINDOW rather than a finished unit: at `deepface/modules/database/db2.py:203` it does not close everything it opens, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that. The matched lines also transfer control out of the body holding them, which cannot survive a move into a called unit unchanged: have the extracted unit return that decision and let each site act on it.
Duplicated block (22 lines × 3) deepface/modules/database/db2.py:329— deepface/modules/database/db2.py:329-350 | deepface/modules/database/oracle.py:350-371 | deepface/modules/database/sqlite.py:289-310 — before extracting anything, compare `deepface/modules/database/db2.py` and `deepface/modules/database/oracle.py` as WHOLE FILES: this scan already matched 4 separate duplicated blocks between them, totalling at least 89 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. If that is what happened, the fix is to keep one copy and have the other call it (or delete it), which resolves this row and its siblings together — extracting one helper per block leaves the fork in place. The matched lines also transfer control out of the body holding them, which cannot survive a move into a called unit unchanged: have the extracted unit return that decision and let each site act on it.
Duplicated block (17 lines × 16) deepface/models/facial_recognition/tf/Facenet.py:607— deepface/models/facial_recognition/tf/Facenet.py:607-623 | deepface/models/facial_recognition/tf/Facenet.py:670-686 | deepface/models/facial_recognition/tf/Facenet.py:733-749 | deepface/models/facial_recognition/tf/Facenet.py:796-812 | deepface/models/facial_recognition/tf/Facenet.py:859-875 | deepface/models/facial_recognition/tf/Facenet.py:922-938 | deepface/models/facial_recognition/tf/Facenet.py:985-1001 | deepface/models/facial_recognition/tf/Facenet.py:1048-1064 | deepface/models/facial_recognition/tf/Facenet.py:1111-1127 | deepface/models/facial_recognition/tf/Facenet.py:1174-1190 | deepface/models/facial_recognition/tf/Facenet.py:1323-1339 | deepface/models/facial_recognition/tf/Facenet.py:1386-1402 | deepface/models/facial_recognition/tf/Facenet.py:1449-1465 | deepface/models/facial_recognition/tf/Facenet.py:1512-1528 | deepface/models/facial_recognition/tf/Facenet.py:1575-1591 | deepface/models/facial_recognition/tf/Facenet.py:1638-1654 — all 16 copies are in the same file, so extract the block into one function there and call it from every one of those sites — resolving only two of them leaves the rest to drift apart the first time one is edited. Read the line range as the matched WINDOW rather than a finished unit: at `deepface/models/facial_recognition/tf/Facenet.py:607` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that. Note first that the copies are not typed on the same thing: `Activation` names `keras.layers` in one and `tensorflow.keras.layers` in another — different types that share a simple name, which is why the text matched. A single extracted unit cannot be given a parameter type that fits both, so unifying those types (or introducing a shared abstraction over them) is the step that has to come BEFORE the extraction above; if they are deliberately separate, the duplication is the price of that separation and the honest resolution is to record the decision rather than to extract.
Duplicated block (17 lines × 2) deepface/modules/database/pinecone.py:140— deepface/modules/database/pinecone.py:140-156 | deepface/modules/database/pinecone.py:236-252 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (15–16 lines × 2) deepface/modules/database/weaviate.py:363— deepface/modules/database/weaviate.py:363-377 | deepface/modules/database/weaviate.py:473-488 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. The `return` at the foot of the matched lines is the enclosing body's own terminal exit, not an early one: it moves with them unchanged, and each site calls the extracted unit from the position that `return` occupied — no decision has to be handed back and re-acted on.
Duplicated block (15 lines × 32) deepface/models/facial_recognition/tf/Facenet.py:587— deepface/models/facial_recognition/tf/Facenet.py:587-601 | deepface/models/facial_recognition/tf/Facenet.py:603-617 | deepface/models/facial_recognition/tf/Facenet.py:650-664 | deepface/models/facial_recognition/tf/Facenet.py:666-680 | deepface/models/facial_recognition/tf/Facenet.py:713-727 | deepface/models/facial_recognition/tf/Facenet.py:729-743 | deepface/models/facial_recognition/tf/Facenet.py:776-790 | deepface/models/facial_recognition/tf/Facenet.py:792-806 | deepface/models/facial_recognition/tf/Facenet.py:839-853 | deepface/models/facial_recognition/tf/Facenet.py:855-869 | deepface/models/facial_recognition/tf/Facenet.py:902-916 | deepface/models/facial_recognition/tf/Facenet.py:918-932 | deepface/models/facial_recognition/tf/Facenet.py:965-979 | deepface/models/facial_recognition/tf/Facenet.py:981-995 | deepface/models/facial_recognition/tf/Facenet.py:1028-1042 | deepface/models/facial_recognition/tf/Facenet.py:1044-1058 | deepface/models/facial_recognition/tf/Facenet.py:1091-1105 | deepface/models/facial_recognition/tf/Facenet.py:1107-1121 | deepface/models/facial_recognition/tf/Facenet.py:1154-1168 | deepface/models/facial_recognition/tf/Facenet.py:1170-1184 | deepface/models/facial_recognition/tf/Facenet.py:1303-1317 | deepface/models/facial_recognition/tf/Facenet.py:1319-1333 | deepface/models/facial_recognition/tf/Facenet.py:1366-1380 | deepface/models/facial_recognition/tf/Facenet.py:1382-1396 | deepface/models/facial_recognition/tf/Facenet.py:1429-1443 | deepface/models/facial_recognition/tf/Facenet.py:1445-1459 | deepface/models/facial_recognition/tf/Facenet.py:1492-1506 | deepface/models/facial_recognition/tf/Facenet.py:1508-1522 | deepface/models/facial_recognition/tf/Facenet.py:1555-1569 | deepface/models/facial_recognition/tf/Facenet.py:1571-1585 | deepface/models/facial_recognition/tf/Facenet.py:1618-1632 | deepface/models/facial_recognition/tf/Facenet.py:1634-1648 — all 32 copies are in the same file, so extract the block into one function there and call it from every one of those sites — resolving only two of them leaves the rest to drift apart the first time one is edited. Note first that the copies are not typed on the same thing: `BatchNormalization` names `keras.layers` in one and `tensorflow.keras.layers` in another — different types that share a simple name, which is why the text matched. A single extracted unit cannot be given a parameter type that fits both, so unifying those types (or introducing a shared abstraction over them) is the step that has to come BEFORE the extraction above; if they are deliberately separate, the duplication is the price of that separation and the honest resolution is to record the decision rather than to extract.
Duplicated block (13–14 lines × 20) deepface/models/facial_recognition/tf/Facenet.py:207— deepface/models/facial_recognition/tf/Facenet.py:207-219 | deepface/models/facial_recognition/tf/Facenet.py:282-294 | deepface/models/facial_recognition/tf/Facenet.py:357-369 | deepface/models/facial_recognition/tf/Facenet.py:432-444 | deepface/models/facial_recognition/tf/Facenet.py:507-520 | deepface/models/facial_recognition/tf/Facenet.py:622-634 | deepface/models/facial_recognition/tf/Facenet.py:685-697 | deepface/models/facial_recognition/tf/Facenet.py:748-760 | deepface/models/facial_recognition/tf/Facenet.py:811-823 | deepface/models/facial_recognition/tf/Facenet.py:874-886 | deepface/models/facial_recognition/tf/Facenet.py:937-949 | deepface/models/facial_recognition/tf/Facenet.py:1000-1012 | deepface/models/facial_recognition/tf/Facenet.py:1063-1075 | deepface/models/facial_recognition/tf/Facenet.py:1126-1138 | deepface/models/facial_recognition/tf/Facenet.py:1189-1202 | deepface/models/facial_recognition/tf/Facenet.py:1338-1350 | deepface/models/facial_recognition/tf/Facenet.py:1401-1413 | deepface/models/facial_recognition/tf/Facenet.py:1464-1476 | deepface/models/facial_recognition/tf/Facenet.py:1527-1539 | deepface/models/facial_recognition/tf/Facenet.py:1590-1602 — all 20 copies are in the same file, so extract the block into one function there and call it from every one of those sites — resolving only two of them leaves the rest to drift apart the first time one is edited. Read the line range as the matched WINDOW rather than a finished unit: at `deepface/models/facial_recognition/tf/Facenet.py:207` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that. Note first that the copies are not typed on the same thing: `Activation` names `keras.layers` in one and `tensorflow.keras.layers` in another — different types that share a simple name, which is why the text matched. A single extracted unit cannot be given a parameter type that fits both, so unifying those types (or introducing a shared abstraction over them) is the step that has to come BEFORE the extraction above; if they are deliberately separate, the duplication is the price of that separation and the honest resolution is to record the decision rather than to extract.
Duplicated block (14 lines × 3) deepface/api/src/modules/core/routes.py:194— deepface/api/src/modules/core/routes.py:194-207 | deepface/api/src/modules/core/routes.py:240-253 | deepface/api/src/modules/core/routes.py:282-295 — all 3 copies are in the same file, so extract the block into one function there and call it from every one of those sites — resolving only two of them leaves the rest to drift apart the first time one is edited. The matched lines also transfer control out of the body holding them, which cannot survive a move into a called unit unchanged: have the extracted unit return that decision and let each site act on it.
Duplicated block (14 lines × 2) deepface/models/spoofing/pytorch/FasNet.py:61— deepface/models/spoofing/pytorch/FasNet.py:61-74 | deepface/models/spoofing/pytorch/FasNet.py:77-90 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (13 lines × 3) deepface/modules/database/db2.py:219— deepface/modules/database/db2.py:219-231 | deepface/modules/database/oracle.py:236-248 | deepface/modules/database/sqlite.py:179-191 — before extracting anything, compare `deepface/modules/database/db2.py` and `deepface/modules/database/oracle.py` as WHOLE FILES: this scan already matched 4 separate duplicated blocks between them, totalling at least 89 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. If that is what happened, the fix is to keep one copy and have the other call it (or delete it), which resolves this row and its siblings together — extracting one helper per block leaves the fork in place. The matched lines also transfer control out of the body holding them, which cannot survive a move into a called unit unchanged: have the extracted unit return that decision and let each site act on it.
Duplicated block (10–12 lines × 8) deepface/models/facial_recognition/tf/OpenFace.py:208— deepface/models/facial_recognition/tf/OpenFace.py:208-217 | deepface/models/facial_recognition/tf/OpenFace.py:245-255 | deepface/models/facial_recognition/tf/OpenFace.py:267-276 | deepface/models/facial_recognition/tf/OpenFace.py:283-292 | deepface/models/facial_recognition/tf/OpenFace.py:305-314 | deepface/models/facial_recognition/tf/OpenFace.py:326-336 | deepface/models/facial_recognition/tf/OpenFace.py:346-355 | deepface/models/facial_recognition/tf/OpenFace.py:364-375 — all 8 copies are in the same file, so extract the block into one function there and call it from every one of those sites — resolving only two of them leaves the rest to drift apart the first time one is edited. Read the line range as the matched WINDOW rather than a finished unit: at `deepface/models/facial_recognition/tf/OpenFace.py:208` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that. ★ These copies have DRIFTED, and that is worth reading before extracting anything: just before the matched lines, `deepface/models/facial_recognition/tf/OpenFace.py:202` calls `ZeroPadding2D`, `concatenate` and `deepface/models/facial_recognition/tf/OpenFace.py:361` does not — after which the two agree again for 2 more lines. One of those two behaviours is the intended one and the other is what a copy-paste left behind, so decide which BEFORE unifying them: extracting the shared part will silently settle it, and if the copy that skips the call is the wrong one, that bug is already live.
Duplicated block (10–12 lines × 3) deepface/models/spoofing/tf/FasNetBackbone.py:256— deepface/models/spoofing/tf/FasNetBackbone.py:256-267 | deepface/models/spoofing/tf/FasNetBackbone.py:274-285 | deepface/models/spoofing/tf/FasNetBackbone.py:299-308 — all 3 copies are in the same file, so extract the block into one function there and call it from every one of those sites — resolving only two of them leaves the rest to drift apart the first time one is edited. Read the line range as the matched WINDOW rather than a finished unit: at `deepface/models/spoofing/tf/FasNetBackbone.py:256` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that. Note that the copies do not run to the end of the range shown: their LAST lines are different code, not the same code under different names — the matched region ends inside that line. Extract the lines above it, and read the last line of each site separately.
Duplicated block (12 lines × 2) deepface/models/facial_recognition/tf/Facenet.py:549— deepface/models/facial_recognition/tf/Facenet.py:549-560 | deepface/models/facial_recognition/tf/Facenet.py:1264-1275 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Read the line range as the matched WINDOW rather than a finished unit: at `deepface/models/facial_recognition/tf/Facenet.py:549` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that. Note first that the copies are not typed on the same thing: `Activation` names `keras.layers` in one and `tensorflow.keras.layers` in another — different types that share a simple name, which is why the text matched. A single extracted unit cannot be given a parameter type that fits both, so unifying those types (or introducing a shared abstraction over them) is the step that has to come BEFORE the extraction above; if they are deliberately separate, the duplication is the price of that separation and the honest resolution is to record the decision rather than to extract.
Duplicated block (11–12 lines × 2) deepface/models/facial_recognition/tf/Facenet.py:562— deepface/models/facial_recognition/tf/Facenet.py:562-572 | deepface/models/facial_recognition/tf/Facenet.py:1277-1288 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited. Read the line range as the matched WINDOW rather than a finished unit: at `deepface/models/facial_recognition/tf/Facenet.py:562` it does not close everything it opens, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that. Note first that the copies are not typed on the same thing: `BatchNormalization` names `keras.layers` in one and `tensorflow.keras.layers` in another — different types that share a simple name, which is why the text matched. A single extracted unit cannot be given a parameter type that fits both, so unifying those types (or introducing a shared abstraction over them) is the step that has to come BEFORE the extraction above; if they are deliberately separate, the duplication is the price of that separation and the honest resolution is to record the decision rather than to extract.
Duplicated block (3–11 lines × 7) deepface/models/facial_recognition/tf/OpenFace.py:68— deepface/models/facial_recognition/tf/OpenFace.py:68-70 | deepface/models/facial_recognition/tf/OpenFace.py:71-73 | deepface/models/facial_recognition/tf/OpenFace.py:86-96 | deepface/models/facial_recognition/tf/OpenFace.py:97-101 | deepface/models/facial_recognition/tf/OpenFace.py:123-127 | deepface/models/facial_recognition/tf/OpenFace.py:129-139 | deepface/models/facial_recognition/tf/OpenFace.py:151-155 — all 7 copies are in the same file, so extract the block into one function there and call it from every one of those sites — resolving only two of them leaves the rest to drift apart the first time one is edited. Note that the copies do not run to the end of the range shown: their LAST lines are different code, not the same code under different names — the matched region ends inside that line. Extract the lines above it, and read the last line of each site separately. Note first that the copies are not typed on the same thing: `Model` names `keras.models` in one and `tensorflow.keras.models` in another — different types that share a simple name, which is why the text matched. A single extracted unit cannot be given a parameter type that fits both, so unifying those types (or introducing a shared abstraction over them) is the step that has to come BEFORE the extraction above; if they are deliberately separate, the duplication is the price of that separation and the honest resolution is to record the decision rather than to extract. ★ These copies have DRIFTED, and that is worth reading before extracting anything: just before the matched lines, `deepface/models/facial_recognition/tf/OpenFace.py:66` calls `MaxPooling2D`, `Lambda`, `lrn` and `deepface/models/facial_recognition/tf/OpenFace.py:85` does not — after which the two agree again for 2 more lines. One of those two behaviours is the intended one and the other is what a copy-paste left behind, so decide which BEFORE unifying them: extracting the shared part will silently settle it, and if the copy that skips the call is the wrong one, that bug is already live.
Duplicated block (11 lines × 2) deepface/modules/database/db2.py:184— deepface/modules/database/db2.py:184-194 | deepface/modules/database/mssql.py:171-181 — before extracting anything, compare `deepface/modules/database/db2.py` and `deepface/modules/database/mssql.py` as WHOLE FILES: this scan already matched 7 separate duplicated blocks between them, totalling at least 138 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. If that is what happened, the fix is to keep one copy and have the other call it (or delete it), which resolves this row and its siblings together — extracting one helper per block leaves the fork in place. ★ These copies have DRIFTED, and that is worth reading before extracting anything: just before the matched lines, `deepface/modules/database/db2.py:183` calls `Binary` and `deepface/modules/database/mssql.py:170` does not — after which the two agree again for 4 more lines. One of those two behaviours is the intended one and the other is what a copy-paste left behind, so decide which BEFORE unifying them: extracting the shared part will silently settle it, and if the copy that skips the call is the wrong one, that bug is already live.
Duplicated block (10 lines × 73) deepface/models/facial_recognition/tf/Facenet.py:138— deepface/models/facial_recognition/tf/Facenet.py:138-147 | deepface/models/facial_recognition/tf/Facenet.py:149-158 | deepface/models/facial_recognition/tf/Facenet.py:160-169 | deepface/models/facial_recognition/tf/Facenet.py:171-180 | deepface/models/facial_recognition/tf/Facenet.py:182-191 | deepface/models/facial_recognition/tf/Facenet.py:193-202 | deepface/models/facial_recognition/tf/Facenet.py:213-222 | deepface/models/facial_recognition/tf/Facenet.py:224-233 | deepface/models/facial_recognition/tf/Facenet.py:235-244 | deepface/models/facial_recognition/tf/Facenet.py:246-255 | deepface/models/facial_recognition/tf/Facenet.py:257-266 | deepface/models/facial_recognition/tf/Facenet.py:268-277 | deepface/models/facial_recognition/tf/Facenet.py:288-297 | deepface/models/facial_recognition/tf/Facenet.py:299-308 | deepface/models/facial_recognition/tf/Facenet.py:310-319 | deepface/models/facial_recognition/tf/Facenet.py:321-330 | deepface/models/facial_recognition/tf/Facenet.py:332-341 | deepface/models/facial_recognition/tf/Facenet.py:343-352 | deepface/models/facial_recognition/tf/Facenet.py:363-372 | deepface/models/facial_recognition/tf/Facenet.py:374-383 | deepface/models/facial_recognition/tf/Facenet.py:385-394 | deepface/models/facial_recognition/tf/Facenet.py:396-405 | deepface/models/facial_recognition/tf/Facenet.py:407-416 | deepface/models/facial_recognition/tf/Facenet.py:418-427 | deepface/models/facial_recognition/tf/Facenet.py:438-447 | deepface/models/facial_recognition/tf/Facenet.py:449-458 | deepface/models/facial_recognition/tf/Facenet.py:460-469 | deepface/models/facial_recognition/tf/Facenet.py:471-480 | deepface/models/facial_recognition/tf/Facenet.py:482-491 | deepface/models/facial_recognition/tf/Facenet.py:493-502 | deepface/models/facial_recognition/tf/Facenet.py:514-523 | deepface/models/facial_recognition/tf/Facenet.py:525-534 | deepface/models/facial_recognition/tf/Facenet.py:536-545 | deepface/models/facial_recognition/tf/Facenet.py:547-556 | deepface/models/facial_recognition/tf/Facenet.py:565-574 | deepface/models/facial_recognition/tf/Facenet.py:576-585 | deepface/models/facial_recognition/tf/Facenet.py:628-637 | deepface/models/facial_recognition/tf/Facenet.py:639-648 | deepface/models/facial_recognition/tf/Facenet.py:691-700 | deepface/models/facial_recognition/tf/Facenet.py:702-711 | deepface/models/facial_recognition/tf/Facenet.py:754-763 | deepface/models/facial_recognition/tf/Facenet.py:765-774 | deepface/models/facial_recognition/tf/Facenet.py:817-826 | deepface/models/facial_recognition/tf/Facenet.py:828-837 | deepface/models/facial_recognition/tf/Facenet.py:880-889 | deepface/models/facial_recognition/tf/Facenet.py:891-900 | deepface/models/facial_recognition/tf/Facenet.py:943-952 | deepface/models/facial_recognition/tf/Facenet.py:954-963 | deepface/models/facial_recognition/tf/Facenet.py:1006-1015 | deepface/models/facial_recognition/tf/Facenet.py:1017-1026 | deepface/models/facial_recognition/tf/Facenet.py:1069-1078 | deepface/models/facial_recognition/tf/Facenet.py:1080-1089 | deepface/models/facial_recognition/tf/Facenet.py:1132-1141 | deepface/models/facial_recognition/tf/Facenet.py:1143-1152 | deepface/models/facial_recognition/tf/Facenet.py:1196-1205 | deepface/models/facial_recognition/tf/Facenet.py:1207-1216 | deepface/models/facial_recognition/tf/Facenet.py:1218-1227 | deepface/models/facial_recognition/tf/Facenet.py:1229-1238 | deepface/models/facial_recognition/tf/Facenet.py:1240-1249 | deepface/models/facial_recognition/tf/Facenet.py:1251-1260 | deepface/models/facial_recognition/tf/Facenet.py:1262-1271 | deepface/models/facial_recognition/tf/Facenet.py:1281-1290 | deepface/models/facial_recognition/tf/Facenet.py:1292-1301 | deepface/models/facial_recognition/tf/Facenet.py:1344-1353 | deepface/models/facial_recognition/tf/Facenet.py:1355-1364 | deepface/models/facial_recognition/tf/Facenet.py:1407-1416 | deepface/models/facial_recognition/tf/Facenet.py:1418-1427 | deepface/models/facial_recognition/tf/Facenet.py:1470-1479 | deepface/models/facial_recognition/tf/Facenet.py:1481-1490 | deepface/models/facial_recognition/tf/Facenet.py:1533-1542 | deepface/models/facial_recognition/tf/Facenet.py:1544-1553 | deepface/models/facial_recognition/tf/Facenet.py:1596-1605 | deepface/models/facial_recognition/tf/Facenet.py:1607-1616 — all 73 copies are in the same file, so extract the block into one function there and call it from every one of those sites — resolving only two of them leaves the rest to drift apart the first time one is edited. Note first that the copies are not typed on the same thing: `BatchNormalization` names `keras.layers` in one and `tensorflow.keras.layers` in another — different types that share a simple name, which is why the text matched. A single extracted unit cannot be given a parameter type that fits both, so unifying those types (or introducing a shared abstraction over them) is the step that has to come BEFORE the extraction above; if they are deliberately separate, the duplication is the price of that separation and the honest resolution is to record the decision rather than to extract.
Duplicated block (10 lines × 7) deepface/modules/database/cassandra.py:447— deepface/modules/database/cassandra.py:447-456 | deepface/modules/database/db2.py:266-275 | deepface/modules/database/mssql.py:253-262 | deepface/modules/database/mysql.py:295-304 | deepface/modules/database/oracle.py:283-292 | deepface/modules/database/redis.py:178-187 | deepface/modules/database/sqlite.py:226-235 — before extracting anything, compare `deepface/modules/database/db2.py` and `deepface/modules/database/mssql.py` as WHOLE FILES: this scan already matched 7 separate duplicated blocks between them, totalling at least 138 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. If that is what happened, the fix is to keep one copy and have the other call it (or delete it), which resolves this row and its siblings together — extracting one helper per block leaves the fork in place. Read the line range as the matched WINDOW rather than a finished unit: at `deepface/modules/database/cassandra.py:447` it does not close everything it opens, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that. ★ These copies have DRIFTED, and that is worth reading before extracting anything: just before the matched lines, `deepface/modules/database/db2.py:264` calls `dumps`, `list`, `astype` and `deepface/modules/database/cassandra.py:446` does not — after which the two agree again for 3 more lines. One of those two behaviours is the intended one and the other is what a copy-paste left behind, so decide which BEFORE unifying them: extracting the shared part will silently settle it, and if the copy that skips the call is the wrong one, that bug is already live.
Duplicated block (10 lines × 5) deepface/modules/database/mssql.py:259— deepface/modules/database/mssql.py:259-268 | deepface/modules/database/mysql.py:301-310 | deepface/modules/database/oracle.py:289-298 | deepface/modules/database/pgvector.py:220-229 | deepface/modules/database/postgres.py:236-245 — before extracting anything, compare `deepface/modules/database/mssql.py` and `deepface/modules/database/mysql.py` as WHOLE FILES: this scan already matched 5 separate duplicated blocks between them, totalling at least 87 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. If that is what happened, the fix is to keep one copy and have the other call it (or delete it), which resolves this row and its siblings together — extracting one helper per block leaves the fork in place. Read the line range as the matched WINDOW rather than a finished unit: at `deepface/modules/database/mssql.py:259` it does not close everything it opens, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
Duplicated block (8–10 lines × 3) deepface/api/src/modules/core/service.py:56— deepface/api/src/modules/core/service.py:56-63 | deepface/api/src/modules/core/service.py:113-120 | deepface/api/src/modules/core/service.py:196-205 — all 3 copies are in the same file, so extract the block into one function there and call it from every one of those sites — resolving only two of them leaves the rest to drift apart the first time one is edited. Read the line range as the matched WINDOW rather than a finished unit: at `deepface/api/src/modules/core/service.py:56` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that. The matched lines also transfer control out of the body holding them, which cannot survive a move into a called unit unchanged: have the extracted unit return that decision and let each site act on it.
Duplicated block (7–9 lines × 2) deepface/modules/database/milvus.py:111— deepface/modules/database/milvus.py:111-119 | deepface/modules/database/pinecone.py:105-111 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from both call sites, so a change lands once. Read the line range as the matched WINDOW rather than a finished unit: at `deepface/modules/database/milvus.py:111` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that. ★ These copies have DRIFTED, and that is worth reading before extracting anything: just before the matched lines, `deepface/modules/database/pinecone.py:103` calls `Index` and `deepface/modules/database/milvus.py:111` does not — after which the two agree again for 4 more lines. One of those two behaviours is the intended one and the other is what a copy-paste left behind, so decide which BEFORE unifying them: extracting the shared part will silently settle it, and if the copy that skips the call is the wrong one, that bug is already live.
Duplicated block (8 lines × 3) deepface/models/facial_recognition/tf/OpenFace.py:194— deepface/models/facial_recognition/tf/OpenFace.py:194-201 | deepface/models/facial_recognition/tf/OpenFace.py:291-298 | deepface/models/facial_recognition/tf/OpenFace.py:354-361 — all 3 copies are in the same file, so extract the block into one function there and call it from every one of those sites — resolving only two of them leaves the rest to drift apart the first time one is edited. Read the line range as the matched WINDOW rather than a finished unit: at `deepface/models/facial_recognition/tf/OpenFace.py:194` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that. ★ These copies have DRIFTED, and that is worth reading before extracting anything: just after the matched lines, `deepface/models/facial_recognition/tf/OpenFace.py:202` calls `ZeroPadding2D`, `concatenate` and `deepface/models/facial_recognition/tf/OpenFace.py:363` does not — after which the two agree again for 3 more lines. One of those two behaviours is the intended one and the other is what a copy-paste left behind, so decide which BEFORE unifying them: extracting the shared part will silently settle it, and if the copy that skips the call is the wrong one, that bug is already live.
Duplicated block (3–8 lines × 4) deepface/models/facial_recognition/tf/OpenFace.py:68— deepface/models/facial_recognition/tf/OpenFace.py:68-70 | deepface/models/facial_recognition/tf/OpenFace.py:77-84 | deepface/models/facial_recognition/tf/OpenFace.py:104-109 | deepface/models/facial_recognition/tf/OpenFace.py:151-155 — all 4 copies are in the same file, so extract the block into one function there and call it from every one of those sites — resolving only two of them leaves the rest to drift apart the first time one is edited. Note first that the copies are not typed on the same thing: `Model` names `keras.models` in one and `tensorflow.keras.models` in another — different types that share a simple name, which is why the text matched. A single extracted unit cannot be given a parameter type that fits both, so unifying those types (or introducing a shared abstraction over them) is the step that has to come BEFORE the extraction above; if they are deliberately separate, the duplication is the price of that separation and the honest resolution is to record the decision rather than to extract. ★ These copies have DRIFTED, and that is worth reading before extracting anything: just before the matched lines, `deepface/models/facial_recognition/tf/OpenFace.py:67` calls `Lambda`, `lrn`, `Conv2D` and `deepface/models/facial_recognition/tf/OpenFace.py:77` does not — after which the two agree again for 2 more lines. One of those two behaviours is the intended one and the other is what a copy-paste left behind, so decide which BEFORE unifying them: extracting the shared part will silently settle it, and if the copy that skips the call is the wrong one, that bug is already live.
Duplicated block (7 lines × 18) deepface/models/facial_recognition/tf/OpenFace.py:177— deepface/models/facial_recognition/tf/OpenFace.py:177-183 | deepface/models/facial_recognition/tf/OpenFace.py:193-199 | deepface/models/facial_recognition/tf/OpenFace.py:207-213 | deepface/models/facial_recognition/tf/OpenFace.py:215-221 | deepface/models/facial_recognition/tf/OpenFace.py:231-237 | deepface/models/facial_recognition/tf/OpenFace.py:244-250 | deepface/models/facial_recognition/tf/OpenFace.py:253-259 | deepface/models/facial_recognition/tf/OpenFace.py:263-272 | deepface/models/facial_recognition/tf/OpenFace.py:274-280 | deepface/models/facial_recognition/tf/OpenFace.py:290-296 | deepface/models/facial_recognition/tf/OpenFace.py:304-310 | deepface/models/facial_recognition/tf/OpenFace.py:312-318 | deepface/models/facial_recognition/tf/OpenFace.py:325-331 | deepface/models/facial_recognition/tf/OpenFace.py:334-340 | deepface/models/facial_recognition/tf/OpenFace.py:345-351 | deepface/models/facial_recognition/tf/OpenFace.py:353-359 | deepface/models/facial_recognition/tf/OpenFace.py:363-369 | deepface/models/facial_recognition/tf/OpenFace.py:373-379 — all 18 copies are in the same file, so extract the block into one function there and call it from every one of those sites — resolving only two of them leaves the rest to drift apart the first time one is edited. Read the line range as the matched WINDOW rather than a finished unit: at `deepface/models/facial_recognition/tf/OpenFace.py:263` it begins part-way through the construct above it, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that. ★ These copies have DRIFTED, and that is worth reading before extracting anything: just after the matched lines, `deepface/models/facial_recognition/tf/OpenFace.py:201` calls `MaxPooling2D`, `ZeroPadding2D`, `concatenate` and `deepface/models/facial_recognition/tf/OpenFace.py:282` does not — after which the two agree again for 2 more lines. One of those two behaviours is the intended one and the other is what a copy-paste left behind, so decide which BEFORE unifying them: extracting the shared part will silently settle it, and if the copy that skips the call is the wrong one, that bug is already live.
Duplicated block (7 lines × 3) deepface/modules/database/db2.py:415— deepface/modules/database/db2.py:415-421 | deepface/modules/database/mssql.py:402-408 | deepface/modules/database/mysql.py:436-442 — before extracting anything, compare `deepface/modules/database/db2.py` and `deepface/modules/database/mssql.py` as WHOLE FILES: this scan already matched 7 separate duplicated blocks between them, totalling at least 138 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. If that is what happened, the fix is to keep one copy and have the other call it (or delete it), which resolves this row and its siblings together — extracting one helper per block leaves the fork in place. Read the line range as the matched WINDOW rather than a finished unit: at `deepface/modules/database/db2.py:415` it does not close everything it opens, so those exact lines cannot be lifted as they stand — widen the region to the smallest complete statement or declaration that contains it, and extract that.
Duplicated block (6 lines × 2) REDACTED:52— REDACTED:52-57 | REDACTED:70-75 — both copies are in the same file, so extract the block into one function there and call it from each site — the copies drift apart the first time only one of them is edited.
Duplicated block (15 lines × 5) deepface/models/facial_recognition/onnx/ArcFace.py:26— deepface/models/facial_recognition/onnx/ArcFace.py:26-40 | deepface/models/facial_recognition/onnx/DeepID.py:26-40 | deepface/models/facial_recognition/onnx/FbDeepFace.py:26-40 | deepface/models/facial_recognition/onnx/GhostFaceNet.py:28-42 | deepface/models/facial_recognition/onnx/OpenFace.py:26-40 — the copies sit in sibling files of one directory, so a shared home is within easy reach: extract the block into a single shared function the call sites can all reach — a file they already depend on, or a new one alongside them — and call it from all 5 call sites, so a change lands once.
Duplicated block (12 lines × 6) deepface/models/demography/pytorch/Age.py:88— deepface/models/demography/pytorch/Age.py:88-99 | deepface/models/demography/pytorch/Emotion.py:149-160 | deepface/models/demography/pytorch/Gender.py:82-93 | deepface/models/demography/pytorch/Race.py:84-95 | deepface/models/facial_recognition/pytorch/ArcFace.py:184-195 | deepface/models/facial_recognition/pytorch/GhostFaceNet.py:293-304 — the copies span different directories, so extracting a shared function means choosing where it lives: put it somewhere all 6 call sites can already reach — a location they all depend on today, or a new shared one if there is none — and call it from each site; until then, every change has to be made 6 times. The `return` at the foot of the matched lines is the enclosing body's own terminal exit, not an early one: it moves with them unchanged, and each site calls the extracted unit from the position that `return` occupied — no decision has to be handed back and re-acted on.
Duplicated block (38 lines × 2) deepface/modules/database/db2.py:362— deepface/modules/database/db2.py:362-399 | deepface/modules/database/mssql.py:349-386 — before extracting anything, compare `deepface/modules/database/db2.py` and `deepface/modules/database/mssql.py` as WHOLE FILES: this scan already matched 7 separate duplicated blocks between them, totalling at least 138 lines, which is the signature of one file having been copied from the other rather than of a helper waiting to be extracted. If that is what happened, the fix is to keep one copy and have the other call it (or delete it), which resolves this row and its siblings together — extracting one helper per block leaves the fork in place. The matched lines also transfer control out of the body holding them, which cannot survive a move into a called unit unchanged: have the extracted unit return that decision and let each site act on it.
No DR/backup evidence — A persistence guard (data volume / purge-protection) was found, but no backup, geo-recovery or RTO/RPO controls were evidenced — a volume that survives a container recreate is not a tested restore from catastrophic loss.
X17 · Uncapped recursion over a caller-supplied document· Recursive walk over a caller-supplied document has no depth cap · ×1
Recursive walk over a caller-supplied document has no depth cap deepface/api/src/modules/core/service.py:25— `to_native` is public and takes `obj: Any` from its caller, and it calls itself at line 25 (`to_native`) with a piece of `obj` (and at line 27, line 29, line 31) — and nowhere in `to_native` is there a depth parameter, a descent counter, or any other cap. The walk therefore spends one stack frame per LEVEL of the value it is handed, so the nesting of the caller's document — not this code — decides how deep the stack goes. Python's parsers accept far more nesting than its stack allows: `json.loads` turns 3,000 bytes of brackets into 1,500 levels and `xml.etree.ElementTree` parses tens of thousands, while a walk like this one raises `RecursionError` once it passes the interpreter's default limit of 1,000 frames. Wherever that exception lands, the work fails: a request errors, and outside any `try` the worker or the program exits with it. Raising `sys.setrecursionlimit` only moves the number the document has to beat. Thread a depth argument through `to_native` and reject the value past a fixed maximum, or rewrite the descent with an explicit stack.
Swallowed exception (empty catch) deepface/models/face_detection/OpenCv.py:64— An empty catch block silently discards the error — failures vanish with no log and no rethrow. Log it, handle it, or don't catch it.
Duplicated predicate deepface/modules/demography.py:169— `anti_spoofing is True and img_obj.get("is_real", True) is False` appears character-identically in 3 files — deepface/modules/demography.py, deepface/modules/representation.py, deepface/modules/verification.py. It is one line, so the duplication detector's token window never sees it; the copies drift when only one is corrected. Give the condition a name and one home.
Duplicated predicate deepface/modules/database/pgvector.py:55— `connection_details or os.environ.get("DEEPFACE_POSTGRES_URI")` appears character-identically in 2 files — deepface/modules/database/pgvector.py, deepface/modules/database/postgres.py. It is one line, so the duplication detector's token window never sees it; the copies drift when only one is corrected. Give the condition a name and one home.
Duplicated predicate deepface/modules/demography.py:112— `isinstance(img_path, np.ndarray) and img_path.ndim == 4` appears character-identically in 3 files — deepface/modules/demography.py, deepface/modules/detection.py, deepface/modules/representation.py. It is one line, so the duplication detector's token window never sees it; the copies drift when only one is corrected. Give the condition a name and one home.
Duplicated predicate deepface/modules/recognition.py:165— `not isinstance(distance_metric, str) and threshold is None` appears character-identically in 2 files — deepface/modules/recognition.py, deepface/modules/verification.py. It is one line, so the duplication detector's token window never sees it; the copies drift when only one is corrected. Give the condition a name and one home.
C2 · Access Controls· No named authorization policies · ×1
No named authorization policies — Authorization IS enforced here (by route guards, auth middleware or checks that refuse a caller) — this is NOT a claim that endpoints are unprotected. What's missing is a NAMED rule set: who may do what is encoded inline where it is checked, so the rules can't be listed, reviewed or tested apart from the code that applies them. Recommendation, not a defect.
Off-boarding risk: anonymized user #1 — If anonymized user #1 becomes unavailable, 29 significant file(s) lose their only recent owner: deepface/modules/database/neo4j.py, deepface/models/facial_recognition/tf/OpenFace.py, deepface/modules/database/pgvector.py, deepface/modules/database/postgres.py, deepface/modules/database/mongo.py, deepface/models/spoofing/tf/FasNetBackbone.py, deepface/models/facial_recognition/pytorch/Facenet.py, deepface/models/facial_recognition/pytorch/GhostFaceNet.py (+21 more). Pair on, review, or document these before any departure.
No ADRs found — No ADRs found. No recognised ADR directory (`docs/adr/`, `docs/decisions/`, `adr/`, `docs/rfcs/`, an `ADR0001/` folder, or their siblings) exists anywhere in this tree. What was searched, so you can tell an empty log from a search that missed one: every directory under the tree (build output, dependencies and VCS metadata excepted), for a document that is either any non-index page inside a recognised ADR directory, whatever its name and however deeply nested (`docs/adr/use-postgres.md`, `docs/adr/2024/0001-x.md`); or a file anywhere whose name is ADR-shaped (`0001-use-postgres.md`, `adr-012-caching.md`); or, when neither turned anything up, a document carrying the decision-record signature (an "Architecture Decision Record" heading, or Status / Context / Decision / Consequences as section headings). A decision log that clears none of these — unnumbered files outside any recognised directory, without those headings — is not seen by this check and this row is then wrong. If that is your case, say so rather than renaming anything; otherwise, consider recording architectural decisions in `docs/adr/`.
D26 · Project Cohesion· Projects may be oversized for their cohesion · ×1
Projects may be oversized for their cohesion — 1 of 1 project(s) overshoot their size bounds, lowering Project Cohesion to 0.0/10. The most over is `(repository root)` (22541 LoC, 234 module-visible types across 25 directories). Review these for cohesion — draw the boundary inside the module first (group each responsibility into its own package or directory and keep the cross-boundary members non-public), since splitting a published package moves types between packages and breaks consumers.
No ADRs — No Architecture Decision Records found — no conventional ADR directory, no numbered `NNNN-title` documents in any markup this check reads, and nothing ADR-shaped by content. Design rationale recorded elsewhere (a design-notes tree, a mailing list, pull-request discussion) is not visible to this check and is not re-findable per decision, so a future maintainer cannot ask why one choice was made and get an answer.
No src/ separation — Production code isn't grouped under a src/ folder — it's spread across several top-level directories, so there's no one place that says 'this is the product'.
P1 · CI/CD gates· CI build step not evidenced · ×1
CI build step not evidenced — A CI pipeline exists but no build step was matched — changes may merge without the build ever running. A build step may be invoked directly as a command, or declared as a task that a runner named in the pipeline resolves.
No SAST — No static application security testing detected. For this repository's stack, add bandit, `semgrep --config=p/python`, or CodeQL's python pack as a CI step. What was searched, so you can tell an absence from a miss: the 2426 CI workflow file(s) in this repository, and the scanner and linter configuration checked in beside them. A scan that runs outside CI, one configured in your forge's web UI rather than in a committed file, or a tool whose name is none of those this check carries, is not seen — if that is your case the row is wrong, and saying so is more useful than adding a second scanner.
P4 · Deployment & Rollback· No rollback/health safety · ×1
No rollback/health safety — Deployment is orchestrated by compose, but no service declares a `healthcheck:` and nothing pins a previous image to fall back to — the runtime can tell that the container is up, not that it is serving, so a bad release is harder to detect and reverse.
No changelog — No CHANGELOG/HISTORY/RELEASES file — what shipped when isn't easy to reconstruct for support or audit. (Versioning/tagging makes releases traceable, but a changelog records the what.)
Interpolated log message defeats structured logging deepface/commons/package_utils.py:57— `debug` at line 57 is handed an f-string as its message, so the logger receives one finished string — the values in it cannot be queried, filtered or aggregated as fields, every occurrence reads as a different message, and the string is built even when the record is filtered out. Pass the values beside a constant message instead: `logger.debug("order %s moved to %s", order_id, status)` with the standard library's `logging`, which keeps the message and its arguments apart on the record, or `log.debug("order moved", order_id=order_id, status=status)` with structlog.
Silent fallback default in catch deepface/modules/streaming.py:508— `grab_facial_areas` swallows every exception into `return []` — a data error becomes a silent behavior change with no trail. Log the failure or let it raise. If that constant is the correct answer rather than a stand-in for a value that could not be read, say so in a comment on the handler or in the docstring, and the row stops firing.
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.
semgrep: not applicable — No personal data was found crossing a boundary the PII/GDPR ruleset checks — nothing written to a log or console sink, placed in a URL or query string, or persisted to browser storage. That is a clean result for the LEAK surface only: this ruleset detects personal data escaping, it does not inventory the personal data a repository holds, so it is not evidence that this repository has no personal-data surface. The personal-data map (Appendix C) and the C1-C5 compliance cards are what speak to that.
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.
Run 01a15aa7-49ee-73bc-a1e9-a3243d892b69 · every finding is also locatable in findings.md, and the complete scoring record (with exit codes + durations) in sidecar.json.
Issues: 17 · Warnings: 120 · Recommendations: 18 — Appendix A · all findings · full markdown report.
Generated by Watchdog — deterministic code-health analysis. 20-10-2026 @ 18:51 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.