Public report of huggingface/datasets, 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, 19:18 UTC · Survey ID cd_7f20714f008e40a28f369836a6d5698e
huggingface/datasets
Measured 20 October 2026, 19:16 UTC, at code version 0623990
CAI scoreAdequate
CriticalWeakAdequateStrongExemplary
Down 9 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: 43,983 lines of product code in 1 project, tests not counted · Estimated rebuild effort: about 0.6 person-years · Weakest lens: Maturity, 35
Findings by how serious they are
11 critical390 serious97 minor
18 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, 19:16 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 ▸
52of 56 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.
483of 498 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.
56dimensions across 6 lenses, read from 43,983 lines of product code in 1 project.
huggingface/datasets scores 55 out of 100 and is rated Adequate, while five of its six lenses score higher still: Performance at 89 and Production Readiness at 72, both rated Strong, and Architecture at 89, Code Health at 70 and Security and Compliance at 68, all rated Adequate.
The score is lower than those five because the weakest lens counts most, and here that is Maturity, which scores 35 and is rated Weak. Its Architecture documentation dimension scores 0 out of 10, and until that reaches 4, Maturity can't be rated higher than Adequate. The first actions are to resolve the 1 No ADRs found finding(s) in ADR Quality and to record your significant design decisions, and if Maturity rose to 70, the score would rise from 55 to about 70.
11 findings are critical: nine in Static Analysis (SAST) and two in Knowledge Freshness.
Watchdog couldn't measure 16 dimensions in this code, among them Code Coverage, Internal API Consistency and Navigability, so they are left out of the score. The product code is 43,983 lines, which would take an estimated 0.6 person-years, around €84,000, 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: 10 findings
These 10 critical or serious findings were in the previous survey, on 4 October 2026, and are gone from this one.
Code Duplication · Members sharing a duplicated core (4 members, 50+ identical tokens) src/datasets/arrow_dataset.py
Code Duplication · Members sharing a duplicated core (4 members, 50+ identical tokens) src/datasets/formatting/jax_formatter.py
Code Duplication · Members sharing a duplicated core (4 members, 50+ identical tokens) src/datasets/io/csv.py
€28,000 to €140,000, for 466 to 1,478 hours of work, about 0.6 person-years for 1 to 2 engineers
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
×0.8 at a score of 55. 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: 43,983 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 1 No ADRs found finding(s) in ADR Quality.
Raises the score by about 15 points · Low effort · ADR Quality
2
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.
Reconcile the README with reality: docs/source/about_dataset_features.mdx links to `./process#flatten`, but `docs/source/process` does not exist; docs/source/about_dataset_load.mdx names `datasets.packaged_modules.csv.Csv`, but no file or folder `datasets.packaged_modules.csv.Csv` exists; docs/source/about_dataset_load.mdx names `datasets.packaged_modules.json.Json`, but no file or folder `datasets.packaged_modules.json.Json` exists; docs/source/about_dataset_load.mdx names `datasets.packaged_modules.sql.Sql`, but no file or folder `datasets.packaged_modules.sql.Sql` exists; docs/source/about_dataset_load.mdx links to `./upload_dataset`, but `docs/source/upload_dataset` does not exist.
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 €84,000 to build again, about 0.6 person-years of work. Its weakest lens is Maturity, at 35 (Weak). Maturity is about how well the project explains and looks after itself, which decides whether a new developer, or a new supplier, can take over. Because the weakest lens counts most in the score, work on Maturity also moves the score most.
Based on: the rebuild estimate · the Maturity grade · how much each lens counts
What to do: Start with the Maturity actions in Top priorities.
If you do one thing first · First action
Resolve the 1 No ADRs found finding(s) in ADR Quality. Of all the actions in this report, it raises the score most for the work it takes: about 15 points for low effort, because it clears the 1 ADR Quality findings. The next survey will show the change in the score.
Based on: the ranked actions in Top priorities, by score gain and effort
What hard-to-read code costs each year · Cost
Four dimensions measure how easy the code is to work in: two measure how tangled its logic is, one how much of it is copied, and one how well each part sticks to a single job. In this codebase they average 6.6 out of 10. Code at that level is slower to change than clean code, and Watchdog estimates the extra work at 3% to 8% on every change. The history of the code shows how much it changes: about 11,885 lines over the last 90 days, which is about 48,200 lines a year. At that pace, the extra work comes to an estimated 4.1 to 24.5 engineer-days a year, and it grows as more code changes.
How this is estimated: The number of changed lines is measured from the history of the code. The rest rests on two assumptions of Watchdog's own: code scoring 8 out of 10 or more adds no extra work, and each point below 8 adds 2.5% to 5.6% to every change; and an engineer changes 150 to 400 lines in a day. The result is a range for planning, not a measurement.
Based on: Cyclomatic Complexity · Cognitive Complexity · Code Duplication · Cohesion (LCOM4) · the code's change history
What to do: Start with the files listed under Churn × Complexity Hotspots, which change often and are hard to read, because that is where the extra work is paid most often.
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.
119 modules, with 102 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; 79 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.
commands.delete_from_hub uses commands, so a change to commands can break commands.delete_from_hub, while a change to commands.delete_from_hub can't break commands.
Position
Below the diagonal: commands.delete_from_hub uses a module listed above it, which is the healthy direction.
download.download_manager uses download.download_config, so a change to download.download_config can break download.download_manager, while a change to download.download_manager can't break download.download_config.
Position
Below the diagonal: download.download_manager uses a module listed above it, which is the healthy direction.
formatting uses formatting.formatting, so a change to formatting.formatting can break formatting, while a change to formatting can't break formatting.formatting.
Position
Below the diagonal: formatting uses a module listed above it, which is the healthy direction.
formatting.jax_formatter uses formatting.formatting, so a change to formatting.formatting can break formatting.jax_formatter, while a change to formatting.jax_formatter can't break formatting.formatting.
Position
Below the diagonal: formatting.jax_formatter uses a module listed above it, which is the healthy direction.
formatting.np_formatter uses formatting.formatting, so a change to formatting.formatting can break formatting.np_formatter, while a change to formatting.np_formatter can't break formatting.formatting.
Position
Below the diagonal: formatting.np_formatter uses a module listed above it, which is the healthy direction.
formatting.polars_formatter uses formatting.formatting, so a change to formatting.formatting can break formatting.polars_formatter, while a change to formatting.polars_formatter can't break formatting.formatting.
Position
Below the diagonal: formatting.polars_formatter uses a module listed above it, which is the healthy direction.
formatting.tf_formatter uses formatting.formatting, so a change to formatting.formatting can break formatting.tf_formatter, while a change to formatting.tf_formatter can't break formatting.formatting.
Position
Below the diagonal: formatting.tf_formatter uses a module listed above it, which is the healthy direction.
arrow_dataset uses features.features, so a change to features.features can break arrow_dataset, while a change to arrow_dataset can't break features.features.
Position
Below the diagonal: arrow_dataset uses a module listed above it, which is the healthy direction.
dataset_dict uses features.features, so a change to features.features can break dataset_dict, while a change to dataset_dict can't break features.features.
Position
Below the diagonal: dataset_dict uses a module listed above it, which is the healthy direction.
iterable_dataset uses features.features, so a change to features.features can break iterable_dataset, while a change to iterable_dataset can't break features.features.
Position
Below the diagonal: iterable_dataset uses a module listed above it, which is the healthy direction.
iterable_dataset uses arrow_dataset, so a change to arrow_dataset can break iterable_dataset, while a change to iterable_dataset can't break arrow_dataset.
Position
Below the diagonal: iterable_dataset uses a module listed above it, which is the healthy direction.
builder uses download.download_config, so a change to download.download_config can break builder, while a change to builder can't break download.download_config.
Position
Below the diagonal: builder uses a module listed above it, which is the healthy direction.
builder uses download.download_manager, so a change to download.download_manager can break builder, while a change to builder can't break download.download_manager.
Position
Below the diagonal: builder uses a module listed above it, which is the healthy direction.
src.datasets uses features.features, so a change to features.features can break src.datasets, while a change to src.datasets can't break features.features.
Position
Below the diagonal: src.datasets uses a module listed above it, which is the healthy direction.
src.datasets uses formatting.formatting, so a change to formatting.formatting can break src.datasets, while a change to src.datasets can't break formatting.formatting.
Position
Below the diagonal: src.datasets uses a module listed above it, which is the healthy direction.
src.datasets uses iterable_dataset, so a change to iterable_dataset can break src.datasets, while a change to src.datasets can't break iterable_dataset.
Position
Below the diagonal: src.datasets uses a module listed above it, which is the healthy direction.
packaged_modules.cache.cache uses info, so a change to info can break packaged_modules.cache.cache, while a change to packaged_modules.cache.cache can't break info.
Position
Below the diagonal: packaged_modules.cache.cache uses a module listed above it, which is the healthy direction.
packaged_modules.cache.cache uses builder, so a change to builder can break packaged_modules.cache.cache, while a change to packaged_modules.cache.cache can't break builder.
Position
Below the diagonal: packaged_modules.cache.cache uses a module listed above it, which is the healthy direction.
packaged_modules.folder_based_builder.folder_based_builder uses builder, so a change to builder can break packaged_modules.folder_based_builder.folder_based_builder, while a change to packaged_modules.folder_based_builder.folder_based_builder can't break builder.
Position
Below the diagonal: packaged_modules.folder_based_builder.folder_based_builder uses a module listed above it, which is the healthy direction.
packaged_modules.generator.generator uses splits, so a change to splits can break packaged_modules.generator.generator, while a change to packaged_modules.generator.generator can't break splits.
Position
Below the diagonal: packaged_modules.generator.generator uses a module listed above it, which is the healthy direction.
packaged_modules.generator.generator uses builder, so a change to builder can break packaged_modules.generator.generator, while a change to packaged_modules.generator.generator can't break builder.
Position
Below the diagonal: packaged_modules.generator.generator uses a module listed above it, which is the healthy direction.
packaged_modules.spark.spark uses download.download_manager, so a change to download.download_manager can break packaged_modules.spark.spark, while a change to packaged_modules.spark.spark can't break download.download_manager.
Position
Below the diagonal: packaged_modules.spark.spark uses a module listed above it, which is the healthy direction.
packaged_modules.spark.spark uses iterable_dataset, so a change to iterable_dataset can break packaged_modules.spark.spark, while a change to packaged_modules.spark.spark can't break iterable_dataset.
Position
Below the diagonal: packaged_modules.spark.spark uses a module listed above it, which is the healthy direction.
packaged_modules.spark.spark uses builder, so a change to builder can break packaged_modules.spark.spark, while a change to packaged_modules.spark.spark can't break builder.
Position
Below the diagonal: packaged_modules.spark.spark uses a module listed above it, which is the healthy direction.
packaged_modules.tsfile.tsfile uses features.features, so a change to features.features can break packaged_modules.tsfile.tsfile, while a change to packaged_modules.tsfile.tsfile can't break features.features.
Position
Below the diagonal: packaged_modules.tsfile.tsfile uses a module listed above it, which is the healthy direction.
packaged_modules.tsfile.tsfile uses builder, so a change to builder can break packaged_modules.tsfile.tsfile, while a change to packaged_modules.tsfile.tsfile can't break builder.
Position
Below the diagonal: packaged_modules.tsfile.tsfile uses a module listed above it, which is the healthy direction.
packaged_modules.webdataset.webdataset uses info, so a change to info can break packaged_modules.webdataset.webdataset, while a change to packaged_modules.webdataset.webdataset can't break info.
Position
Below the diagonal: packaged_modules.webdataset.webdataset uses a module listed above it, which is the healthy direction.
packaged_modules.webdataset.webdataset uses builder, so a change to builder can break packaged_modules.webdataset.webdataset, while a change to packaged_modules.webdataset.webdataset can't break builder.
Position
Below the diagonal: packaged_modules.webdataset.webdataset uses a module listed above it, which is the healthy direction.
At a glance — Code Health · 70% · Adequate · gated by D2 ·
Findings mapped to OWASP categories; the specific CVEs/secrets are in the Security dimension cards below and findings.md (redacted only on the public version of this report).
OWASP category
Findings
Severity
A05:2025 — Injection
16
High / Critical
A03:2025 — Software Supply Chain Failures
8
Medium
Roadmap
Begin by establishing architectural governance by documenting significant design decisions in a dedicated ADR folder and resolving the single missing ADR instance. Simultaneously, correct documentation accuracy by fixing broken links and references in the README and source files, and update the README to clearly explain how to run the test suite. Finally, improve knowledge freshness by addressing orphaned files, specifically starting with tsfile.py and tf_utils.py.
Ranked by impact ÷ effort. "Helps" is the estimated gain on the 0–100 health score.
Do this
Helps
Effort
Dimension
Resolve the 1 No ADRs found finding(s) in ADR Quality.
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.
Reconcile the README with reality: docs/source/about_dataset_features.mdx links to `./process#flatten`, but `docs/source/process` does not exist; docs/source/about_dataset_load.mdx names `datasets.packaged_modules.csv.Csv`, but no file or folder `datasets.packaged_modules.csv.Csv` exists; docs/source/about_dataset_load.mdx names `datasets.packaged_modules.json.Json`, but no file or folder `datasets.packaged_modules.json.Json` exists; docs/source/about_dataset_load.mdx names `datasets.packaged_modules.sql.Sql`, but no file or folder `datasets.packaged_modules.sql.Sql` exists; docs/source/about_dataset_load.mdx links to `./upload_dataset`, but `docs/source/upload_dataset` does not exist.
Explain in the README how to run the tests. Add a section called Testing to the README in the repository's top folder, saying how to run the test suite.
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 — 11
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 — 390
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 — 97
Recorded, with no effect on how the codebase functions.
Present so the survey is complete, not because it needs doing.
Could not be resolved — 18
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. 52 of 56 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 0.9 — 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 — 56 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, 483 of 498 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.
D12 Dependency Hygiene — not measured this run — This is a true statement about the repository that carries nothing for its owner to act on, so it is reported here rather than as a defect in their code. This repository's 12 shipped Python distribution(s) across 1 manifest(s) WERE read; what went unanswered is the currency half, because it grades an EXACT pin against the newest release and every declaration here is a floor or a range — which installs the newest release it admits and so cannot be behind one. So pypi.org was never asked. This is a true statement about the repository, and the healthy one, NOT a gap on our side: no reader we could write would find a currency question here.
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.
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.
C3 Audit Trail — 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. 'elasticsearch' is imported (src/datasets/search.py:19), but no entity, schema, table or model declaration this check reads was found beside it, so whether personal data is stored could not be decided.
C4 Data Retention — 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. 'elasticsearch' is imported (src/datasets/search.py:19), but no entity, schema, table or model declaration this check reads was found beside it, so whether personal data is stored could not be decided.
C5 Data-Subject Rights — 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. 'elasticsearch' is imported (src/datasets/search.py:19), but no entity, schema, table or model declaration this check reads was found beside it, so whether personal data is stored could not be decided.
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.
M4 Documentation accuracy — measured, with a gap in what it reached — Watchdog measured this, but not all of it. What it did not reach is a gap on our side — a collector, parser or image we have not built yet — so the numbers on that dimension cover less than the repository, and the part left out is not evidence that it would have passed. 34 further occurrence(s) are not listed individually; the score already reflects all 74.
P5 DR & Backup — not measured this run — This is a true statement about the repository that carries nothing for its owner to act on, so it is reported here rather than as a defect in their code. No backup/snapshot/replication config, RTO/RPO or restore-procedure documentation was found — and no production persistence was detected either (no data-access packages, no data-store services, no database resources), so there is nothing in this repository whose loss a DR control would recover. If this system's data lives in a platform or ops repo we can't see, that's where the DR evidence belongs.
P8 Schema migrations — 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. The schema may be created by a runner, a deploy script or a config file outside what this check reads, so an absence here is our blind spot rather than a missing migration strategy. The card abstains instead of scoring. You can widen what we reach: if the schema is created and evolved by a tool this check does not name, naming it lets us teach the detector to read it.
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 (.gitattributes linguist-generated/vendored, .editorconfig generated_code): none declared — every source file was scored.
Limitations & what we did not check
Watchdog assesses the repository exactly as committed, and only the repository. By design it does not reach outside the source tree: the live cloud account, the running CI/CD pipeline, the host's branch-protection and approval rules, the production configuration, or a restore actually exercised against a backup are all out of scope. That boundary is a feature, not a gap — a repo-relative, deterministic scan re-runs identically on any commit and every finding opens at a real file and line, where a live audit can neither be reproduced nor traced. The visible consequence is that controls which leave no in-repo evidence are reported as "not evidenced" and excluded from the score rather than awarded a number a static scan cannot justify.
Per-dimension blind spots
For each dimension that was measured, what a static, repo-only scan structurally cannot see — the honest edge of the measurement, not a failure of it.
D1 Cyclomatic Complexity: Cyclomatic complexity counts branches statically — it cannot tell an essential decision tree from accidental tangle, nor see complexity that lives in data or configuration (large switch-case token tables, DSL lexers/parsers, data-as-code rule tables) rather than control flow: a tokenizer's many single-character cases read as high complexity though each branch is trivial.
D2 Cognitive Complexity: Cognitive-complexity heuristics approximate how hard code is to follow; genuine domain difficulty and well-named intent that eases reading are not captured.
D3 God Classes: "God class" is sized by members and responsibilities visible in the type — a deliberately broad facade over a coherent subsystem can read the same as an accidental grab-bag. For front-end JS the file-length check is cohesion-aware (a single-responsibility module — one class/IIFE — earns a 3× threshold), but cohesion is approximated from top-level declarations, not true dependency structure.
D4 Code Duplication: Duplication is token-similarity — 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.
D11 Test Reliability: Flakiness is inferred from history/markers — Watchdog runs the suite once (for coverage), not the repeated runs under varied conditions that reveal nondeterminism, so a flaky test never recorded as failing is invisible here.
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").
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.
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 Dockerfile (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.
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.
69 method(s) exceeded the cyclomatic complexity threshold of 15; the worst was features._cast_to_python_objects at 67. A further 1 method(s) were over the threshold but excluded as flat dispatchers (a long switch/match over independent cases: many branches, almost no nesting), the largest being features._arrow_to_datasets_dtype at 32 — they are counted neither in the figure above nor in this dimension's score.
+ 64 more group(s) — more in Appendix A; the complete list is findings.md.
What to do
Bring the 10 bodies over 30 down to 30 or less in Cyclomatic Complexity — start with features._cast_to_python_objects (cyclomatic 67), Dataset.map (cyclomatic 61), TypedSequence._arrow_array (cyclomatic 55). — 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 5.6 to about 6.9/10, projected with the scoring formula itself and assuming each lands exactly at 30; a cleaner split scores higher.
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.
+ 129 more group(s) — more in Appendix A; the complete list is findings.md.
What to do
Bring the 8 bodies over 60 down to 60 or less in Cognitive Complexity — start with Json._generate_tables (cognitive 202), TypedSequence._arrow_array (cognitive 99), Dataset._map_single (cognitive 96). — 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 8 together lifts Cognitive Complexity from 2.0 to about 3.6/10, projected with the scoring formula itself and assuming each lands exactly at 60; a cleaner split scores higher.
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 / 10Stronggated by 13 serious findings✓ 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 9 FileTooLong finding(s) in God Classes — start with arrow_dataset.py, iterable_dataset.py, features.py. — One of this dimension's main actionable groups (9 warning-level).
Resolve the 4 TooManyMethods finding(s) in God Classes — start with arrow_dataset.py, iterable_dataset.py, dataset_dict.py. — One of this dimension's main actionable groups (4 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.
91 duplicated block group(s) detected. A further 14 rows report members as variants of one another; they aggregate block groups already counted above and are not themselves counted.
+ 66 more group(s) — more in Appendix A; the complete list is findings.md.
What to do
Resolve the 7 Duplicated block (5 lines × 2) finding(s) in Code Duplication — start with iterable_dataset.py (2), arrow_dataset.py (2), dataset_dict.py. — One of this dimension's main actionable groups (7 warning-level).
Resolve the 6 Duplicated block (7 lines × 2) finding(s) in Code Duplication — start with parquet.py (2), arrow_dataset.py, arrow_writer.py. — One of this dimension's main actionable groups (6 warning-level).
Resolve the 5 Members sharing a duplicated core (4 members, 50+ identical tokens) finding(s) in Code Duplication — start with arrow_dataset.py, jax_formatter.py, csv.py. — One of this dimension's main actionable groups (5 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.
1675 test methods: 1675 unit, 0 integration, 0 BDD, 0 e2e. The Python suite contributes 1675 test function(s) across 80 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.
✓ On the Gold path — maintain.
Detailed fixes: d9_recommendation.md.
Do you agree with this assessment?
D11 · Test Reliability10.0 / 10Exemplary✓ Tool-verified
What it measures: Whether the tests pass reliably, with no flakiness.
Method: Suite re-run N times within tiered wall-clock budgets (unit to e2e); tests failing non-deterministically across runs flagged; guarded tests retried when #if guards detected.
What it measures: Whether 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.
0 of 28 shipped Python distribution(s) use a banned license. Licences were resolved from PyPI over the distributions a consumer installs — this repository's 12 declared runtime requirement(s) closed transitively over each distribution's published `requires_dist` (16 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. 2 of them publish no licence on PyPI this pass can read; that is missing data, not a violation, and none of them is charged.
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 3 Hotspot finding(s) in Churn × Complexity Hotspots — start with iterable_dataset.py, arrow_dataset.py, load.py. — One of this dimension's main actionable groups (3 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 Factor9.2 / 10Exemplary✓ 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.
7 source file(s) have their living knowledge concentrated in one author (≥90% of recent, decayed contribution). The largest is src/datasets/info.py. Counted over 93 of the 163 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
Further sole-owners (lower concentration)
✓ On the Gold path — maintain.
Detailed fixes: d16_recommendation.md · top locations in Appendix A, every location in findings.md.
What it measures: Acknowledged debt left in the code — TODOs, dead code, suppressed warnings.
Method: Roslyn syntactic debt markers (suppressions/TODO/FIXME/HACK/empty-catch/commented-code/Obsolete) plus SymbolFinder dead-code analysis; weighted-debt-per-KLoC density deducted 2.0x per unit. Deterministic, exhaustive.
38 deducted task-comment markers across 43983 LoC (0.1/KLoC) → score 9.8. 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.
TodoComment · ×37src/datasets/streaming.py:63
XxxCommenttests/utils.py:591
What to do
Resolve the 37 TodoComment finding(s) in Explicit Debt — start with iterable_dataset.py (6), arrow_dataset.py (4), splits.py (2). — One of this dimension's main actionable groups (37 warning-level).
Resolve the 1 XxxComment finding(s) in Explicit Debt — start with utils.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 is well documented: a root README gives an overview and links to the docs/, notebooks/, templates/, and about_arrow/ directories (each of which documents its own directory), plus a CI badge, license, and contributor covenant. The docs/README.md generates documentation for itself (installation, building, previewing) while the other docs/ subdirectories are tutorial-style guides covering dataset loading across modalities, installation, and usage. All four READMEs are well-structured with an overview, install/build steps, and a table of contents; none is missing any of the categories listed.
✓ On the Gold path — maintain.
Detailed fixes: d19_recommendation.md.
Do you agree with this assessment?
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).
16 finding(s): 0 critical, 9 high, 7 medium, 0 low. semgrep hit a parse error in 1 file(s) — `.github/conda/meta.yaml` — so no absence of findings in them is evidence of anything, and nothing in them was analysed. Fix the syntax error (or exclude the file deliberately) and re-scan to cover them. Separately, one or more rules could not re-parse an embedded snippet in 1 file(s) (e.g. a workflow `run:` block read as shell). Those files WERE scanned and their other rows are unaffected; only those rules' view of those snippets is missing.
REDACTED
REDACTED
REDACTED
REDACTED
REDACTED
+ 1 more group(s) — more in Appendix A; the complete list is findings.md.
What to do
Resolve the 7 REDACTED finding(s) in Static Analysis (SAST) — start with REDACTED (7). — One of this dimension's main actionable groups (7 issue-level).
Resolve the 1 REDACTED finding(s) in Static Analysis (SAST) — start with REDACTED. — One of this dimension's main actionable groups (1 issue-level).
Resolve the 1 REDACTED finding(s) in Static Analysis (SAST) — start with REDACTED. — One of this dimension's main actionable groups (1 issue-level).
Detailed fixes: d29_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.
18 of 93 significant source file(s) are orphaned — their living knowledge has decayed to nothing, so no one currently understands them. The largest is src/datasets/packaged_modules/tsfile/tsfile.py. Counted over 93 of the 163 production source files in this repository: the rest are under the ~2,400-byte size floor this dimension measures over.
Resolve the 2 Orphaned knowledge finding(s) in Knowledge Freshness — start with tsfile.py, tf_utils.py. — One of this dimension's main actionable groups (2 issue-level).
Resolve the 16 Orphaned file finding(s) in Knowledge Freshness — start with inspect.py, spark.py, iceberg.py. — One of this dimension's main actionable groups (16 recommendation-level).
Resolve the 1 Further orphaned files (smaller) finding(s) in Knowledge Freshness. — One of this dimension's main actionable groups (1 recommendation-level).
Detailed fixes: d34_recommendation.md · top locations in Appendix A, every location in findings.md.
What it measures: Whether files that change together actually belong together — pairs that repeatedly co-change in git history despite having no explicit code dependency, surfacing the hidden/logical coupling (and boundaries in the wrong place) a static scan can't see.
Method: Pairwise co-occurrence over the per-commit file sets in git history (production source only — tests and generated dropped): Degree-of-Coupling = shared ÷ min individual revisions, reported above noise floors (each file ≥10 revisions, ≥5 shared commits, ≥50% strength); sweeping commits excluded. Deterministic over fixed history.
Coverage: Population: PRODUCTION source files only — test and generated files are dropped before pairing, so a class co-changing with its own test (trivially ~100%) can't drown the real production↔production coupling. Pairs ranked by Degree-of-Coupling. 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 6 Change coupling finding(s) in Change Coupling — start with audio.py, setup.py, pandas.py. — One of this dimension's main actionable groups (6 warning-level).
Detailed fixes: d35_recommendation.md · top locations in Appendix A, every location in findings.md.
What it measures: Whether the build pipeline provides supply-chain integrity — generated provenance/attestation, signed artifacts (cosign/sigstore), an SBOM, and pinned build actions. Presence of the configuration, not a runtime guarantee.
Method: Supply-chain provenance/signing read deterministically from CI/build config (.github/workflows, .gitlab-ci.yml, azure-pipelines, Jenkinsfile, .circleci) + the release surface: four signals — generated provenance/attestation (SLSA/in-toto/actions-attest), artifact signing (cosign/sigstore/gitsign), an SBOM (syft/sbom-action/*.spdx.json/*.cdx.json), and SHA-pinned build actions — scored 10·present/denom. NotApplicable without a build pipeline. Detects configuration presence, not runtime enforcement.
+ 3 more group(s) — more in Appendix A; the complete list is findings.md.
What to do
Resolve the 1 REDACTED finding(s) in Supply-chain Provenance & Signing — start with REDACTED. — One of this dimension's main actionable groups (1 warning-level).
Resolve the 1 REDACTED finding(s) in Supply-chain Provenance & Signing — start with REDACTED. — One of this dimension's main actionable groups (1 warning-level).
Resolve the 1 REDACTED finding(s) in Supply-chain Provenance & Signing — start with REDACTED. — One of this dimension's main actionable groups (1 warning-level).
Detailed fixes: d36_recommendation.md · top locations in Appendix A, every location in findings.md.
What it measures: Whether the repository publishes a coordinated-vulnerability-disclosure policy (SECURITY.md or security.txt) with a reporting contact, so finders know how to report a vulnerability. Presence of a policy file with a contact, not whether the policy is adequate or honoured.
Method: Vulnerability-disclosure policy read deterministically from the repo: a SECURITY.md (root/.github/docs) or .well-known/security.txt / security.txt, regex-checked for a reporting contact (email / URL / mailto). Present + contact → 10; present without a contact → 4; NotApplicable when no policy file exists (it may live off-repo). Detects the policy file's presence + contact, not its adequacy.
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 12 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 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 the codebase has a recognisable, scale-appropriate structure (a named architectural style, or modular enough for its size) rather than being an ad-hoc ball of mud.
Method: Roslyn plus csproj analysis: architecture style detection (DDD, clean, vertical-slice, CQRS) and structure fitness for repo size. Deterministic.
Other · Architecture — Whether feature slices stay independent (no direct cross-slice references) — the discipline that makes vertical-slice architecture pay off.
Method: Roslyn scan (vertical-slice gated): feature slices resolved from namespaces (.Features.*, .Slices.*) or project names; cross-slice type references detected. Deterministic, traceable.
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
Explain in the README how to run the tests. Add a section called Testing to the README in the repository's top folder, saying how to run the test suite.
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.
No C4/Structurizr/PlantUML/Mermaid/Graphviz/D2 diagram, no drawn diagram named for the architecture, no file named `architecture` or `design` in any markup this check reads, and nothing in the README, docs or contributor guides that announces the shape — no `## Architecture` heading, no "architecture overview"/"high-level design" phrasing, no "the architecture is …" introduction, no guided code tour. A shape laid out in prose that never names itself as the architecture is not visible to this check, and neither is one kept outside the repository, so this row reports the absence of a re-findable shape document — not evidence that nobody wrote the shape down.
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.
Add a C4 context/container diagram (Structurizr, PlantUML or Mermaid) or an architecture.md overview.
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.
docs/source/about_dataset_features.mdx links to `./process#flatten`, but `docs/source/process` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong. — docs/source/about_dataset_features.mdx:49
docs/source/about_dataset_load.mdx names `datasets.packaged_modules.csv.Csv`, but no file or folder `datasets.packaged_modules.csv.Csv` exists. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong. — docs/source/about_dataset_load.mdx:21
docs/source/about_dataset_load.mdx names `datasets.packaged_modules.json.Json`, but no file or folder `datasets.packaged_modules.json.Json` exists. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong. — docs/source/about_dataset_load.mdx:22
docs/source/about_dataset_load.mdx names `datasets.packaged_modules.sql.Sql`, but no file or folder `datasets.packaged_modules.sql.Sql` exists. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong. — docs/source/about_dataset_load.mdx:25
docs/source/about_dataset_load.mdx links to `./upload_dataset`, but `docs/source/upload_dataset` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong. — docs/source/about_dataset_load.mdx:31
docs/source/about_mapstyle_vs_iterable.mdx links to `./stream`, but `docs/source/stream` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong. — docs/source/about_mapstyle_vs_iterable.mdx:39
docs/source/about_mapstyle_vs_iterable.mdx links to `./tabular_load`, but `docs/source/tabular_load` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong. — docs/source/about_mapstyle_vs_iterable.mdx:87
docs/source/about_mapstyle_vs_iterable.mdx links to `./nlp_load`, but `docs/source/nlp_load` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong. — docs/source/about_mapstyle_vs_iterable.mdx:87
docs/source/about_mapstyle_vs_iterable.mdx links to `./image_load`, but `docs/source/image_load` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong. — docs/source/about_mapstyle_vs_iterable.mdx:87
docs/source/about_mapstyle_vs_iterable.mdx links to `./audio_load]`, but `docs/source/audio_load]` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong. — docs/source/about_mapstyle_vs_iterable.mdx:87
docs/source/about_mapstyle_vs_iterable.mdx links to `./use_with_pytorch#stream-data`, but `docs/source/use_with_pytorch` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong. — docs/source/about_mapstyle_vs_iterable.mdx:237
docs/source/access.mdx links to `access/#slicing`, but `docs/source/access` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong. — docs/source/access.mdx:157
docs/source/access.mdx links to `about_mapstyle_vs_iterable`, but `docs/source/about_mapstyle_vs_iterable` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong. — docs/source/access.mdx:161
docs/source/access.mdx links to `process`, but `docs/source/process` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong. — docs/source/access.mdx:163
docs/source/access.mdx links to `stream`, but `docs/source/stream` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong. — docs/source/access.mdx:163
docs/source/audio_dataset.mdx links to `repository_structure#split-pattern-hierarchy`, but `docs/source/repository_structure` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong. — docs/source/audio_dataset.mdx:50
docs/source/audio_load.mdx links to `./installation#audio`, but `docs/source/installation` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong. — docs/source/audio_load.mdx:9
docs/source/audio_load.mdx links to `./audio_dataset`, but `docs/source/audio_dataset` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong. — docs/source/audio_load.mdx:89
docs/source/cache.mdx documents the environment variable `HF_HUB_CACHE`, but no code, script or configuration file names it. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong. — docs/source/cache.mdx:50
docs/source/cli.mdx links to `repository_structure`, but `docs/source/repository_structure` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong. — docs/source/cli.mdx:23
docs/source/create_dataset.mdx links to `stream`, but `docs/source/stream` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong. — docs/source/create_dataset.mdx:3
docs/source/create_dataset.mdx links to `upload_dataset#upload-with-the-hub-ui`, but `docs/source/upload_dataset` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong. — docs/source/create_dataset.mdx:3
docs/source/document_dataset.mdx links to `repository_structure#split-pattern-hierarchy`, but `docs/source/repository_structure` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong. — docs/source/document_dataset.mdx:13
docs/source/document_load.mdx links to `./document_dataset`, but `docs/source/document_dataset` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong. — docs/source/document_load.mdx:178
docs/source/harbor_load.mdx links to `./installation#harbor`, but `docs/source/installation` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong. — docs/source/harbor_load.mdx:30
docs/source/harbor_load.mdx names `task.toml`, but no file or folder `task.toml` exists. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong. — docs/source/harbor_load.mdx:34
docs/source/harbor_load.mdx names `instruction.md`, but no file or folder `instruction.md` exists. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong. — docs/source/harbor_load.mdx:34
docs/source/how_to.md links to `tutorial`, but `docs/source/tutorial` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong. — docs/source/how_to.md:5
docs/source/image_dataset.mdx links to `repository_structure#split-pattern-hierarchy`, but `docs/source/repository_structure` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong. — docs/source/image_dataset.mdx:17
docs/source/image_dataset.mdx links to `upload_dataset#upload-with-python`, but `docs/source/upload_dataset` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong. — docs/source/image_dataset.mdx:149
docs/source/image_load.mdx links to `./installation#vision`, but `docs/source/installation` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong. — docs/source/image_load.mdx:6
docs/source/image_load.mdx links to `./image_dataset`, but `docs/source/image_dataset` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong. — docs/source/image_load.mdx:117
docs/source/loading.mdx links to `share#create-the-repository`, but `docs/source/share` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong. — docs/source/loading.mdx:19
docs/source/loading.mdx links to `./upload_dataset`, but `docs/source/upload_dataset` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong. — docs/source/loading.mdx:38
docs/source/loading.mdx links to `tabular_load#csv-files`, but `docs/source/tabular_load` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong. — docs/source/loading.mdx:83
docs/source/loading.mdx links to `tsfile_load`, but `docs/source/tsfile_load` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong. — docs/source/loading.mdx:254
docs/source/loading.mdx names `instruction.md`, but no file or folder `instruction.md` exists. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong. — docs/source/loading.mdx:260
docs/source/loading.mdx names `task.toml`, but no file or folder `task.toml` exists. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong. — docs/source/loading.mdx:262
docs/source/loading.mdx links to `harbor_load`, but `docs/source/harbor_load` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong. — docs/source/loading.mdx:273
docs/source/loading.mdx names `metadata.csv`, but no file or folder `metadata.csv` exists. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong. — docs/source/loading.mdx:280
What to do
Reconcile the README with reality: docs/source/about_dataset_features.mdx links to `./process#flatten`, but `docs/source/process` does not exist; docs/source/about_dataset_load.mdx names `datasets.packaged_modules.csv.Csv`, but no file or folder `datasets.packaged_modules.csv.Csv` exists; docs/source/about_dataset_load.mdx names `datasets.packaged_modules.json.Json`, but no file or folder `datasets.packaged_modules.json.Json` exists; docs/source/about_dataset_load.mdx names `datasets.packaged_modules.sql.Sql`, but no file or folder `datasets.packaged_modules.sql.Sql` exists; docs/source/about_dataset_load.mdx links to `./upload_dataset`, but `docs/source/upload_dataset` does not exist.
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.
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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.
Do you agree with this assessment?
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 10510 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.
Dependabot is configured but does not watch `pip` — add that `package-ecosystem` entry to .github/dependabot.yml so those dependencies get the same automatic update and advisory pressure as the ones it already covers.
Readiness · Readiness — Whether releases are traceable — a maintained changelog and explicit version stamping.
Method: Filesystem scan: changelog file presence and version tags in csproj or git tags. Exhaustive, deterministic.
No CHANGELOG/HISTORY/RELEASES file — what shipped when isn't easy to reconstruct for support or audit. (Versioning/tagging makes releases traceable, but a changelog records the what.)
What to do
Keep a changelog (e.g. Keep-a-Changelog) recording what shipped in each release.
Readiness · Performance — Whether the code protects its performance with benchmarks — a benchmark suite, allocation/memory measurement, and (ideally) a CI gate. Presence is credited as a bonus, never a deduction.
Method: Repo + source scan: BenchmarkDotNet referenced (csproj/source), [Benchmark]/[MemoryDiagnoser] attribute counts, and a benchmark step in CI; off .NET, the same ladder over Go testing.B, Rust criterion/#[bench]/divan, JMH/kotlinx-benchmark, pytest-benchmark/asv/pyperf, tinybench/mitata/vitest bench/benchmark.js and Swift package-benchmark — scored as a bonus ladder (absence is neutral, never a deduction). Deterministic, presence detection.
Readiness · Performance — Whether asynchronous code stays responsive — it avoids sync-over-async blocking (a .NET .Wait()/.GetAwaiter().GetResult(), a time.sleep or blocking HTTP call inside a Python coroutine, a *Sync call inside an async JavaScript function, block_on inside a Rust async fn, runBlocking inside a Kotlin suspend function, block() inside a Reactor publisher) that stalls a thread or event loop and risks deadlock, and, where the code is a reusable library on .NET, awaits with ConfigureAwait(false) so it never captures and stalls its caller's context.
Method: Production-source scan: sync-over-async blocking counted everywhere — .Wait()/.GetAwaiter().GetResult() in .NET; off .NET, read from the language model, a blocking call inside an async function (Python, TS/JS, Rust, Kotlin, Swift, Dart) or inside a Java method returning a Reactor Mono/Flux or a Scala method returning a Future or effect — and, for a .NET library with ≥5 awaits, the share of awaits using ConfigureAwait(false). Deterministic, syntax/text detection.
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.
Do you agree with this assessment?
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 document3.0 / 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.
`generate_from_dict` is public and takes `obj: Any` from its caller, and it calls itself at line 1645 (`generate_from_dict`) with a piece of `obj` (and at line 1648, line 1658, line 1661, line 1664) — and nowhere in `generate_from_dict` 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 `generate_from_dict` and reject the value past a fixed maximum, or rewrite the descent with an explicit stack. — src/datasets/features/features.py:1645
`json_encode_field` is public and takes `example: Any` from its caller, and it calls itself at line 32 (`json_encode_field`) with a piece of `example` (and at line 34) — and nowhere in `json_encode_field` 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 `json_encode_field` and reject the value past a fixed maximum, or rewrite the descent with an explicit stack. — src/datasets/utils/json.py:32
`json_decode_field` is public and takes `example: Any` from its caller, and it calls itself at line 53 (`json_decode_field`) with a piece of `example` (and at line 55) — and nowhere in `json_decode_field` 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 `json_decode_field` and reject the value past a fixed maximum, or rewrite the descent with an explicit stack. — src/datasets/utils/json.py:53
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.
`debug` at line 1037 is called with an argument built by `"…".join(self.info.splits)` (line 1037), whose cost grows with the collection it walks. Python evaluates a call's arguments BEFORE entering the method, so that join/projection runs in full every time this line is reached — and then the level check inside the logger discards the string it produced, because debug is a level a shipped configuration normally leaves off. Nothing here decides beforehand whether the message is wanted: no `isEnabledFor(logging.DEBUG)` test, no debug flag, no `lambda` the logger calls only when the level is on. — src/datasets/builder.py:1037
What to do
In Python, put the construction behind the logger's own check (`if logger.isEnabledFor(logging.DEBUG):`), or pass the collection itself with a `%s` placeholder (`logger.debug("Items %s", items)`) so the logging module renders it only when the record is emitted. The placeholder alone is not the fix when the argument is still built first: it defers the formatting, not a `json.dumps`, a `join` or a comprehension in the argument, and an f-string defers nothing at all.
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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. (×4) — src/datasets/load.py:198, src/datasets/download/download_manager.py:200, src/datasets/packaged_modules/tsfile/tsfile.py:420, …
`except Exception` takes every exception and records none of it — the body neither logs it, rethrows it, nor even names it, so the failure is discarded as completely as by an empty catch and only the substituted value survives. Log it through whatever this codebase already uses to report problems, narrow the catch to the exception this call can actually raise, or say in a comment on the catch why the failure genuinely cannot matter. (×3) — src/datasets/builder.py:1665, src/datasets/utils/json.py:61, src/datasets/utils/_dill.py:276
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.
`warning` at line 59 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.warning("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.warning("order moved", order_id=order_id, status=status)` with structlog. — src/datasets/utils/logging.py:59
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 — 2 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.
X10 Duplicated predicate — 25 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 evidenced — 5 control(s) we could not find positive evidence for
These checks grade a working control, and the repository shows no evidence of one. That is deliberately not scored as a zero: a repository cannot show an ops runbook, a database TTL or an infrastructure-side audit log, so absence of evidence here is not evidence the control is missing. It is also not a statement that the check is irrelevant to this codebase — the thing it grades applies; we just could not see it. Excluded from the score either way.
C3 Audit Trail — Not assessed: this repository stores data through 'elasticsearch' (src/datasets/search.py:19) but declares no persisted model this check reads (Django, SQLAlchemy, SQLModel, Peewee, Tortoise and MongoEngine model fields, Alembic and Django migration columns, CREATE TABLE and ALTER TABLE … ADD columns in SQL files). Personal data may be written through raw SQL or a hosted backend, so whether its changes need an audit trail could not be established, and the trail's absence is not scored.
C4 Data Retention — Not assessed: this repository stores data through 'elasticsearch' (src/datasets/search.py:19) but declares no persisted model this check reads (Django, SQLAlchemy, SQLModel, Peewee, Tortoise and mongoengine model fields, Alembic and Django migration columns, SQL schemas). Personal data may be written through raw SQL or a hosted backend, so whether a retention limit applies here could not be established, and its absence is not scored.
C5 Data-Subject Rights — Not assessed: this repository stores data through 'elasticsearch' (src/datasets/search.py:19) but declares no persisted model this check reads (Django, SQLAlchemy, SQLModel, Peewee, Tortoise and mongoengine model fields, Alembic and Django migration columns, CREATE TABLE and ALTER TABLE … ADD columns in SQL files). Personal data may be written through raw SQL or a hosted backend, so whether data-subject rights apply here could not be established, and their absence is not scored.
P4 Deployment & Rollback — not evidenced — no deploy/rollback/approval signal in the repo; absence of evidence is not evidence of a manual release
P5 DR & Backup — not evidenced — repo shows no backup/RTO/RPO controls; absence of evidence is not evidence of a working control
Not included — 57 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.
AX2 Stateful singletons — No container singleton was found, so there is no shared instance for concurrent requests to race on. No function in this Python code is served by a threaded web framework (a Flask, Bottle or FastAPI route, a Django or Pyramid view), so no module is shared between request threads.
AX4 Dependency direction — not applicable to a vertical-slice architecture (the inward-dependency rule is for layered/clean styles)
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
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
AXB1 Runtime evidence locked — no reproducible boot — This repository has nothing the runtime tiers could boot or serve — no markup, no UI framework or web-server dependency, no UI component source, no native UI project and no API definition — nothing here is a surface to boot — so runtime a11y/egress/header evidence has no subject here. Not applicable: this is neither a gap in the scan nor a finding about your code.
AXB2 Runtime readiness — Advisory — this card reports evidence and never carries a score, so there is nothing missing here.
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.
C2 Access Controls — No access-control surface detected in the analyzed source — this repository registers no route and starts no server another machine can reach (no Flask/FastAPI/Starlette route, Django urlpatterns or class-based view, Tornado handler). Access control is therefore N/A here — a library or command-line tool is authorized by the application that serves it, not by itself. If this codebase grows request handlers, the dimension reactivates and a default-deny posture is expected then.
D10 Test Quality — ~32950 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.
D12 Dependency Hygiene — Dependency currency not established — no declaration carries an exact pin to grade
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.
D30 Dependency Vulnerabilities — Not scored — no dependency manifest in a supported ecosystem was read for this repository. A gap in the analyzer's language coverage, NOT a finding that the repository is free of vulnerable dependencies (a Python pyproject.toml/requirements.txt (pip/uv/Poetry) — not scanned yet).
D31 IaC & Container Security — No Infrastructure-as-Code or container manifests found (Dockerfile, Docker Compose, Terraform, Kubernetes/Helm, CloudFormation, ARM, Bicep, Ansible); nothing to scan.
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.
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.
D43 Malicious Dependencies — Not scored — no dependency manifest in a supported ecosystem was read for this repository. A gap in the analyzer's language coverage, NOT a finding that the repository is free of vulnerable dependencies (a Python pyproject.toml/requirements.txt (pip/uv/Poetry) — not scanned yet).
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 (40 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'.
P7 Outbound HTTP resilience — not applicable — no HTTP server, API framework or worker entry point was found in the Python source, so there is no service whose uptime a failing dependency could take down
P8 Schema migrations — SQLAlchemy is declared, but the schema-migration mechanism could not be identified. Our limit, not a verdict on this repository.
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
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.
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.
Orphaned knowledge src/datasets/packaged_modules/tsfile/tsfile.py:1— No living knowledge remains for this large file — its last meaningful change has decayed away; if it breaks, no one currently understands it. Schedule a read-through / add characterisation tests before it bites.
Orphaned knowledge src/datasets/utils/tf_utils.py:1— No living knowledge remains for this large file — its last meaningful change has decayed away; if it breaks, no one currently understands it. Schedule a read-through / add characterisation tests before it bites.
TodoComment src/datasets/streaming.py:63— # TODO(QL): always update the module to add subsequent new authentication without removing old ones — 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.
TodoComment src/datasets/splits.py:499— # TODO(epot): Check that the number of examples per shard % 100 == 0 — 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.
TodoComment src/datasets/splits.py:507— # TODO(epot): If a split is already added but there is no overlap between — 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.
TodoComment src/datasets/iterable_dataset.py:3664— # TODO(QL): add format_kwargs — 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.
TodoComment src/datasets/iterable_dataset.py:3665— # TODO(QL): add format_columns and return_all_columns — 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.
TodoComment src/datasets/iterable_dataset.py:3666— # TODO(QL): add pandas format — 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.
TodoComment src/datasets/iterable_dataset.py:5117— # max_shard_size: Optional[Union[int, str]] = None, # TODO(QL): add arg — 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.
TodoComment src/datasets/iterable_dataset.py:5570— # TODO: improve this to account for a mix of ClassLabel and Value for example — 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.
TodoComment src/datasets/iterable_dataset.py:5651— # TODO: improve this to account for a mix of ClassLabel and Value for example — 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.
TodoComment src/datasets/arrow_dataset.py:457— # TODO(Matt, QL): deprecate the retention of label_ids and label — 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.
TodoComment src/datasets/arrow_dataset.py:516— # TODO(Matt, QL): deprecate returning the dict content when there's only one key — 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.
TodoComment src/datasets/arrow_dataset.py:3514— # TODO: this assumes the format_spec of rank in suffix_template — 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.
TodoComment src/datasets/arrow_dataset.py:3830— # TODO(QL, MS): ideally the behavior should be the same even if the dataset is formatted (may require major release) — 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.
TodoComment src/datasets/formatting/torch_formatter.py:84— # TODO(QL): set output to torch tensors ? — 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.
TodoComment src/datasets/formatting/torch_formatter.py:91— # TODO(QL): set output to jax arrays ? — 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.
TodoComment src/datasets/formatting/tf_formatter.py:85— # TODO(QL): set output to jax arrays ? — 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.
TodoComment src/datasets/formatting/jax_formatter.py:114— # TODO(QL): set output to jax arrays ? — 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.
TodoComment src/datasets/formatting/jax_formatter.py:121— # TODO(QL): set output to jax arrays ? — 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.
TodoComment src/datasets/formatting/tf_formatter.py:78— # TODO(QL): set output to tf tensors ? — 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.
TodoComment src/datasets/formatting/np_formatter.py:74— # TODO(QL): set output to np arrays ? — 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.
TodoComment src/datasets/formatting/np_formatter.py:81— # TODO(QL): set output to np arrays ? — 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.
TodoComment src/datasets/io/parquet.py:148— # TODO(kszucs): we may want to persist multiple parameters — 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.
TodoComment src/datasets/packaged_modules/lance/lance.py:115— # TODO: remove once Lance supports HF links with revisions — 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.
TodoComment src/datasets/packaged_modules/lance/lance.py:117— # TODO: remove once Lance supports symlinks for _version files — 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.
TodoComment src/datasets/packaged_modules/spark/spark.py:156— # TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error. — 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.
FileTooLong: datasets/arrow_dataset.py src/datasets/arrow_dataset.py— FileTooLong — 3666 significant lines (blank, comment-only and punctuation-only lines excluded). The bar is 500 significant lines; this is 3166 over it, 7.33× 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: datasets/iterable_dataset.py src/datasets/iterable_dataset.py— FileTooLong — 3366 significant lines (blank, comment-only and punctuation-only lines excluded). The bar is 500 significant lines; this is 2866 over it, 6.73× 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: features/features.py src/datasets/features/features.py— FileTooLong — 1426 significant lines (blank, comment-only and punctuation-only lines excluded). The bar is 500 significant lines; this is 926 over it, 2.85× 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: datasets/builder.py src/datasets/builder.py— FileTooLong — 1145 significant lines (blank, comment-only and punctuation-only lines excluded). The bar is 500 significant lines; this is 645 over it, 2.29× 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: datasets/load.py src/datasets/load.py— FileTooLong — 1072 significant lines (blank, comment-only and punctuation-only lines excluded). The bar is 500 significant lines; this is 572 over it, 2.14× 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: datasets/dataset_dict.py src/datasets/dataset_dict.py— FileTooLong — 976 significant lines (blank, comment-only and punctuation-only lines excluded). The bar is 500 significant lines; this is 476 over it, 1.95× 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: datasets/table.py src/datasets/table.py— FileTooLong — 972 significant lines (blank, comment-only and punctuation-only lines excluded). The bar is 500 significant lines; this is 472 over it, 1.94× 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: REDACTED REDACTED— FileTooLong — 856 significant lines (blank, comment-only and punctuation-only lines excluded). The bar is 500 significant lines; this is 356 over it, 1.71× 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: datasets/arrow_writer.py src/datasets/arrow_writer.py— FileTooLong — 591 significant lines (blank, comment-only and punctuation-only lines excluded). The bar is 500 significant lines; this is 91 over it, 1.18× 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.
Duplicated block (5 lines × 2) src/datasets/iterable_dataset.py:830— src/datasets/iterable_dataset.py:830-834 | src/datasets/iterable_dataset.py:905-909 — 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 (5 lines × 2) src/datasets/arrow_dataset.py:620— src/datasets/arrow_dataset.py:620-624 | src/datasets/iterable_dataset.py:1111-1117 — 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. 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 (5 lines × 2) src/datasets/arrow_dataset.py:6833— src/datasets/arrow_dataset.py:6833-6837 | src/datasets/dataset_dict.py:2700-2704 — before extracting anything, compare `src/datasets/arrow_dataset.py` and `src/datasets/dataset_dict.py` as WHOLE FILES: this scan already matched 6 separate duplicated blocks between them, totalling at least 181 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 (5 lines × 2) src/datasets/dataset_dict.py:63— src/datasets/dataset_dict.py:63-67 | src/datasets/dataset_dict.py:1818-1822 — 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 (5 lines × 2) src/datasets/iterable_dataset.py:415— src/datasets/iterable_dataset.py:415-419 | src/datasets/iterable_dataset.py:436-440 — 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 (5 lines × 2) src/datasets/packaged_modules/parquet/parquet.py:186— src/datasets/packaged_modules/parquet/parquet.py:186-190 | src/datasets/packaged_modules/vortex/vortex.py:346-350 — 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.
Duplicated block (5 lines × 2) src/datasets/table.py:2139— src/datasets/table.py:2139-2143 | src/datasets/table.py:2235-2239 — 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.
Change coupling: audio.py ↔ image.py src/datasets/features/audio.py:1— `src/datasets/features/audio.py` and `src/datasets/features/image.py` change together 70% of the time (21 of the 30 commits that touched whichever of the two files changed less often, counting a file under its earlier names as well — a repo-wide or module-wide sweep is evidence about the sweep rather than about any pair inside it and is left out of BOTH sides of this ratio, while a dependency bump, a formatter/rename sweep, or a commit whose edit to one of the two files was a tool directive such as //go:generate or whitespace only is left out of the shared count ONLY, so the two sides are not taken over identical commit sets) with no explicit dependency between them. They sit in the same directory, but in this ecosystem each file is its own module — a sibling reference still needs an import — so the missing import edge is real: the coupling runs through shared behaviour, not a declared dependency. If they duplicate structure, extract the common part into one unit; otherwise the coupling is hidden and worth breaking. You can check this without leaving the row: of the 21 shared commits counted here, the most recent 3 are `f15c5997` Infer types from lance blobs (#7966); `072eb549` Support pathlib.Path for feature input (#7755); `11320c3f` Add `IterableDataset.push_to_hub()` (#7595) — run `git show` on any of them.
Change coupling: setup.py ↔ __init__.py setup.py:1— `setup.py` and `src/datasets/features/__init__.py` change together 67% of the time (8 of the 12 commits that touched whichever of the two files changed less often, counting a file under its earlier names as well — a repo-wide or module-wide sweep is evidence about the sweep rather than about any pair inside it and is left out of BOTH sides of this ratio, while a dependency bump, a formatter/rename sweep, or a commit whose edit to one of the two files was a tool directive such as //go:generate or whitespace only is left out of the shared count ONLY, so the two sides are not taken over identical commit sets) with no explicit dependency — the edge is real but nothing declares it. Read the pair before acting: if one registers itself into the other through a hook or an initialiser, the missing dependency is DELIBERATE — the registration is the link, and it is meant not to be an import — and the thing to add is a comment on each side naming the other, not a merge; if they simply belong together, co-locate them; if neither holds, the coupling is hidden and worth breaking. You can check this without leaving the row: of the 8 shared commits counted here, the most recent 3 are `c75df53d` feat(features): add BioSequence and BioStructure decodable features (…; `d4284e94` feat: add 3D mesh support and MeshFolder builder (#8055); `51388765` Add nifti support (#7815) — run `git show` on any of them.
Change coupling: pandas.py ↔ text.py src/datasets/packaged_modules/pandas/pandas.py:1— `src/datasets/packaged_modules/pandas/pandas.py` and `src/datasets/packaged_modules/text/text.py` change together 67% of the time (8 of the 12 commits that touched whichever of the two files changed less often, counting a file under its earlier names as well — a repo-wide or module-wide sweep is evidence about the sweep rather than about any pair inside it and is left out of BOTH sides of this ratio, while a dependency bump, a formatter/rename sweep, or a commit whose edit to one of the two files was a tool directive such as //go:generate or whitespace only is left out of the shared count ONLY, so the two sides are not taken over identical commit sets) with no explicit dependency — the edge is real but nothing declares it. Read the pair before acting: if one registers itself into the other through a hook or an initialiser, the missing dependency is DELIBERATE — the registration is the link, and it is meant not to be an import — and the thing to add is a comment on each side naming the other, not a merge; if they simply belong together, co-locate them; if neither holds, the coupling is hidden and worth breaking. You can check this without leaving the row: of the 8 shared commits counted here, the most recent 3 are `dc98f979` Add _generate_shards (#7943); `5bbbf1b1` Validate config name and data_files in packaged modules (#6915); `7feeb564` Unpack `dl_manager.iter_files` to allow parallization (#4625) — run `git show` on any of them.
Change coupling: config.py ↔ __init__.py src/datasets/config.py:1— `src/datasets/config.py` and `src/datasets/features/__init__.py` change together 50% of the time (6 of the 12 commits that touched whichever of the two files changed less often, counting a file under its earlier names as well — a repo-wide or module-wide sweep is evidence about the sweep rather than about any pair inside it and is left out of BOTH sides of this ratio, while a dependency bump, a formatter/rename sweep, or a commit whose edit to one of the two files was a tool directive such as //go:generate or whitespace only is left out of the shared count ONLY, so the two sides are not taken over identical commit sets) with no explicit dependency — the edge is real but nothing declares it. Read the pair before acting: if one registers itself into the other through a hook or an initialiser, the missing dependency is DELIBERATE — the registration is the link, and it is meant not to be an import — and the thing to add is a comment on each side naming the other, not a merge; if they simply belong together, co-locate them; if neither holds, the coupling is hidden and worth breaking. You can check this without leaving the row: of the 6 shared commits counted here, the most recent 3 are `c75df53d` feat(features): add BioSequence and BioStructure decodable features (…; `d4284e94` feat: add 3D mesh support and MeshFolder builder (#8055); `51388765` Add nifti support (#7815) — run `git show` on any of them.
Change coupling: arrow_dataset.py ↔ hub.py src/datasets/arrow_dataset.py:1— `src/datasets/arrow_dataset.py` and `src/datasets/utils/hub.py` change together 50% of the time (5 of the 10 commits that touched whichever of the two files changed less often, counting a file under its earlier names as well — a repo-wide or module-wide sweep is evidence about the sweep rather than about any pair inside it and is left out of BOTH sides of this ratio, while a dependency bump, a formatter/rename sweep, or a commit whose edit to one of the two files was a tool directive such as //go:generate or whitespace only is left out of the shared count ONLY, so the two sides are not taken over identical commit sets) with no explicit dependency — the edge is real but nothing declares it. Read the pair before acting: if one registers itself into the other through a hook or an initialiser, the missing dependency is DELIBERATE — the registration is the link, and it is meant not to be an import — and the thing to add is a comment on each side naming the other, not a merge; if they simply belong together, co-locate them; if neither holds, the coupling is hidden and worth breaking. You can check this without leaving the row: of the 5 shared commits counted here, the most recent 3 are `a0c786c7` Support huggingface_hub v0.x and v1.x (#7783); `66d62426` Remove deprecated code (#6761); `3f699ab2` Replace `list_files_info` with `list_repo_tree` in `push_to_hub` (#6510) — run `git show` on any of them.
Change coupling: csv.py ↔ json.py src/datasets/io/csv.py:1— `src/datasets/io/csv.py` and `src/datasets/io/json.py` change together 50% of the time (5 of the 10 commits that touched whichever of the two files changed less often, counting a file under its earlier names as well — a repo-wide or module-wide sweep is evidence about the sweep rather than about any pair inside it and is left out of BOTH sides of this ratio, while a dependency bump, a formatter/rename sweep, or a commit whose edit to one of the two files was a tool directive such as //go:generate or whitespace only is left out of the shared count ONLY, so the two sides are not taken over identical commit sets) with no explicit dependency between them. They sit in the same directory, but in this ecosystem each file is its own module — a sibling reference still needs an import — so the missing import edge is real: the coupling runs through shared behaviour, not a declared dependency. If they duplicate structure, extract the common part into one unit; otherwise the coupling is hidden and worth breaking. You can check this without leaving the row: of the 5 shared commits counted here, the most recent 3 are `c6fc5cdb` Preserve nullable integer columns in to_json/to_csv/to_sql (#8366); `e52f4d0c` Add `fsspec` support for `to_json`, `to_csv`, and `to_parquet` (#6096); `b3193829` Better `tqdm` wrapper (#6433) — run `git show` on any of them.
Duplicated block (7 lines × 2) src/datasets/arrow_dataset.py:1903— src/datasets/arrow_dataset.py:1903-1910 | src/datasets/arrow_dataset.py:1915-1921 — 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 `src/datasets/arrow_dataset.py:1903` 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 (7 lines × 2) src/datasets/arrow_writer.py:414— src/datasets/arrow_writer.py:414-422 | src/datasets/arrow_writer.py:435-441 — 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 (7 lines × 2) src/datasets/load.py:428— src/datasets/load.py:428-434 | src/datasets/load.py:874-880 — 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 (7 lines × 2) src/datasets/packaged_modules/spark/spark.py:230— src/datasets/packaged_modules/spark/spark.py:230-236 | src/datasets/packaged_modules/spark/spark.py:248-254 — 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 `src/datasets/packaged_modules/spark/spark.py:230` 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 (7 lines × 2) src/datasets/packaged_modules/parquet/parquet.py:130— src/datasets/packaged_modules/parquet/parquet.py:130-136 | src/datasets/packaged_modules/parquet/parquet.py:228-234 — 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 (7 lines × 2) src/datasets/packaged_modules/parquet/parquet.py:144— src/datasets/packaged_modules/parquet/parquet.py:144-150 | src/datasets/packaged_modules/vortex/vortex.py:286-292 — the copies span different directories, so extracting a shared function means choosing where it lives: put it somewhere both call sites can already reach — a location they all depend on today, or a new shared one if there is none — and call it from each site; until then, every change has to be made twice. Read the line range as the matched WINDOW rather than a finished unit: at `src/datasets/packaged_modules/parquet/parquet.py:144` 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.
D4 · Code Duplication· Members sharing a duplicated core (4 members, 50+ identical tokens) · ×5
Members sharing a duplicated core (4 members, 50+ identical tokens) src/datasets/arrow_dataset.py:873— src/datasets/arrow_dataset.py:873-932 | src/datasets/arrow_dataset.py:942-980 | src/datasets/arrow_dataset.py:991-1146 | src/datasets/iterable_dataset.py:3273-3314 — These 4 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 4 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 4 times.
Members sharing a duplicated core (4 members, 50+ identical tokens) src/datasets/formatting/jax_formatter.py:84— src/datasets/formatting/jax_formatter.py:84-132 | src/datasets/formatting/np_formatter.py:50-83 | src/datasets/formatting/tf_formatter.py:56-87 | src/datasets/formatting/torch_formatter.py:50-93 — These 4 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 4 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 4 times.
Members sharing a duplicated core (4 members, 50+ identical tokens) src/datasets/io/csv.py:47— src/datasets/io/csv.py:47-64 | src/datasets/io/json.py:53-70 | src/datasets/io/parquet.py:56-73 | src/datasets/io/text.py:41-58 — These 4 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 4 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 4 times.
Members sharing a duplicated core (4 members, 50+ identical tokens) src/datasets/packaged_modules/fasta/fasta.py:85— src/datasets/packaged_modules/fasta/fasta.py:85-103 | src/datasets/packaged_modules/fastq/fastq.py:85-103 | src/datasets/packaged_modules/genbank/genbank.py:245-263 | src/datasets/packaged_modules/xml/xml.py:30-45 — These 4 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 4 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 4 times.
Members sharing a duplicated core (4 members, 50+ identical tokens) src/datasets/utils/extract.py:93— src/datasets/utils/extract.py:93-129 | src/datasets/utils/extract.py:195-227 | src/datasets/utils/extract.py:252-289 | src/datasets/utils/extract.py:332-369 — These 4 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 4 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 4 times.
Duplicated block (10 lines × 2) src/datasets/formatting/formatting.py:258— src/datasets/formatting/formatting.py:258-267 | src/datasets/formatting/polars_formatter.py:73-82 — 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 (10 lines × 2) src/datasets/formatting/formatting.py:327— src/datasets/formatting/formatting.py:327-336 | src/datasets/formatting/formatting.py:342-351 — 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 (10 lines × 2) src/datasets/packaged_modules/csv/csv.py:184— src/datasets/packaged_modules/csv/csv.py:184-193 | src/datasets/packaged_modules/folder_based_builder/folder_based_builder.py:300-309 — 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.
Duplicated block (10 lines × 2) src/datasets/packaged_modules/text/text.py:75— src/datasets/packaged_modules/text/text.py:75-84 | src/datasets/packaged_modules/xml/xml.py:49-58 — 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 (10 lines × 2) src/datasets/packaged_modules/fasta/fasta.py:106— src/datasets/packaged_modules/fasta/fasta.py:106-115 | src/datasets/packaged_modules/fastq/fastq.py:106-115 — `src/datasets/packaged_modules/fasta/fasta.py` and `src/datasets/packaged_modules/fastq/fastq.py` are one unit implemented once per sibling directory, so they are most likely parallel implementations of one contract rather than a copy of each other — this scan matched 10 separate duplicated blocks between them, totalling at least 157 lines. If both are selected at run time, neither can be retired in favour of the other, and the lines that DIFFER between them are the reason both exist. The move that pays here is to hoist the identical part into a shared location the whole family can reach and give what differs a parameter or a seam, so a change lands once instead of once per sibling; extracting one helper per block leaves every sibling to drift on its own. 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.
TooManyMethods: Dataset src/datasets/arrow_dataset.py:709— TooManyMethods — 87 methods. The bar is 30 methods; this is 57 over it, 2.90× the bar. To reduce it, group the members that share the same data into a smaller type of their own and delegate to it, so no single type carries every responsibility.
TooManyMethods: IterableDataset src/datasets/iterable_dataset.py:2690— TooManyMethods — 58 methods. The bar is 30 methods; this is 28 over it, 1.93× the bar. To reduce it, group the members that share the same data into a smaller type of their own and delegate to it, so no single type carries every responsibility.
TooManyMethods: DatasetDict src/datasets/dataset_dict.py:53— TooManyMethods — 35 methods. The bar is 30 methods; this is 5 over it, 1.17× the bar. To reduce it, group the members that share the same data into a smaller type of their own and delegate to it, so no single type carries every responsibility.
TooManyMethods: Table src/datasets/table.py:210— TooManyMethods — 31 methods. The bar is 30 methods; this is 1 over it, 1.03× the bar. To reduce it, group the members that share the same data into a smaller type of their own and delegate to it, so no single type carries every responsibility.
Duplicated block (9 lines × 2) src/datasets/arrow_dataset.py:4024— src/datasets/arrow_dataset.py:4024-4032 | src/datasets/iterable_dataset.py:1668-1676 — 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 (9 lines × 2) src/datasets/io/csv.py:77— src/datasets/io/csv.py:77-85 | src/datasets/io/json.py:83-91 — before extracting anything, compare `src/datasets/io/csv.py` and `src/datasets/io/json.py` as WHOLE FILES: this scan already matched 3 separate duplicated blocks between them, totalling at least 36 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 (9 lines × 2) src/datasets/io/csv.py:135— src/datasets/io/csv.py:135-143 | src/datasets/io/json.py:182-190 — before extracting anything, compare `src/datasets/io/csv.py` and `src/datasets/io/json.py` as WHOLE FILES: this scan already matched 3 separate duplicated blocks between them, totalling at least 36 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 `src/datasets/io/csv.py:135` 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 (9 lines × 2) src/datasets/packaged_modules/arrow/arrow.py:32— src/datasets/packaged_modules/arrow/arrow.py:32-40 | src/datasets/packaged_modules/hdf5/hdf5.py:49-57 — 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.
Swallowed exception (empty catch) src/datasets/load.py:198— 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.
Swallowed exception (empty catch) src/datasets/download/download_manager.py:200— 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.
Swallowed exception (empty catch) src/datasets/packaged_modules/tsfile/tsfile.py:420— 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.
Swallowed exception (empty catch) src/datasets/utils/_dill.py:288— 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.
Hotspot: src/datasets/iterable_dataset.py src/datasets/iterable_dataset.py:2958— src/datasets/iterable_dataset.py changed 26 times in last 90 days, and the most complex body those changes touched has cyclomatic complexity 22 in IterableDataset._prepare_ex_iterable_for_iteration at line 2958. 12 of those changes were fix/bug commits, and the other 14 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 20:07:31 +02:00' --until='2026-10-19 20:07:31 +02:00' --full-history --no-merges -- src/datasets/iterable_dataset.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: src/datasets/arrow_dataset.py src/datasets/arrow_dataset.py:6887— src/datasets/arrow_dataset.py changed 10 times in last 90 days, and the most complex body those changes touched has cyclomatic complexity 34 in arrow_dataset._get_updated_dataset_card at line 6887. 5 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 20:07:31 +02:00' --until='2026-10-19 20:07:31 +02:00' --full-history --no-merges -- src/datasets/arrow_dataset.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: src/datasets/load.py src/datasets/load.py:857— src/datasets/load.py changed 6 times in last 90 days, and the most complex body those changes touched has cyclomatic complexity 15 in HubBucketDatasetModuleFactory.get_module at line 857. 4 of those changes were fix/bug commits, so the churn is repair rather than feature work. 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 20:07:31 +02:00' --until='2026-10-19 20:07:31 +02:00' --full-history --no-merges -- src/datasets/load.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 (12 lines × 2) src/datasets/arrow_dataset.py:5934— src/datasets/arrow_dataset.py:5934-5945 | src/datasets/iterable_dataset.py:5166-5177 — 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 `src/datasets/arrow_dataset.py:5934` 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 (12 lines × 2) src/datasets/features/image.py:113— src/datasets/features/image.py:113-124 | src/datasets/features/video.py:123-134 — before extracting anything, compare `src/datasets/features/image.py` and `src/datasets/features/video.py` as WHOLE FILES: this scan already matched 8 separate duplicated blocks between them, totalling at least 93 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 (12 lines × 2) src/datasets/iterable_dataset.py:4087— src/datasets/iterable_dataset.py:4087-4098 | src/datasets/iterable_dataset.py:4166-4177 — 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 (8 lines × 2) src/datasets/formatting/jax_formatter.py:87— src/datasets/formatting/jax_formatter.py:87-94 | src/datasets/formatting/torch_formatter.py:52-59 — before extracting anything, compare `src/datasets/formatting/jax_formatter.py` and `src/datasets/formatting/torch_formatter.py` as WHOLE FILES: this scan already matched 5 separate duplicated blocks between them, totalling at least 38 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 (8 lines × 2) src/datasets/packaged_modules/csv/csv.py:171— src/datasets/packaged_modules/csv/csv.py:171-178 | src/datasets/packaged_modules/sql/sql.py:103-110 — 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 (8 lines × 2) src/datasets/info.py:327— src/datasets/info.py:327-334 | src/datasets/splits.py:59-66 — 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. 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 (6 lines × 2) src/datasets/arrow_dataset.py:7420— src/datasets/arrow_dataset.py:7420-7425 | src/datasets/arrow_dataset.py:7479-7484 — 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 (6 lines × 2) src/datasets/packaged_modules/fasta/fasta.py:211— src/datasets/packaged_modules/fasta/fasta.py:211-216 | src/datasets/packaged_modules/fastq/fastq.py:237-242 — `src/datasets/packaged_modules/fasta/fasta.py` and `src/datasets/packaged_modules/fastq/fastq.py` are one unit implemented once per sibling directory, so they are most likely parallel implementations of one contract rather than a copy of each other — this scan matched 10 separate duplicated blocks between them, totalling at least 157 lines. If both are selected at run time, neither can be retired in favour of the other, and the lines that DIFFER between them are the reason both exist. The move that pays here is to hoist the identical part into a shared location the whole family can reach and give what differs a parameter or a seam, so a change lands once instead of once per sibling; extracting one helper per block leaves every sibling to drift on its own. 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 (6 lines × 2) REDACTED:1020— REDACTED:1020-1025 | REDACTED:1073-1078 — 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 `REDACTED:1020` 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.
X17 · Uncapped recursion over a caller-supplied document· Recursive walk over a caller-supplied document has no depth cap · ×3
Recursive walk over a caller-supplied document has no depth cap src/datasets/features/features.py:1645— `generate_from_dict` is public and takes `obj: Any` from its caller, and it calls itself at line 1645 (`generate_from_dict`) with a piece of `obj` (and at line 1648, line 1658, line 1661, line 1664) — and nowhere in `generate_from_dict` 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 `generate_from_dict` and reject the value past a fixed maximum, or rewrite the descent with an explicit stack.
Recursive walk over a caller-supplied document has no depth cap src/datasets/utils/json.py:32— `json_encode_field` is public and takes `example: Any` from its caller, and it calls itself at line 32 (`json_encode_field`) with a piece of `example` (and at line 34) — and nowhere in `json_encode_field` 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 `json_encode_field` and reject the value past a fixed maximum, or rewrite the descent with an explicit stack.
Recursive walk over a caller-supplied document has no depth cap src/datasets/utils/json.py:53— `json_decode_field` is public and takes `example: Any` from its caller, and it calls itself at line 53 (`json_decode_field`) with a piece of `example` (and at line 55) — and nowhere in `json_decode_field` 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 `json_decode_field` and reject the value past a fixed maximum, or rewrite the descent with an explicit stack.
Swallowed exception (caught, then discarded) src/datasets/builder.py:1665— `except Exception` takes every exception and records none of it — the body neither logs it, rethrows it, nor even names it, so the failure is discarded as completely as by an empty catch and only the substituted value survives. Log it through whatever this codebase already uses to report problems, narrow the catch to the exception this call can actually raise, or say in a comment on the catch why the failure genuinely cannot matter.
Swallowed exception (caught, then discarded) src/datasets/utils/json.py:61— `except Exception` takes every exception and records none of it — the body neither logs it, rethrows it, nor even names it, so the failure is discarded as completely as by an empty catch and only the substituted value survives. Log it through whatever this codebase already uses to report problems, narrow the catch to the exception this call can actually raise, or say in a comment on the catch why the failure genuinely cannot matter.
Swallowed exception (caught, then discarded) src/datasets/utils/_dill.py:276— `except Exception` takes every exception and records none of it — the body neither logs it, rethrows it, nor even names it, so the failure is discarded as completely as by an empty catch and only the substituted value survives. Log it through whatever this codebase already uses to report problems, narrow the catch to the exception this call can actually raise, or say in a comment on the catch why the failure genuinely cannot matter.
Duplicated block (15 lines × 2) src/datasets/formatting/jax_formatter.py:104— src/datasets/formatting/jax_formatter.py:104-118 | src/datasets/formatting/tf_formatter.py:68-82 — before extracting anything, compare `src/datasets/formatting/jax_formatter.py` and `src/datasets/formatting/tf_formatter.py` as WHOLE FILES: this scan already matched 5 separate duplicated blocks between them, totalling at least 45 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 (15 lines × 2) src/datasets/packaged_modules/parquet/parquet.py:98— src/datasets/packaged_modules/parquet/parquet.py:98-112 | src/datasets/packaged_modules/vortex/vortex.py:230-244 — 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 (14 lines × 2) src/datasets/arrow_dataset.py:6871— src/datasets/arrow_dataset.py:6871-6884 | src/datasets/dataset_dict.py:2749-2762 — before extracting anything, compare `src/datasets/arrow_dataset.py` and `src/datasets/dataset_dict.py` as WHOLE FILES: this scan already matched 6 separate duplicated blocks between them, totalling at least 181 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 `src/datasets/arrow_dataset.py:6871` 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 (14 lines × 2) src/datasets/dataset_dict.py:954— src/datasets/dataset_dict.py:954-967 | src/datasets/dataset_dict.py:1990-2003 — 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 `src/datasets/dataset_dict.py:954` 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 (13 lines × 3) src/datasets/features/image.py:163— src/datasets/features/image.py:163-175 | src/datasets/features/mesh.py:113-125 | src/datasets/features/nifti.py:171-183 — before extracting anything, compare `src/datasets/features/image.py` and `src/datasets/features/mesh.py` as WHOLE FILES: this scan already matched 9 separate duplicated blocks between them, totalling at least 124 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 `src/datasets/features/image.py:163` 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 (13 lines × 3) src/datasets/iterable_dataset.py:1768— src/datasets/iterable_dataset.py:1768-1780 | src/datasets/iterable_dataset.py:1786-1798 | src/datasets/iterable_dataset.py:1803-1815 — 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 `src/datasets/iterable_dataset.py:1768` 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 (12 lines × 3) src/datasets/features/image.py:179— src/datasets/features/image.py:179-190 | src/datasets/features/nifti.py:190-201 | src/datasets/features/pdf.py:150-161 — before extracting anything, compare `src/datasets/features/image.py` and `src/datasets/features/nifti.py` as WHOLE FILES: this scan already matched 9 separate duplicated blocks between them, totalling at least 107 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 `src/datasets/features/image.py:179` 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 (12 lines × 3) src/datasets/load.py:482— src/datasets/load.py:482-493 | src/datasets/load.py:690-701 | src/datasets/load.py:928-939 — 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 (6 lines × 4) src/datasets/features/audio.py:257— src/datasets/features/audio.py:257-262 | src/datasets/features/image.py:247-252 | src/datasets/features/mesh.py:192-197 | src/datasets/features/video.py:263-268 — 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 4 call sites, so a change lands once.
Duplicated block (6 lines × 4) src/datasets/formatting/jax_formatter.py:169— src/datasets/formatting/jax_formatter.py:169-174 | src/datasets/formatting/np_formatter.py:118-123 | src/datasets/formatting/tf_formatter.py:124-129 | src/datasets/formatting/torch_formatter.py:125-130 — before extracting anything, compare `src/datasets/formatting/jax_formatter.py` and `src/datasets/formatting/tf_formatter.py` as WHOLE FILES: this scan already matched 5 separate duplicated blocks between them, totalling at least 45 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 `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 × 7) src/datasets/features/audio.py:342— src/datasets/features/audio.py:342-349 | src/datasets/features/bio_sequence.py:298-302 | src/datasets/features/image.py:337-344 | src/datasets/features/mesh.py:270-277 | src/datasets/features/nifti.py:275-282 | src/datasets/features/pdf.py:279-286 | src/datasets/features/video.py:353-360 — 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 7 call sites, so a change lands once. Read the line range as the matched WINDOW rather than a finished unit: at `src/datasets/features/audio.py:342` 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 × 7) src/datasets/features/bio_sequence.py:114— src/datasets/features/bio_sequence.py:114-118 | src/datasets/features/bio_structure.py:120-124 | src/datasets/features/image.py:116-120 | src/datasets/features/mesh.py:72-76 | src/datasets/features/nifti.py:125-129 | src/datasets/features/pdf.py:93-97 | src/datasets/features/video.py:126-130 — 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 7 call sites, so a change lands once. 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. ★ These copies have DRIFTED, and that is worth reading before extracting anything: just before the matched lines, `src/datasets/features/video.py:123` calls `isinstance`, `array` and `src/datasets/features/bio_sequence.py:112` 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.
features._cast_to_python_objects (cyclomatic 67) src/datasets/features/features.py:286— features._cast_to_python_objects has cyclomatic complexity 67 (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.
Dataset.map (cyclomatic 61) src/datasets/arrow_dataset.py:3205— Dataset.map has cyclomatic complexity 61 (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.
TypedSequence._arrow_array (cyclomatic 55) src/datasets/arrow_writer.py:323— TypedSequence._arrow_array has cyclomatic complexity 55 (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.
Dataset.train_test_split (cyclomatic 50) src/datasets/arrow_dataset.py:4967— Dataset.train_test_split has cyclomatic complexity 50 (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.
Json._generate_tables (cyclomatic 50) src/datasets/packaged_modules/json/json.py:142— Json._generate_tables has cyclomatic complexity 50 (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.
Dataset._map_single (cyclomatic 46) src/datasets/arrow_dataset.py:3660— Dataset._map_single has cyclomatic complexity 46 (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.
load.dataset_module_factory (cyclomatic 39) src/datasets/load.py:957— load.dataset_module_factory has cyclomatic complexity 39 (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.
TensorflowDatasetMixin.to_tf_dataset (cyclomatic 37) src/datasets/arrow_dataset.py:336— TensorflowDatasetMixin.to_tf_dataset has cyclomatic complexity 37 (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.
arrow_dataset._get_updated_dataset_card (cyclomatic 34) src/datasets/arrow_dataset.py:6887— arrow_dataset._get_updated_dataset_card has cyclomatic complexity 34 (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.
RebatchedArrowExamplesIterable._iter_arrow (cyclomatic 33) src/datasets/iterable_dataset.py:529— RebatchedArrowExamplesIterable._iter_arrow 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.
arrow_dataset._interleave_map_style_datasets (cyclomatic 28) src/datasets/arrow_dataset.py:7135— arrow_dataset._interleave_map_style_datasets has cyclomatic complexity 28 (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.
DatasetBuilder.download_and_prepare (cyclomatic 28) src/datasets/builder.py:702— DatasetBuilder.download_and_prepare has cyclomatic complexity 28 (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.
MappedExamplesIterable._iter_arrow (cyclomatic 28) src/datasets/iterable_dataset.py:1678— MappedExamplesIterable._iter_arrow has cyclomatic complexity 28 (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.
table.array_cast (cyclomatic 28) src/datasets/table.py:1907— table.array_cast has cyclomatic complexity 28 (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.
py_utils.map_nested (cyclomatic 28) src/datasets/utils/py_utils.py:416— py_utils.map_nested has cyclomatic complexity 28 (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.
DatasetBuilder._create_builder_config (cyclomatic 27) src/datasets/builder.py:514— DatasetBuilder._create_builder_config has cyclomatic complexity 27 (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.
table.cast_array_to_feature (cyclomatic 26) src/datasets/table.py:2020— table.cast_array_to_feature has cyclomatic complexity 26 (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.
DatasetBuilder.__init__ (cyclomatic 25) src/datasets/builder.py:308— DatasetBuilder.__init__ has cyclomatic complexity 25 (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.
NumpyArrowExtractor._arrow_array_to_numpy (cyclomatic 25) src/datasets/formatting/formatting.py:166— NumpyArrowExtractor._arrow_array_to_numpy has cyclomatic complexity 25 (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.
FolderBasedBuilder._split_generators (cyclomatic 24) src/datasets/packaged_modules/folder_based_builder/folder_based_builder.py:76— FolderBasedBuilder._split_generators has cyclomatic complexity 24 (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.
TsFile._read_device_from_file (cyclomatic 24) src/datasets/packaged_modules/tsfile/tsfile.py:537— TsFile._read_device_from_file has cyclomatic complexity 24 (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.
FolderBasedBuilder._read_metadata (cyclomatic 23) src/datasets/packaged_modules/folder_based_builder/folder_based_builder.py:288— FolderBasedBuilder._read_metadata has cyclomatic complexity 23 (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.
Lance._split_generators (cyclomatic 23) src/datasets/packaged_modules/lance/lance.py:100— Lance._split_generators has cyclomatic complexity 23 (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.
IterableDataset._prepare_ex_iterable_for_iteration (cyclomatic 22) src/datasets/iterable_dataset.py:2958— IterableDataset._prepare_ex_iterable_for_iteration has cyclomatic complexity 22 (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.
IterableDataset._push_parquet_shards_to_hub (cyclomatic 22) src/datasets/iterable_dataset.py:5188— IterableDataset._push_parquet_shards_to_hub has cyclomatic complexity 22 (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.
data_files.resolve_pattern (cyclomatic 21) src/datasets/data_files.py:303— data_files.resolve_pattern has cyclomatic complexity 21 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
TsFile._scan_metadata (cyclomatic 21) src/datasets/packaged_modules/tsfile/tsfile.py:302— TsFile._scan_metadata has cyclomatic complexity 21 (threshold 15). To reduce it, separate the cases: extract each independent branch into its own named function, and where the body has guards that only reject input, fold those into early returns at the top.
Dataset.save_to_disk (cyclomatic 20) src/datasets/arrow_dataset.py:1781— Dataset.save_to_disk 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.
features.string_to_arrow (cyclomatic 20) src/datasets/features/features.py:130— features.string_to_arrow 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. This is NOT this file's highest cyclomatic complexity: features._arrow_to_datasets_dtype (cyclomatic 32) is higher and carries no row of its own — it was excluded as a flat dispatcher (a long switch/match over independent cases: many branches, almost no nesting), which this dimension does not treat as a refactor obligation. It is named here so the ranking you see in this file is not mistaken for the whole of it; the excluded method is counted neither in this dimension's figures nor in its score.
JaxFormatter._tensorize (cyclomatic 20) src/datasets/formatting/jax_formatter.py:83— JaxFormatter._tensorize has cyclomatic complexity 20 (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.
TorchFormatter._tensorize (cyclomatic 20) src/datasets/formatting/torch_formatter.py:49— TorchFormatter._tensorize 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.
CyclingMultiSourcesExamplesIterable._iter_arrow (cyclomatic 20) src/datasets/iterable_dataset.py:828— CyclingMultiSourcesExamplesIterable._iter_arrow 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. This shape REPEATS in the file: one other method here (CyclingMultiSourcesExamplesIterable.__iter__) has the same decision points, in the same order, at the same nesting depths — so this is one pattern written twice rather than two separate problems. Splitting this body alone leaves the other exactly as it is. Where these are variations on one operation, the change that clears both is the shared one: lift the common shape into a single routine the variants call, parameterised by whatever genuinely differs between them, and keep in each method only the part that is not shared.
CyclingMultiSourcesExamplesIterable.__iter__ (cyclomatic 20) src/datasets/iterable_dataset.py:903— CyclingMultiSourcesExamplesIterable.__iter__ 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. This shape REPEATS in the file: one other method here (CyclingMultiSourcesExamplesIterable._iter_arrow) has the same decision points, in the same order, at the same nesting depths — so this is one pattern written twice rather than two separate problems. Splitting this body alone leaves the other exactly as it is. Where these are variations on one operation, the change that clears both is the shared one: lift the common shape into a single routine the variants call, parameterised by whatever genuinely differs between them, and keep in each method only the part that is not shared.
HubDatasetModuleFactory.get_module (cyclomatic 20) src/datasets/load.py:577— HubDatasetModuleFactory.get_module has cyclomatic complexity 20 (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.
Fastq._parse_fastq (cyclomatic 20) src/datasets/packaged_modules/fastq/fastq.py:117— Fastq._parse_fastq 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.
TensorflowDatasetMixin._get_output_signature (cyclomatic 19) src/datasets/arrow_dataset.py:239— TensorflowDatasetMixin._get_output_signature has cyclomatic complexity 19 (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.
Dataset.shuffle (cyclomatic 19) src/datasets/arrow_dataset.py:4827— Dataset.shuffle has cyclomatic complexity 19 (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.
ArrowWriter._write_batch (cyclomatic 19) src/datasets/arrow_writer.py:759— ArrowWriter._write_batch has cyclomatic complexity 19 (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.
features.decode_nested_example (cyclomatic 19) src/datasets/features/features.py:1546— features.decode_nested_example has cyclomatic complexity 19 (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. This is NOT this file's highest cyclomatic complexity: features._arrow_to_datasets_dtype (cyclomatic 32) is higher and carries no row of its own — it was excluded as a flat dispatcher (a long switch/match over independent cases: many branches, almost no nesting), which this dimension does not treat as a refactor obligation. It is named here so the ranking you see in this file is not mistaken for the whole of it; the excluded method is counted neither in this dimension's figures nor in its score.
NumpyFormatter._tensorize (cyclomatic 19) src/datasets/formatting/np_formatter.py:49— NumpyFormatter._tensorize has cyclomatic complexity 19 (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.
FaissIndex.add_vectors (cyclomatic 19) src/datasets/search.py:255— FaissIndex.add_vectors has cyclomatic complexity 19 (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.
file_utils.cached_path (cyclomatic 19) REDACTED:129— file_utils.cached_path has cyclomatic complexity 19 (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.
Dataset.sort (cyclomatic 18) src/datasets/arrow_dataset.py:4697— Dataset.sort has cyclomatic complexity 18 (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.
Dataset._push_parquet_shards_to_hub (cyclomatic 18) src/datasets/arrow_dataset.py:5960— Dataset._push_parquet_shards_to_hub has cyclomatic complexity 18 (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.
dataset_dict._push_to_repo (cyclomatic 18) src/datasets/dataset_dict.py:2515— dataset_dict._push_to_repo 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.
IterableDataset._map (cyclomatic 18) src/datasets/iterable_dataset.py:3771— IterableDataset._map 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.
cache._find_hash_in_cache (cyclomatic 18) src/datasets/packaged_modules/cache/cache.py:25— cache._find_hash_in_cache 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.
_dill._save_transformersPreTrainedTokenizerBase (cyclomatic 18) src/datasets/utils/_dill.py:246— _dill._save_transformersPreTrainedTokenizerBase 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.
file_utils._prepare_single_hop_path_and_storage_options (cyclomatic 18) REDACTED:886— file_utils._prepare_single_hop_path_and_storage_options 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.
arrow_dataset._concatenate_map_style_datasets (cyclomatic 17) src/datasets/arrow_dataset.py:7021— arrow_dataset._concatenate_map_style_datasets has cyclomatic complexity 17 (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.
arrow_reader.make_file_instructions (cyclomatic 17) src/datasets/arrow_reader.py:92— arrow_reader.make_file_instructions has cyclomatic complexity 17 (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.
GeneratorBasedBuilder._prepare_split (cyclomatic 17) src/datasets/builder.py:1352— GeneratorBasedBuilder._prepare_split has cyclomatic complexity 17 (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.
ArrowBasedBuilder._prepare_split (cyclomatic 17) src/datasets/builder.py:1654— ArrowBasedBuilder._prepare_split has cyclomatic complexity 17 (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.
Audio.encode_example (cyclomatic 17) src/datasets/features/audio.py:96— Audio.encode_example 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.
MappedExamplesIterable._iter (cyclomatic 17) src/datasets/iterable_dataset.py:1467— MappedExamplesIterable._iter has cyclomatic complexity 17 (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.
IterableDataset.push_to_hub (cyclomatic 17) src/datasets/iterable_dataset.py:5320— IterableDataset.push_to_hub has cyclomatic complexity 17 (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.
Conll._generate_tables (cyclomatic 17) src/datasets/packaged_modules/conll/conll.py:113— Conll._generate_tables has cyclomatic complexity 17 (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.
FolderBasedBuilder._generate_examples (cyclomatic 17) src/datasets/packaged_modules/folder_based_builder/folder_based_builder.py:379— FolderBasedBuilder._generate_examples has cyclomatic complexity 17 (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.
GenBank._handle_features_line (cyclomatic 17) src/datasets/packaged_modules/genbank/genbank.py:481— GenBank._handle_features_line has cyclomatic complexity 17 (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.
json.find_mixed_struct_types_field_paths (cyclomatic 17) src/datasets/utils/json.py:65— json.find_mixed_struct_types_field_paths has cyclomatic complexity 17 (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.
py_utils.convert_file_size_to_int (cyclomatic 17) src/datasets/utils/py_utils.py:95— py_utils.convert_file_size_to_int 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.
Dataset.__init__ (cyclomatic 16) src/datasets/arrow_dataset.py:712— Dataset.__init__ has cyclomatic complexity 16 (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.
arrow_dataset._push_to_repo (cyclomatic 16) src/datasets/arrow_dataset.py:6656— arrow_dataset._push_to_repo 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.
DatasetDict.push_to_hub (cyclomatic 16) src/datasets/dataset_dict.py:1623— DatasetDict.push_to_hub 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.
IterableDatasetDict.push_to_hub (cyclomatic 16) src/datasets/dataset_dict.py:2327— IterableDatasetDict.push_to_hub 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.
TFFormatter._tensorize (cyclomatic 16) src/datasets/formatting/tf_formatter.py:55— TFFormatter._tensorize 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.
IterableDataset._write_parquet_shards (cyclomatic 16) src/datasets/iterable_dataset.py:5033— IterableDataset._write_parquet_shards has cyclomatic complexity 16 (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.
GenBank._handle_header_line (cyclomatic 16) src/datasets/packaged_modules/genbank/genbank.py:408— GenBank._handle_header_line has cyclomatic complexity 16 (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.
py_utils._single_map_nested (cyclomatic 16) src/datasets/utils/py_utils.py:372— py_utils._single_map_nested has cyclomatic complexity 16 (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.
XxxComment tests/utils.py:591— # XXX: the timeout doesn't seem to make any difference here — 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.
Json._generate_tables (cognitive 202) src/datasets/packaged_modules/json/json.py:142— Json._generate_tables has cognitive complexity 202 (threshold 15). Drivers by points: if/else 26 (100 pts), error handling 7 (41 pts), loops 7 (27 pts), ternaries 5 (27 pts), boolean chains 7 (nesting depth added 150). To reduce it, flatten the nesting: this score is depth rather than breadth — most of its points come from checks stacked inside one another, so the work sits several levels in. Invert each enclosing check into an early exit (a return, or the language's equivalent) so the happy path stays at one level, and where a level cannot be exited early, lift the block it encloses into its own named function.
TypedSequence._arrow_array (cognitive 99) src/datasets/arrow_writer.py:323— TypedSequence._arrow_array has cognitive complexity 99 (threshold 15). Drivers by points: if/else 28 (60 pts), boolean chains 14, error handling 4 (13 pts), loops 2 (8 pts), ternaries 3 (4 pts) (nesting depth added 48). 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.
Dataset._map_single (cognitive 96) src/datasets/arrow_dataset.py:3660— Dataset._map_single has cognitive complexity 96 (threshold 15). Drivers by points: if/else 34 (72 pts), boolean chains 9, loops 3 (7 pts), error handling 3 (6 pts), ternaries 1 (2 pts) (nesting depth added 46). To reduce it, flatten the nesting: this score is depth rather than breadth — most of its points come from checks stacked inside one another, so the work sits several levels in. Invert each enclosing check into an early exit (a return, or the language's equivalent) so the happy path stays at one level, and where a level cannot be exited early, lift the block it encloses into its own named function.
Dataset.map (cognitive 93) src/datasets/arrow_dataset.py:3205— Dataset.map has cognitive complexity 93 (threshold 15). Drivers by points: if/else 46 (61 pts), loops 6 (15 pts), boolean chains 11, ternaries 3 (4 pts), error handling 1 (2 pts) (nesting depth added 26). 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.
features._cast_to_python_objects (cognitive 68) src/datasets/features/features.py:286— features._cast_to_python_objects has cognitive complexity 68 (threshold 15). Drivers by points: if/else 26 (38 pts), boolean chains 23, loops 2 (5 pts), ternaries 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.
load.dataset_module_factory (cognitive 66) src/datasets/load.py:957— load.dataset_module_factory has cognitive complexity 66 (threshold 15). Drivers by points: if/else 16 (34 pts), error handling 13 (27 pts), boolean chains 5 (nesting depth added 32). 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.
Lance._split_generators (cognitive 64) src/datasets/packaged_modules/lance/lance.py:100— Lance._split_generators has cognitive complexity 64 (threshold 15). Drivers by points: if/else 15 (43 pts), loops 4 (12 pts), error handling 1 (6 pts), boolean chains 3 (nesting depth added 41). 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.
Dataset.train_test_split (cognitive 62) src/datasets/arrow_dataset.py:4967— Dataset.train_test_split has cognitive complexity 62 (threshold 15). Drivers by points: if/else 27 (39 pts), boolean chains 17, ternaries 2 (4 pts), error handling 1 (2 pts) (nesting depth added 15). 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.
arrow_dataset._get_updated_dataset_card (cognitive 57) src/datasets/arrow_dataset.py:6887— arrow_dataset._get_updated_dataset_card has cognitive complexity 57 (threshold 15). Drivers by points: if/else 19 (33 pts), boolean chains 9, loops 4 (7 pts), ternaries 4 (6 pts), error handling 2 (nesting depth added 19). 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.
MappedExamplesIterable._iter_arrow (cognitive 57) src/datasets/iterable_dataset.py:1678— MappedExamplesIterable._iter_arrow has cognitive complexity 57 (threshold 15). Drivers by points: if/else 20 (40 pts), loops 3 (7 pts), ternaries 4 (6 pts), boolean chains 4 (nesting depth added 26). 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.
table.cast_array_to_feature (cognitive 55) src/datasets/table.py:2020— table.cast_array_to_feature has cognitive complexity 55 (threshold 15). Drivers by points: if/else 21 (48 pts), boolean chains 4, ternaries 1 (3 pts) (nesting depth added 29). To reduce it, flatten the nesting: this score is depth rather than breadth — most of its points come from checks stacked inside one another, so the work sits several levels in. Invert each enclosing check into an early exit (a return, or the language's equivalent) so the happy path stays at one level, and where a level cannot be exited early, lift the block it encloses into its own named function.
FolderBasedBuilder._read_metadata (cognitive 54) src/datasets/packaged_modules/folder_based_builder/folder_based_builder.py:288— FolderBasedBuilder._read_metadata has cognitive complexity 54 (threshold 15). Drivers by points: if/else 13 (28 pts), loops 4 (9 pts), ternaries 4 (8 pts), error handling 2 (7 pts), boolean chains 2 (nesting depth added 29). 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.
RebatchedArrowExamplesIterable._iter_arrow (cognitive 53) src/datasets/iterable_dataset.py:529— RebatchedArrowExamplesIterable._iter_arrow has cognitive complexity 53 (threshold 15). Drivers by points: if/else 19 (41 pts), boolean chains 7, loops 2 (3 pts), ternaries 2 (nesting depth added 23). 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.
arrow_dataset._interleave_map_style_datasets (cognitive 51) src/datasets/arrow_dataset.py:7135— arrow_dataset._interleave_map_style_datasets has cognitive complexity 51 (threshold 15). Drivers by points: if/else 14 (33 pts), loops 5 (10 pts), boolean chains 4, ternaries 2 (4 pts) (nesting depth added 26). 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.
FolderBasedBuilder._split_generators (cognitive 51) src/datasets/packaged_modules/folder_based_builder/folder_based_builder.py:76— FolderBasedBuilder._split_generators has cognitive complexity 51 (threshold 15). Drivers by points: if/else 16 (31 pts), loops 5 (12 pts), boolean chains 4, ternaries 1 (4 pts) (nesting depth added 25). 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.
TensorflowDatasetMixin.to_tf_dataset (cognitive 49) src/datasets/arrow_dataset.py:336— TensorflowDatasetMixin.to_tf_dataset has cognitive complexity 49 (threshold 15). Drivers by points: if/else 27 (38 pts), boolean chains 8, loops 2, ternaries 1 (nesting depth added 11). 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.
FolderBasedBuilder._generate_examples (cognitive 48) src/datasets/packaged_modules/folder_based_builder/folder_based_builder.py:379— FolderBasedBuilder._generate_examples has cognitive complexity 48 (threshold 15). Drivers by points: if/else 9 (24 pts), loops 6 (19 pts), ternaries 1 (3 pts), boolean chains 2 (nesting depth added 30). 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.
features.decode_nested_example (cognitive 47) src/datasets/features/features.py:1546— features.decode_nested_example has cognitive complexity 47 (threshold 15). Drivers by points: if/else 12 (34 pts), loops 2 (8 pts), ternaries 2 (4 pts), boolean chains 1 (nesting depth added 30). To reduce it, flatten the nesting: this score is depth rather than breadth — most of its points come from checks stacked inside one another, so the work sits several levels in. Invert each enclosing check into an early exit (a return, or the language's equivalent) so the happy path stays at one level, and where a level cannot be exited early, lift the block it encloses into its own named function.
arrow_reader.make_file_instructions (cognitive 45) src/datasets/arrow_reader.py:92— arrow_reader.make_file_instructions has cognitive complexity 45 (threshold 15). Drivers by points: ternaries 5 (19 pts), if/else 7 (18 pts), loops 3 (7 pts), boolean chains 1 (nesting depth added 29). 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.
TsFile._scan_metadata (cognitive 45) src/datasets/packaged_modules/tsfile/tsfile.py:302— TsFile._scan_metadata has cognitive complexity 45 (threshold 15). Drivers by points: if/else 13 (29 pts), ternaries 3 (7 pts), loops 3 (5 pts), boolean chains 2, error handling 1 (2 pts) (nesting depth added 23). 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.
Conll._generate_tables (cognitive 44) src/datasets/packaged_modules/conll/conll.py:113— Conll._generate_tables has cognitive complexity 44 (threshold 15). Drivers by points: if/else 8 (28 pts), loops 4 (10 pts), ternaries 1 (4 pts), boolean chains 2 (nesting depth added 29). 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.
table.array_cast (cognitive 44) src/datasets/table.py:1907— table.array_cast has cognitive complexity 44 (threshold 15). Drivers by points: if/else 17 (39 pts), boolean chains 5 (nesting depth added 22). To reduce it, flatten the nesting: this score is depth rather than breadth — most of its points come from checks stacked inside one another, so the work sits several levels in. Invert each enclosing check into an early exit (a return, or the language's equivalent) so the happy path stays at one level, and where a level cannot be exited early, lift the block it encloses into its own named function.
py_utils.map_nested (cognitive 43) src/datasets/utils/py_utils.py:416— py_utils.map_nested has cognitive complexity 43 (threshold 15). Drivers by points: if/else 19 (36 pts), boolean chains 6, ternaries 1 (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.
DatasetBuilder._create_builder_config (cognitive 42) src/datasets/builder.py:514— DatasetBuilder._create_builder_config has cognitive complexity 42 (threshold 15). Drivers by points: if/else 18 (31 pts), boolean chains 7, loops 1 (2 pts), ternaries 1 (2 pts) (nesting depth added 15). 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.
Fastq._parse_fastq (cognitive 42) src/datasets/packaged_modules/fastq/fastq.py:117— Fastq._parse_fastq has cognitive complexity 42 (threshold 15). Drivers by points: if/else 11 (29 pts), ternaries 3 (6 pts), loops 3 (5 pts), boolean chains 2 (nesting depth added 23). 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.
NumpyArrowExtractor._arrow_array_to_numpy (cognitive 40) src/datasets/formatting/formatting.py:166— NumpyArrowExtractor._arrow_array_to_numpy has cognitive complexity 40 (threshold 15). Drivers by points: if/else 12 (25 pts), boolean chains 13, loops 1 (2 pts) (nesting depth added 14). To reduce it, flatten the nesting: this score is depth rather than breadth — most of its points come from checks stacked inside one another, so the work sits several levels in. Invert each enclosing check into an early exit (a return, or the language's equivalent) so the happy path stays at one level, and where a level cannot be exited early, lift the block it encloses into its own named function.
Text._generate_tables (cognitive 39) src/datasets/packaged_modules/text/text.py:89— Text._generate_tables has cognitive complexity 39 (threshold 15). Drivers by points: if/else 6 (27 pts), loops 4 (11 pts), ternaries 1 (nesting depth added 28). 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.
CyclingMultiSourcesExamplesIterable._iter_arrow (cognitive 38) src/datasets/iterable_dataset.py:828— CyclingMultiSourcesExamplesIterable._iter_arrow has cognitive complexity 38 (threshold 15). Drivers by points: if/else 14 (31 pts), loops 4 (5 pts), boolean chains 1, ternaries 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. This shape REPEATS in the file: one other method here (CyclingMultiSourcesExamplesIterable.__iter__) has the same decision points, in the same order, at the same nesting depths — so this is one pattern written twice rather than two separate problems. Splitting this body alone leaves the other exactly as it is. Where these are variations on one operation, the change that clears both is the shared one: lift the common shape into a single routine the variants call, parameterised by whatever genuinely differs between them, and keep in each method only the part that is not shared.
CyclingMultiSourcesExamplesIterable.__iter__ (cognitive 38) src/datasets/iterable_dataset.py:903— CyclingMultiSourcesExamplesIterable.__iter__ has cognitive complexity 38 (threshold 15). Drivers by points: if/else 14 (31 pts), loops 4 (5 pts), boolean chains 1, ternaries 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. This shape REPEATS in the file: one other method here (CyclingMultiSourcesExamplesIterable._iter_arrow) has the same decision points, in the same order, at the same nesting depths — so this is one pattern written twice rather than two separate problems. Splitting this body alone leaves the other exactly as it is. Where these are variations on one operation, the change that clears both is the shared one: lift the common shape into a single routine the variants call, parameterised by whatever genuinely differs between them, and keep in each method only the part that is not shared.
arrow_dataset._concatenate_map_style_datasets (cognitive 37) src/datasets/arrow_dataset.py:7021— arrow_dataset._concatenate_map_style_datasets has cognitive complexity 37 (threshold 15). Drivers by points: if/else 18 (28 pts), loops 3 (9 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.
DatasetBuilder.__init__ (cognitive 37) src/datasets/builder.py:308— DatasetBuilder.__init__ has cognitive complexity 37 (threshold 15). Drivers by points: if/else 13 (22 pts), boolean chains 6, loops 2 (5 pts), ternaries 4 (nesting depth added 12). 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.
dataset_dict._push_to_repo (cognitive 37) src/datasets/dataset_dict.py:2515— dataset_dict._push_to_repo has cognitive complexity 37 (threshold 15). Drivers by points: if/else 8 (16 pts), loops 4 (7 pts), error handling 2 (6 pts), boolean chains 4, ternaries 2 (4 pts) (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.
IterableDataset._prepare_ex_iterable_for_iteration (cognitive 37) src/datasets/iterable_dataset.py:2958— IterableDataset._prepare_ex_iterable_for_iteration has cognitive complexity 37 (threshold 15). Drivers by points: if/else 12 (24 pts), boolean chains 7, ternaries 1 (4 pts), error handling 1 (2 pts) (nesting depth added 16). To reduce it, flatten the nesting: this score is depth rather than breadth — most of its points come from checks stacked inside one another, so the work sits several levels in. Invert each enclosing check into an early exit (a return, or the language's equivalent) so the happy path stays at one level, and where a level cannot be exited early, lift the block it encloses into its own named function.
features.string_to_arrow (cognitive 36) src/datasets/features/features.py:130— features.string_to_arrow has cognitive complexity 36 (threshold 15). Drivers by points: if/else 24 (36 pts) (nesting depth added 12). 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.
arrow_dataset._push_to_repo (cognitive 35) src/datasets/arrow_dataset.py:6656— arrow_dataset._push_to_repo has cognitive complexity 35 (threshold 15). Drivers by points: if/else 8 (16 pts), error handling 2 (6 pts), loops 3 (6 pts), ternaries 2 (4 pts), boolean chains 3 (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.
arrow_dataset.get_indices_from_mask_function (cognitive 35) src/datasets/arrow_dataset.py:7370— arrow_dataset.get_indices_from_mask_function has cognitive complexity 35 (threshold 15). Drivers by points: if/else 13 (29 pts), loops 2 (6 pts) (nesting depth added 20). 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 shape REPEATS in the file: one other method here (arrow_dataset.async_get_indices_from_mask_function) has the same decision points, in the same order, at the same nesting depths — so this is one pattern written twice rather than two separate problems. Splitting this body alone leaves the other exactly as it is. Where these are variations on one operation, the change that clears both is the shared one: lift the common shape into a single routine the variants call, parameterised by whatever genuinely differs between them, and keep in each method only the part that is not shared.
arrow_dataset.async_get_indices_from_mask_function (cognitive 35) src/datasets/arrow_dataset.py:7428— arrow_dataset.async_get_indices_from_mask_function has cognitive complexity 35 (threshold 15). Drivers by points: if/else 13 (29 pts), loops 2 (6 pts) (nesting depth added 20). 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 shape REPEATS in the file: one other method here (arrow_dataset.get_indices_from_mask_function) has the same decision points, in the same order, at the same nesting depths — so this is one pattern written twice rather than two separate problems. Splitting this body alone leaves the other exactly as it is. Where these are variations on one operation, the change that clears both is the shared one: lift the common shape into a single routine the variants call, parameterised by whatever genuinely differs between them, and keep in each method only the part that is not shared.
IterableDataset._write_parquet_shards (cognitive 34) src/datasets/iterable_dataset.py:5033— IterableDataset._write_parquet_shards has cognitive complexity 34 (threshold 15). Drivers by points: loops 8 (17 pts), if/else 4 (11 pts), error handling 2 (5 pts), boolean chains 1 (nesting depth added 19). 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.
TsFile._read_device_from_file (cognitive 34) src/datasets/packaged_modules/tsfile/tsfile.py:537— TsFile._read_device_from_file has cognitive complexity 34 (threshold 15). Drivers by points: if/else 14 (21 pts), ternaries 5 (8 pts), loops 3 (4 pts), boolean chains 1 (nesting depth added 11). 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.
Dataset.save_to_disk (cognitive 33) src/datasets/arrow_dataset.py:1781— Dataset.save_to_disk has cognitive complexity 33 (threshold 15). Drivers by points: if/else 11 (17 pts), loops 4 (8 pts), boolean chains 5, error handling 1 (2 pts), ternaries 1 (nesting depth added 11). 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.
Fasta._parse_fasta (cognitive 33) src/datasets/packaged_modules/fasta/fasta.py:117— Fasta._parse_fasta has cognitive complexity 33 (threshold 15). Drivers by points: if/else 8 (21 pts), loops 3 (6 pts), ternaries 3 (6 pts) (nesting depth added 19). 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.
Pickler.save (cognitive 33) src/datasets/utils/_dill.py:32— Pickler.save has cognitive complexity 33 (threshold 15). Drivers by points: if/else 14 (33 pts) (nesting depth added 19). 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.
json.find_mixed_struct_types_field_paths (cognitive 33) src/datasets/utils/json.py:65— json.find_mixed_struct_types_field_paths has cognitive complexity 33 (threshold 15). Drivers by points: if/else 9 (24 pts), loops 2 (5 pts), boolean chains 4 (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.
RandomlyCyclingMultiSourcesExamplesIterable._get_indices_iterator (cognitive 32) src/datasets/iterable_dataset.py:1271— RandomlyCyclingMultiSourcesExamplesIterable._get_indices_iterator has cognitive complexity 32 (threshold 15). Drivers by points: if/else 7 (21 pts), loops 4 (10 pts), ternaries 1 (nesting depth added 20). 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.
file_utils.cached_path (cognitive 32) REDACTED:129— file_utils.cached_path has cognitive complexity 32 (threshold 15). Drivers by points: if/else 14 (21 pts), ternaries 1 (5 pts), boolean chains 3, error handling 1 (3 pts) (nesting depth added 13). 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.
TensorflowDatasetMixin._get_output_signature (cognitive 31) src/datasets/arrow_dataset.py:239— TensorflowDatasetMixin._get_output_signature has cognitive complexity 31 (threshold 15). Drivers by points: if/else 14 (24 pts), loops 4 (6 pts), boolean chains 1 (nesting depth added 12). 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.
DatasetBuilder.download_and_prepare (cognitive 31) src/datasets/builder.py:702— DatasetBuilder.download_and_prepare has cognitive complexity 31 (threshold 15). Drivers by points: if/else 14 (16 pts), boolean chains 9, ternaries 5 (6 pts) (nesting depth added 3). 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.
features.encode_nested_example (cognitive 31) src/datasets/features/features.py:1506— features.encode_nested_example has cognitive complexity 31 (threshold 15). Drivers by points: if/else 7 (18 pts), error handling 1 (4 pts), loops 1 (4 pts), ternaries 2 (4 pts), boolean chains 1 (nesting depth added 19). To reduce it, flatten the nesting: this score is depth rather than breadth — most of its points come from checks stacked inside one another, so the work sits several levels in. Invert each enclosing check into an early exit (a return, or the language's equivalent) so the happy path stays at one level, and where a level cannot be exited early, lift the block it encloses into its own named function.
data_files._get_data_files_patterns (cognitive 30) src/datasets/data_files.py:259— data_files._get_data_files_patterns has cognitive complexity 30 (threshold 15). Drivers by points: if/else 5 (14 pts), loops 5 (10 pts), error handling 2 (6 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.
Fastq._generate_tables (cognitive 30) src/datasets/packaged_modules/fastq/fastq.py:244— Fastq._generate_tables has cognitive complexity 30 (threshold 15). Drivers by points: if/else 9 (26 pts), loops 2 (3 pts), boolean chains 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.
ArrowWriter._write_batch (cognitive 29) src/datasets/arrow_writer.py:759— ArrowWriter._write_batch has cognitive complexity 29 (threshold 15). Drivers by points: if/else 8 (12 pts), ternaries 6 (11 pts), boolean chains 5, loops 1 (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.
data_files.resolve_pattern (cognitive 29) src/datasets/data_files.py:303— data_files.resolve_pattern has cognitive complexity 29 (threshold 15). Drivers by points: if/else 13 (20 pts), ternaries 4 (5 pts), boolean chains 3, loops 1 (nesting depth added 8). 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.
ArrowExamplesIterable.__iter__ (cognitive 29) src/datasets/iterable_dataset.py:413— ArrowExamplesIterable.__iter__ has cognitive complexity 29 (threshold 15). Drivers by points: if/else 4 (16 pts), loops 4 (10 pts), ternaries 2 (3 pts) (nesting depth added 19). 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.
GenBank._generate_tables (cognitive 29) src/datasets/packaged_modules/genbank/genbank.py:651— GenBank._generate_tables has cognitive complexity 29 (threshold 15). Drivers by points: if/else 7 (20 pts), loops 3 (6 pts), boolean chains 3 (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.
FaissIndex.add_vectors (cognitive 29) src/datasets/search.py:255— FaissIndex.add_vectors has cognitive complexity 29 (threshold 15). Drivers by points: if/else 12 (18 pts), ternaries 3 (6 pts), boolean chains 4, loops 1 (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.
MappedExamplesIterable._iter (cognitive 28) src/datasets/iterable_dataset.py:1467— MappedExamplesIterable._iter has cognitive complexity 28 (threshold 15). Drivers by points: if/else 10 (13 pts), error handling 3 (6 pts), loops 2 (4 pts), ternaries 2 (3 pts), boolean chains 2 (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.
GeneratorBasedBuilder._prepare_split (cognitive 27) src/datasets/builder.py:1352— GeneratorBasedBuilder._prepare_split has cognitive complexity 27 (threshold 15). Drivers by points: if/else 14 (19 pts), boolean chains 4, loops 2 (4 pts) (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.
Nifti.decode_example (cognitive 27) src/datasets/features/nifti.py:150— Nifti.decode_example has cognitive complexity 27 (threshold 15). Drivers by points: if/else 12 (18 pts), ternaries 2 (8 pts), boolean chains 1 (nesting depth added 12). 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.
IterableDataset._push_parquet_shards_to_hub (cognitive 27) src/datasets/iterable_dataset.py:5188— IterableDataset._push_parquet_shards_to_hub has cognitive complexity 27 (threshold 15). Drivers by points: if/else 9 (11 pts), boolean chains 6, ternaries 5 (6 pts), loops 2 (4 pts) (nesting depth added 5). 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.
Fasta._generate_tables (cognitive 27) src/datasets/packaged_modules/fasta/fasta.py:218— Fasta._generate_tables has cognitive complexity 27 (threshold 15). Drivers by points: if/else 8 (23 pts), loops 2 (3 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.
TypedSequence._infer_custom_type_and_encode (cognitive 26) src/datasets/arrow_writer.py:279— TypedSequence._infer_custom_type_and_encode has cognitive complexity 26 (threshold 15). Drivers by points: ternaries 4 (12 pts), if/else 6 (10 pts), boolean chains 4 (nesting depth added 12). 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.
ArrowBasedBuilder._prepare_split (cognitive 26) src/datasets/builder.py:1654— ArrowBasedBuilder._prepare_split has cognitive complexity 26 (threshold 15). Drivers by points: if/else 12 (17 pts), boolean chains 4, loops 2 (4 pts), error handling 1 (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.
IterableDataset._map (cognitive 26) src/datasets/iterable_dataset.py:3771— IterableDataset._map has cognitive complexity 26 (threshold 15). Drivers by points: if/else 10 (14 pts), ternaries 3 (7 pts), boolean chains 5 (nesting depth added 8). 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.
file_utils._prepare_single_hop_path_and_storage_options (cognitive 26) REDACTED:886— file_utils._prepare_single_hop_path_and_storage_options has cognitive complexity 26 (threshold 15). Drivers by points: if/else 10 (18 pts), boolean chains 3, loops 1 (3 pts), ternaries 2 (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.
HorizontallyConcatenatedMultiSourcesExamplesIterable._iter_arrow (cognitive 25) src/datasets/iterable_dataset.py:1190— HorizontallyConcatenatedMultiSourcesExamplesIterable._iter_arrow has cognitive complexity 25 (threshold 15). Drivers by points: if/else 5 (11 pts), loops 4 (11 pts), error handling 1 (3 pts) (nesting depth added 15). 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.
FormattedExamplesIterable.__iter__ (cognitive 25) src/datasets/iterable_dataset.py:2541— FormattedExamplesIterable.__iter__ has cognitive complexity 25 (threshold 15). Drivers by points: if/else 6 (10 pts), loops 3 (7 pts), ternaries 3 (6 pts), boolean chains 2 (nesting depth added 11). 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.
TsFile._build_device_index (cognitive 25) src/datasets/packaged_modules/tsfile/tsfile.py:423— TsFile._build_device_index has cognitive complexity 25 (threshold 15). Drivers by points: if/else 6 (17 pts), loops 3 (5 pts), error handling 1 (2 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.
FilesIterable._iter_from_urlpaths (cognitive 25) REDACTED:1438— FilesIterable._iter_from_urlpaths has cognitive complexity 25 (threshold 15). Drivers by points: if/else 6 (17 pts), loops 3 (8 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.
Dataset.shuffle (cognitive 24) src/datasets/arrow_dataset.py:4827— Dataset.shuffle has cognitive complexity 24 (threshold 15). Drivers by points: if/else 12 (15 pts), ternaries 3 (5 pts), boolean chains 4 (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.
TorchFormatter._tensorize (cognitive 24) src/datasets/formatting/torch_formatter.py:49— TorchFormatter._tensorize has cognitive complexity 24 (threshold 15). Drivers by points: if/else 10 (16 pts), boolean chains 6, error handling 1 (2 pts) (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.
TsFile._iter_device_batches (cognitive 24) src/datasets/packaged_modules/tsfile/tsfile.py:503— TsFile._iter_device_batches has cognitive complexity 24 (threshold 15). Drivers by points: if/else 6 (15 pts), loops 3 (6 pts), error handling 1 (3 pts) (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.
Dataset.sort (cognitive 23) src/datasets/arrow_dataset.py:4697— Dataset.sort has cognitive complexity 23 (threshold 15). Drivers by points: if/else 13 (18 pts), boolean chains 2, ternaries 2, 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.
ArrowBasedBuilder._prepare_split_single (cognitive 23) src/datasets/builder.py:1811— ArrowBasedBuilder._prepare_split_single has cognitive complexity 23 (threshold 15). Drivers by points: if/else 8 (15 pts), error handling 2 (3 pts), boolean chains 2, ternaries 2, loops 1 (nesting depth added 8). 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.
JaxFormatter._tensorize (cognitive 23) src/datasets/formatting/jax_formatter.py:83— JaxFormatter._tensorize has cognitive complexity 23 (threshold 15). Drivers by points: if/else 11 (15 pts), boolean chains 6, error handling 1 (2 pts) (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.
IterableDataset.push_to_hub (cognitive 23) src/datasets/iterable_dataset.py:5320— IterableDataset.push_to_hub has cognitive complexity 23 (threshold 15). Drivers by points: if/else 10 (12 pts), error handling 2 (4 pts), ternaries 2 (4 pts), boolean chains 3 (nesting depth added 6). 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.
Parquet._split_generators (cognitive 23) src/datasets/packaged_modules/parquet/parquet.py:114— Parquet._split_generators has cognitive complexity 23 (threshold 15). Drivers by points: if/else 7 (14 pts), error handling 1 (4 pts), loops 2 (4 pts), boolean chains 1 (nesting depth added 12). 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.
Parquet._generate_tables (cognitive 23) src/datasets/packaged_modules/parquet/parquet.py:185— Parquet._generate_tables has cognitive complexity 23 (threshold 15). Drivers by points: if/else 7 (13 pts), loops 2 (4 pts), boolean chains 3, error handling 1 (2 pts), ternaries 1 (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.
TsFile._finalize_device (cognitive 23) src/datasets/packaged_modules/tsfile/tsfile.py:627— TsFile._finalize_device has cognitive complexity 23 (threshold 15). Drivers by points: if/else 7 (13 pts), ternaries 3 (5 pts), loops 3 (4 pts), boolean chains 1 (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.
patch_submodule.__enter__ (cognitive 23) src/datasets/utils/patching.py:48— patch_submodule.__enter__ has cognitive complexity 23 (threshold 15). Drivers by points: loops 4 (9 pts), if/else 4 (8 pts), error handling 2 (4 pts), boolean chains 2 (nesting depth added 11). 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.
Dataset.from_dict (cognitive 22) src/datasets/arrow_dataset.py:983— Dataset.from_dict has cognitive complexity 22 (threshold 15). Drivers by points: ternaries 6 (13 pts), if/else 6 (7 pts), boolean chains 1, loops 1 (nesting depth added 8). 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.
fingerprint.format_kwargs_for_fingerprint (cognitive 22) src/datasets/fingerprint.py:333— fingerprint.format_kwargs_for_fingerprint has cognitive complexity 22 (threshold 15). Drivers by points: if/else 11 (16 pts), ternaries 1 (3 pts), boolean chains 2, loops 1 (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.
HubDatasetModuleFactory.get_module (cognitive 22) src/datasets/load.py:577— HubDatasetModuleFactory.get_module has cognitive complexity 22 (threshold 15). Drivers by points: if/else 13, error handling 4 (5 pts), boolean chains 4 (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.
_dill._save_transformersPreTrainedTokenizerBase (cognitive 22) src/datasets/utils/_dill.py:246— _dill._save_transformersPreTrainedTokenizerBase has cognitive complexity 22 (threshold 15). Drivers by points: if/else 8 (12 pts), boolean chains 7, error handling 2 (3 pts) (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.
Dataset.push_to_hub (cognitive 21) src/datasets/arrow_dataset.py:6070— Dataset.push_to_hub has cognitive complexity 21 (threshold 15). Drivers by points: if/else 9 (11 pts), error handling 2 (4 pts), ternaries 2 (4 pts), boolean chains 2 (nesting depth added 6). 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.
data_files.sanitize_patterns (cognitive 21) src/datasets/data_files.py:123— data_files.sanitize_patterns has cognitive complexity 21 (threshold 15). Drivers by points: if/else 6 (12 pts), ternaries 2 (5 pts), loops 1 (3 pts), boolean chains 1 (nesting depth added 11). 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.
DatasetDict.push_to_hub (cognitive 21) src/datasets/dataset_dict.py:1623— DatasetDict.push_to_hub has cognitive complexity 21 (threshold 15). Drivers by points: if/else 9 (12 pts), error handling 2 (4 pts), boolean chains 2, ternaries 1 (2 pts), loops 1 (nesting depth added 6). 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.
IterableDatasetDict.push_to_hub (cognitive 21) src/datasets/dataset_dict.py:2327— IterableDatasetDict.push_to_hub has cognitive complexity 21 (threshold 15). Drivers by points: if/else 9 (12 pts), error handling 2 (4 pts), boolean chains 2, ternaries 1 (2 pts), loops 1 (nesting depth added 6). 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.
cache._find_hash_in_cache (cognitive 21) src/datasets/packaged_modules/cache/cache.py:25— cache._find_hash_in_cache has cognitive complexity 21 (threshold 15). Drivers by points: boolean chains 9, if/else 7 (8 pts), ternaries 2 (4 pts) (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.
WebDataset._generate_examples (cognitive 21) src/datasets/packaged_modules/webdataset/webdataset.py:116— WebDataset._generate_examples has cognitive complexity 21 (threshold 15). Drivers by points: if/else 6 (12 pts), loops 4 (9 pts) (nesting depth added 11). 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.
py_utils.convert_file_size_to_int (cognitive 21) src/datasets/utils/py_utils.py:95— py_utils.convert_file_size_to_int has cognitive complexity 21 (threshold 15). Drivers by points: if/else 11, ternaries 5 (10 pts) (nesting depth added 5). 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.
Dataset._select_with_indices_mapping (cognitive 20) src/datasets/arrow_dataset.py:4505— Dataset._select_with_indices_mapping has cognitive complexity 20 (threshold 15). Drivers by points: if/else 13 (16 pts), boolean chains 2, error handling 1, ternaries 1 (nesting depth added 3). 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.
fingerprint.update_fingerprint (cognitive 20) src/datasets/fingerprint.py:253— fingerprint.update_fingerprint has cognitive complexity 20 (threshold 15). Drivers by points: if/else 8 (16 pts), error handling 2 (3 pts), loops 1 (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.
file_utils.xopen (cognitive 20) REDACTED:939— file_utils.xopen has cognitive complexity 20 (threshold 15). Drivers by points: if/else 6 (11 pts), error handling 3 (6 pts), boolean chains 2, loops 1 (nesting depth added 8). 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.
Dataset.__init__ (cognitive 19) src/datasets/arrow_dataset.py:712— Dataset.__init__ has cognitive complexity 19 (threshold 15). Drivers by points: if/else 11 (13 pts), boolean chains 2, error handling 1 (2 pts), ternaries 2 (nesting depth added 3). 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.
ArrowWriter._build_schema (cognitive 19) src/datasets/arrow_writer.py:599— ArrowWriter._build_schema has cognitive complexity 19 (threshold 15). Drivers by points: if/else 8 (16 pts), loops 1 (3 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.
combine.interleave_datasets (cognitive 19) src/datasets/combine.py:18— combine.interleave_datasets has cognitive complexity 19 (threshold 15). Drivers by points: if/else 8 (15 pts), ternaries 1 (3 pts), loops 1 (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.
Audio.encode_example (cognitive 19) src/datasets/features/audio.py:96— Audio.encode_example has cognitive complexity 19 (threshold 15). Drivers by points: if/else 10 (15 pts), boolean chains 3, error handling 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.
ArrayExtensionArray.to_numpy (cognitive 19) src/datasets/features/features.py:806— ArrayExtensionArray.to_numpy has cognitive complexity 19 (threshold 15). Drivers by points: if/else 7 (11 pts), loops 3 (8 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.
Image.decode_example (cognitive 19) src/datasets/features/image.py:139— Image.decode_example has cognitive complexity 19 (threshold 15). Drivers by points: if/else 11 (12 pts), ternaries 2 (6 pts), boolean chains 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.
formatting.format_table (cognitive 19) src/datasets/formatting/formatting.py:637— formatting.format_table has cognitive complexity 19 (threshold 15). Drivers by points: if/else 11 (19 pts) (nesting depth added 8). 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.
NumpyFormatter._tensorize (cognitive 19) src/datasets/formatting/np_formatter.py:49— NumpyFormatter._tensorize has cognitive complexity 19 (threshold 15). Drivers by points: if/else 8 (11 pts), boolean chains 6, error handling 1 (2 pts) (nesting depth added 4). 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.
IterableDataset._iter_pytorch (cognitive 19) src/datasets/iterable_dataset.py:2889— IterableDataset._iter_pytorch has cognitive complexity 19 (threshold 15). Drivers by points: if/else 6 (8 pts), loops 2 (6 pts), boolean chains 4, ternaries 1 (nesting depth added 6). 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.
WebDataset._split_generators (cognitive 19) src/datasets/packaged_modules/webdataset/webdataset.py:65— WebDataset._split_generators has cognitive complexity 19 (threshold 15). Drivers by points: if/else 7 (16 pts), loops 2 (3 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.
ZipExtractor.is_extractable (cognitive 19) src/datasets/utils/extract.py:157— ZipExtractor.is_extractable has cognitive complexity 19 (threshold 15). Drivers by points: if/else 6 (16 pts), boolean chains 2, error handling 1 (nesting depth added 10). To reduce it, flatten the nesting: this score is depth rather than breadth — most of its points come from checks stacked inside one another, so the work sits several levels in. Invert each enclosing check into an early exit (a return, or the language's equivalent) so the happy path stays at one level, and where a level cannot be exited early, lift the block it encloses into its own named function.
py_utils.temp_seed (cognitive 19) src/datasets/utils/py_utils.py:243— py_utils.temp_seed has cognitive complexity 19 (threshold 15). Drivers by points: if/else 10 (15 pts), boolean chains 4 (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.
py_utils._single_map_nested (cognitive 19) src/datasets/utils/py_utils.py:372— py_utils._single_map_nested has cognitive complexity 19 (threshold 15). Drivers by points: if/else 10 (12 pts), boolean chains 4, ternaries 3 (nesting depth added 2). 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.
Dataset.select (cognitive 18) src/datasets/arrow_dataset.py:4359— Dataset.select has cognitive complexity 18 (threshold 15). Drivers by points: if/else 10 (14 pts), boolean chains 2, error handling 1 (2 pts) (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.
Dataset._push_parquet_shards_to_hub (cognitive 18) src/datasets/arrow_dataset.py:5960— Dataset._push_parquet_shards_to_hub has cognitive complexity 18 (threshold 15). Drivers by points: boolean chains 7, if/else 5 (6 pts), ternaries 4, 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.
BuilderConfig.create_config_id (cognitive 18) src/datasets/builder.py:144— BuilderConfig.create_config_id has cognitive complexity 18 (threshold 15). Drivers by points: if/else 12 (18 pts) (nesting depth added 6). 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.
combine.concatenate_datasets (cognitive 18) src/datasets/combine.py:168— combine.concatenate_datasets has cognitive complexity 18 (threshold 15). Drivers by points: if/else 7 (14 pts), ternaries 1 (3 pts), loops 1 (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.
Mesh.decode_example (cognitive 18) src/datasets/features/mesh.py:91— Mesh.decode_example has cognitive complexity 18 (threshold 15). Drivers by points: if/else 10 (14 pts), ternaries 2 (4 pts) (nesting depth added 6). 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.
TFFormatter._tensorize (cognitive 18) src/datasets/formatting/tf_formatter.py:55— TFFormatter._tensorize has cognitive complexity 18 (threshold 15). Drivers by points: if/else 8 (11 pts), boolean chains 5, error handling 1 (2 pts) (nesting depth added 4). 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.
load.load_dataset_builder (cognitive 18) src/datasets/load.py:1187— load.load_dataset_builder has cognitive complexity 18 (threshold 15). Drivers by points: if/else 7 (9 pts), ternaries 3 (5 pts), boolean chains 4 (nesting depth added 4). 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.
Fasta._get_columns (cognitive 18) src/datasets/packaged_modules/fasta/fasta.py:176— Fasta._get_columns has cognitive complexity 18 (threshold 15). Drivers by points: if/else 7 (14 pts), loops 1 (2 pts), boolean chains 1, ternaries 1 (nesting depth added 8). 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.
Fastq._get_columns (cognitive 18) src/datasets/packaged_modules/fastq/fastq.py:202— Fastq._get_columns has cognitive complexity 18 (threshold 15). Drivers by points: if/else 7 (14 pts), loops 1 (2 pts), boolean chains 1, ternaries 1 (nesting depth added 8). 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.
GenBank._handle_features_line (cognitive 18) src/datasets/packaged_modules/genbank/genbank.py:481— GenBank._handle_features_line has cognitive complexity 18 (threshold 15). Drivers by points: if/else 8 (12 pts), boolean chains 6 (nesting depth added 4). 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.
json.parse_traces_info (cognitive 18) src/datasets/packaged_modules/json/json.py:438— json.parse_traces_info has cognitive complexity 18 (threshold 15). Drivers by points: if/else 6 (15 pts), boolean chains 2, loops 1 (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.
NumpyMultiprocessingGenerator.__iter__ (cognitive 18) src/datasets/utils/tf_utils.py:298— NumpyMultiprocessingGenerator.__iter__ has cognitive complexity 18 (threshold 15). Drivers by points: if/else 4 (9 pts), loops 5 (8 pts), boolean chains 1 (nesting depth added 8). 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.
ReadInstruction.to_spec (cognitive 17) src/datasets/arrow_reader.py:567— ReadInstruction.to_spec has cognitive complexity 17 (threshold 15). Drivers by points: ternaries 4 (12 pts), boolean chains 2, if/else 1 (2 pts), loops 1 (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.
ArrowWriter.write_examples_on_file (cognitive 17) src/datasets/arrow_writer.py:651— ArrowWriter.write_examples_on_file has cognitive complexity 17 (threshold 15). Drivers by points: if/else 7 (10 pts), ternaries 2 (6 pts), loops 1 (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.
GeneratorBasedBuilder._prepare_split_single (cognitive 17) src/datasets/builder.py:1510— GeneratorBasedBuilder._prepare_split_single has cognitive complexity 17 (threshold 15). Drivers by points: if/else 5 (10 pts), ternaries 2 (3 pts), boolean chains 2, error handling 1, loops 1 (nesting depth added 6). 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.
image.encode_np_array (cognitive 17) src/datasets/features/image.py:379— image.encode_np_array has cognitive complexity 17 (threshold 15). Drivers by points: if/else 9 (14 pts), loops 1 (2 pts), ternaries 1 (nesting depth added 6). 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.
DatasetInfosDict.to_dataset_card_data (cognitive 17) src/datasets/info.py:405— DatasetInfosDict.to_dataset_card_data has cognitive complexity 17 (threshold 15). Drivers by points: if/else 6 (10 pts), loops 2 (5 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.
HubBucketDatasetModuleFactory.get_module (cognitive 17) src/datasets/load.py:857— HubBucketDatasetModuleFactory.get_module has cognitive complexity 17 (threshold 15). Drivers by points: if/else 10 (12 pts), boolean chains 3, error handling 2 (nesting depth added 2). 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.
load.create_builder_configs_from_metadata_configs (cognitive 17) src/datasets/load.py:313— load.create_builder_configs_from_metadata_configs has cognitive complexity 17 (threshold 15). Drivers by points: if/else 3 (6 pts), ternaries 4 (6 pts), boolean chains 2, error handling 1 (2 pts), loops 1 (nesting depth added 6). 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.
hdf5._recursive_load_arrays (cognitive 17) src/datasets/packaged_modules/hdf5/hdf5.py:261— hdf5._recursive_load_arrays has cognitive complexity 17 (threshold 15). Drivers by points: if/else 7 (12 pts), loops 2 (3 pts), ternaries 1 (2 pts) (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.
Json._cast_table (cognitive 17) src/datasets/packaged_modules/json/json.py:109— Json._cast_table has cognitive complexity 17 (threshold 15). Drivers by points: if/else 3 (8 pts), loops 2 (4 pts), ternaries 1 (4 pts), boolean chains 1 (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.
json.get_user_prompt (cognitive 17) src/datasets/packaged_modules/json/json.py:487— json.get_user_prompt has cognitive complexity 17 (threshold 15). Drivers by points: if/else 8 (14 pts), boolean chains 3 (nesting depth added 6). 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.
Vortex._split_generators (cognitive 17) src/datasets/packaged_modules/vortex/vortex.py:255— Vortex._split_generators has cognitive complexity 17 (threshold 15). Drivers by points: if/else 5 (8 pts), error handling 1 (4 pts), loops 2 (4 pts), boolean chains 1 (nesting depth added 8). 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.
Dataset.load_from_disk (cognitive 16) src/datasets/arrow_dataset.py:1978— Dataset.load_from_disk has cognitive complexity 16 (threshold 15). Drivers by points: if/else 7 (10 pts), boolean chains 3, ternaries 3 (nesting depth added 3). 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.
DatasetInfo.__post_init__ (cognitive 16) src/datasets/info.py:167— DatasetInfo.__post_init__ has cognitive complexity 16 (threshold 15). Drivers by points: if/else 9 (11 pts), boolean chains 5 (nesting depth added 2). 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.
LocalDatasetModuleFactory.get_module (cognitive 16) src/datasets/load.py:422— LocalDatasetModuleFactory.get_module has cognitive complexity 16 (threshold 15). Drivers by points: if/else 10 (12 pts), boolean chains 3, ternaries 1 (nesting depth added 2). 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.
GenBank._get_columns (cognitive 16) src/datasets/packaged_modules/genbank/genbank.py:600— GenBank._get_columns has cognitive complexity 16 (threshold 15). Drivers by points: if/else 6 (12 pts), loops 1 (2 pts), boolean chains 1, ternaries 1 (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.
table._batch_accumulate_arrow_table_by_columns (cognitive 16) src/datasets/table.py:71— table._batch_accumulate_arrow_table_by_columns has cognitive complexity 16 (threshold 15). Drivers by points: if/else 7 (9 pts), loops 3 (5 pts), boolean chains 2 (nesting depth added 4). 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.
D4 · Code Duplication· Near-duplicate member family (5 members, 15 shared lines) · ×1
Near-duplicate member family (5 members, 15 shared lines) src/datasets/features/audio.py:238— src/datasets/features/audio.py:238-281 | src/datasets/features/bio_sequence.py:243-264 | src/datasets/features/nifti.py:299-332 | src/datasets/features/pdf.py:184-218 | src/datasets/features/video.py:243-292 — These 5 members are variants of one another: a block of 15 lines reported below appears in every one of them, and the pairwise near-duplicate rows they would otherwise produce are collapsed into this row. Read them as one construct written 5 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 5 times.
D4 · Code Duplication· Near-duplicate member family (4 members, 57 shared lines) · ×1
Near-duplicate member family (4 members, 57 shared lines) src/datasets/arrow_dataset.py:6088— src/datasets/arrow_dataset.py:6088-6260 | src/datasets/dataset_dict.py:1640-1807 | src/datasets/dataset_dict.py:2344-2512 | src/datasets/iterable_dataset.py:5338-5512 — These 4 members are variants of one another: a block of 57 lines reported below appears in every one of them, and the pairwise near-duplicate rows they would otherwise produce are collapsed into this row. Read them as one construct written 4 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 4 times.
D4 · Code Duplication· Near-duplicate member family (4 members, 18 shared lines) · ×1
Near-duplicate member family (4 members, 18 shared lines) src/datasets/features/audio.py:238— src/datasets/features/audio.py:238-281 | src/datasets/features/image.py:217-276 | src/datasets/features/mesh.py:163-213 | src/datasets/features/video.py:243-292 — These 4 members are variants of one another: a block of 18 lines reported below appears in every one of them, and the pairwise near-duplicate rows they would otherwise produce are collapsed into this row. Read them as one construct written 4 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 4 times.
D4 · Code Duplication· Near-duplicate member family (3 members, 33 shared lines) · ×1
Near-duplicate member family (3 members, 33 shared lines) src/datasets/packaged_modules/fasta/fasta.py:219— src/datasets/packaged_modules/fasta/fasta.py:219-289 | src/datasets/packaged_modules/fastq/fastq.py:245-313 | src/datasets/packaged_modules/genbank/genbank.py:652-722 — These 3 members are variants of one another: a block of 33 lines reported below appears in every one of them, and the pairwise near-duplicate rows they would otherwise produce are collapsed into this row. Read them as one construct written 3 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 3 times.
Near-duplicate member pair (74 shared lines) src/datasets/load.py:423— src/datasets/load.py:423-508 | src/datasets/load.py:858-954 — These two members are variants of one another: 74 of their lines are already reported as duplicated blocks below, spread through both bodies rather than gathered into one. Read them as a single construct written twice. The repair is at the members' grain — factor the shared pipeline into one implementation the two call with their differences as parameters or as an injected step, or, where the difference is systematic (sync against async, one transport against another), generate one from the other. Extracting the individual blocks below is not the same fix: it leaves the two bodies in place and the next edit still has to be made twice.
Near-duplicate member pair (35 shared lines) src/datasets/arrow_dataset.py:7380— src/datasets/arrow_dataset.py:7380-7425 | src/datasets/arrow_dataset.py:7438-7484 — These two members are variants of one another: 35 of their lines are already reported as duplicated blocks below, spread through both bodies rather than gathered into one. Read them as a single construct written twice. The repair is at the members' grain — factor the shared pipeline into one implementation the two call with their differences as parameters or as an injected step, or, where the difference is systematic (sync against async, one transport against another), generate one from the other. Extracting the individual blocks below is not the same fix: it leaves the two bodies in place and the next edit still has to be made twice.
D4 · Code Duplication· Members sharing a duplicated core (7 members, 50+ identical tokens) · ×1
Members sharing a duplicated core (7 members, 50+ identical tokens) src/datasets/features/bio_sequence.py:100— src/datasets/features/bio_sequence.py:100-130 | src/datasets/features/bio_structure.py:106-136 | src/datasets/features/image.py:99-137 | src/datasets/features/mesh.py:58-89 | src/datasets/features/nifti.py:111-148 | src/datasets/features/pdf.py:79-111 | src/datasets/features/video.py:106-151 — These 7 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 7 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 7 times.
D4 · Code Duplication· Members sharing a duplicated core (6 members, 50+ identical tokens) · ×1
Members sharing a duplicated core (6 members, 50+ identical tokens) src/datasets/features/audio.py:97— src/datasets/features/audio.py:97-162 | src/datasets/features/bio_sequence.py:100-130 | src/datasets/features/bio_structure.py:106-136 | src/datasets/features/image.py:99-137 | src/datasets/features/nifti.py:111-148 | src/datasets/features/pdf.py:79-111 — These 6 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 6 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 6 times.
D4 · Code Duplication· Members sharing a duplicated core (5 members, 50+ identical tokens) · ×1
Members sharing a duplicated core (5 members, 50+ identical tokens) src/datasets/packaged_modules/conll/conll.py:76— src/datasets/packaged_modules/conll/conll.py:76-99 | src/datasets/packaged_modules/csv/csv.py:152-167 | src/datasets/packaged_modules/eval/eval.py:22-51 | src/datasets/packaged_modules/json/json.py:80-107 | src/datasets/packaged_modules/text/text.py:52-71 — These 5 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 5 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 5 times.
Duplicated block (107–108 lines × 2) src/datasets/builder.py:1364— src/datasets/builder.py:1364-1471 | src/datasets/builder.py:1666-1772 — 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 (61–63 lines × 2) src/datasets/arrow_dataset.py:6688— src/datasets/arrow_dataset.py:6688-6750 | src/datasets/dataset_dict.py:2560-2620 — before extracting anything, compare `src/datasets/arrow_dataset.py` and `src/datasets/dataset_dict.py` as WHOLE FILES: this scan already matched 6 separate duplicated blocks between them, totalling at least 181 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 `src/datasets/arrow_dataset.py:6688` 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 (48 lines × 2) src/datasets/iterable_dataset.py:840— src/datasets/iterable_dataset.py:840-887 | src/datasets/iterable_dataset.py:915-962 — 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 (47 lines × 2) src/datasets/arrow_dataset.py:873— src/datasets/arrow_dataset.py:873-919 | src/datasets/arrow_dataset.py:991-1118 — 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 (42 lines × 2) src/datasets/arrow_dataset.py:6774— src/datasets/arrow_dataset.py:6774-6815 | src/datasets/dataset_dict.py:2643-2684 — before extracting anything, compare `src/datasets/arrow_dataset.py` and `src/datasets/dataset_dict.py` as WHOLE FILES: this scan already matched 6 separate duplicated blocks between them, totalling at least 181 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 `src/datasets/arrow_dataset.py:6774` 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 (36 lines × 6) src/datasets/features/audio.py:315— src/datasets/features/audio.py:315-350 | src/datasets/features/image.py:310-345 | src/datasets/features/mesh.py:243-278 | src/datasets/features/nifti.py:248-283 | src/datasets/features/pdf.py:252-287 | src/datasets/features/video.py:326-361 — 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. Read the line range as the matched WINDOW rather than a finished unit: at `src/datasets/features/audio.py:315` 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 (36 lines × 2) src/datasets/builder.py:1520— src/datasets/builder.py:1520-1555 | src/datasets/builder.py:1816-1851 — 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 (35 lines × 2) src/datasets/load.py:444— src/datasets/load.py:444-478 | src/datasets/load.py:890-924 — 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 (33 lines × 4) src/datasets/arrow_dataset.py:6226— src/datasets/arrow_dataset.py:6226-6258 | src/datasets/dataset_dict.py:1774-1806 | src/datasets/dataset_dict.py:2479-2511 | src/datasets/iterable_dataset.py:5478-5510 — before extracting anything, compare `src/datasets/arrow_dataset.py` and `src/datasets/dataset_dict.py` as WHOLE FILES: this scan already matched 6 separate duplicated blocks between them, totalling at least 181 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 `src/datasets/arrow_dataset.py:6226` 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. ★ These copies have DRIFTED, and that is worth reading before extracting anything: just before the matched lines, `src/datasets/dataset_dict.py:2478` calls `get` and `src/datasets/arrow_dataset.py:6225` 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 (31–33 lines × 2) src/datasets/packaged_modules/fasta/fasta.py:251— src/datasets/packaged_modules/fasta/fasta.py:251-281 | src/datasets/packaged_modules/fastq/fastq.py:273-305 — `src/datasets/packaged_modules/fasta/fasta.py` and `src/datasets/packaged_modules/fastq/fastq.py` are one unit implemented once per sibling directory, so they are most likely parallel implementations of one contract rather than a copy of each other — this scan matched 10 separate duplicated blocks between them, totalling at least 157 lines. If both are selected at run time, neither can be retired in favour of the other, and the lines that DIFFER between them are the reason both exist. The move that pays here is to hoist the identical part into a shared location the whole family can reach and give what differs a parameter or a seam, so a change lands once instead of once per sibling; extracting one helper per block leaves every sibling to drift on its own.
Duplicated block (27–31 lines × 2) src/datasets/arrow_dataset.py:942— src/datasets/arrow_dataset.py:942-968 | src/datasets/iterable_dataset.py:3273-3303 — 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 (31 lines × 2) src/datasets/utils/_dataset_viewer.py:29— src/datasets/utils/_dataset_viewer.py:29-59 | src/datasets/utils/_dataset_viewer.py:65-95 — 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 (3–30 lines × 3) src/datasets/builder.py:1479— src/datasets/builder.py:1479-1508 | src/datasets/builder.py:1780-1809 | src/datasets/packaged_modules/spark/spark.py:342-344 — there are 3 copies across 2 file(s) — more copies than files, so at least one file holds the block twice. Extract it once into a single shared function every call site can reach and call it from all 3 sites; resolving a subset leaves the remainder to drift apart. Read the line range as the matched WINDOW rather than a finished unit: at `src/datasets/builder.py:1479` 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 (29–30 lines × 2) src/datasets/features/image.py:217— src/datasets/features/image.py:217-246 | src/datasets/features/mesh.py:163-191 — before extracting anything, compare `src/datasets/features/image.py` and `src/datasets/features/mesh.py` as WHOLE FILES: this scan already matched 9 separate duplicated blocks between them, totalling at least 124 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. Note first that the copies are not typed on the same thing: the declarations holding them bind `storage` to `Union[pa.StringArray, pa.StructArray, pa.ListArray]` in one and `Union[pa.StringArray, pa.StructArray]` 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 (29 lines × 2) src/datasets/arrow_dataset.py:6021— src/datasets/arrow_dataset.py:6021-6049 | src/datasets/iterable_dataset.py:5265-5293 — 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 `src/datasets/arrow_dataset.py:6021` 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 (25 lines × 2) src/datasets/combine.py:126— src/datasets/combine.py:126-150 | src/datasets/combine.py:204-228 — 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 (21–25 lines × 2) src/datasets/packaged_modules/fasta/fasta.py:219— src/datasets/packaged_modules/fasta/fasta.py:219-243 | src/datasets/packaged_modules/fastq/fastq.py:245-265 — `src/datasets/packaged_modules/fasta/fasta.py` and `src/datasets/packaged_modules/fastq/fastq.py` are one unit implemented once per sibling directory, so they are most likely parallel implementations of one contract rather than a copy of each other — this scan matched 10 separate duplicated blocks between them, totalling at least 157 lines. If both are selected at run time, neither can be retired in favour of the other, and the lines that DIFFER between them are the reason both exist. The move that pays here is to hoist the identical part into a shared location the whole family can reach and give what differs a parameter or a seam, so a change lands once instead of once per sibling; extracting one helper per block leaves every sibling to drift on its own.
Duplicated block (24 lines × 4) src/datasets/arrow_dataset.py:6200— src/datasets/arrow_dataset.py:6200-6223 | src/datasets/dataset_dict.py:1749-1772 | src/datasets/dataset_dict.py:2454-2477 | src/datasets/iterable_dataset.py:5452-5475 — before extracting anything, compare `src/datasets/arrow_dataset.py` and `src/datasets/dataset_dict.py` as WHOLE FILES: this scan already matched 6 separate duplicated blocks between them, totalling at least 181 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 `src/datasets/arrow_dataset.py:6200` 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. ★ These copies have DRIFTED, and that is worth reading before extracting anything: just after the matched lines, `src/datasets/dataset_dict.py:2478` calls `get` and `src/datasets/arrow_dataset.py:6224` 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 (24 lines × 2) src/datasets/load.py:485— src/datasets/load.py:485-508 | src/datasets/load.py:931-954 — 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 `src/datasets/load.py:485` it runs out through the closing brace of the declaration holding it and carries on into the declaration that follows — the window is the tail of one member plus the head of the next, so no call can be substituted for those exact lines, and the smallest declaration that contains all of them is the type they sit in. The repeated unit is the member each site sits in: where those members' bodies are the same, move one whole member to the shared location and have the others delegate to it; where the copies are a run of near-identical overloads or wrappers that differ only in their signatures, the repetition IS the run — a one-line delegation has no helper inside it to lift — so generate the run from the set it enumerates, or accept it and keep each member's own documentation with it. 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 (20–22 lines × 4) src/datasets/utils/extract.py:93— src/datasets/utils/extract.py:93-112 | src/datasets/utils/extract.py:195-216 | src/datasets/utils/extract.py:252-273 | src/datasets/utils/extract.py:332-353 — 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. ★ These copies have DRIFTED, and that is worth reading before extracting anything: just after the matched lines, `src/datasets/utils/extract.py:114` calls `badlink`, `resolved`, `join` and `src/datasets/utils/extract.py:218` 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 (21 lines × 2) src/datasets/packaged_modules/fasta/fasta.py:179— src/datasets/packaged_modules/fasta/fasta.py:179-199 | src/datasets/packaged_modules/fastq/fastq.py:205-225 — `src/datasets/packaged_modules/fasta/fasta.py` and `src/datasets/packaged_modules/fastq/fastq.py` are one unit implemented once per sibling directory, so they are most likely parallel implementations of one contract rather than a copy of each other — this scan matched 10 separate duplicated blocks between them, totalling at least 157 lines. If both are selected at run time, neither can be retired in favour of the other, and the lines that DIFFER between them are the reason both exist. The move that pays here is to hoist the identical part into a shared location the whole family can reach and give what differs a parameter or a seam, so a change lands once instead of once per sibling; extracting one helper per block leaves every sibling to drift on its own. 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 (16–19 lines × 4) src/datasets/packaged_modules/fasta/fasta.py:85— src/datasets/packaged_modules/fasta/fasta.py:85-103 | src/datasets/packaged_modules/fastq/fastq.py:85-103 | src/datasets/packaged_modules/genbank/genbank.py:245-263 | src/datasets/packaged_modules/xml/xml.py:30-45 — `src/datasets/packaged_modules/fasta/fasta.py` and `src/datasets/packaged_modules/fastq/fastq.py` are one unit implemented once per sibling directory, so they are most likely parallel implementations of one contract rather than a copy of each other — this scan matched 10 separate duplicated blocks between them, totalling at least 157 lines. If both are selected at run time, neither can be retired in favour of the other, and the lines that DIFFER between them are the reason both exist. The move that pays here is to hoist the identical part into a shared location the whole family can reach and give what differs a parameter or a seam, so a change lands once instead of once per sibling; extracting one helper per block leaves every sibling to drift on its own. 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 (12–18 lines × 5) src/datasets/packaged_modules/conll/conll.py:76— src/datasets/packaged_modules/conll/conll.py:76-93 | src/datasets/packaged_modules/csv/csv.py:152-163 | src/datasets/packaged_modules/eval/eval.py:22-34 | src/datasets/packaged_modules/json/json.py:80-92 | src/datasets/packaged_modules/text/text.py:52-67 — the copies span different directories, so extracting a shared function means choosing where it lives: put it somewhere all 5 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 5 times. Read the line range as the matched WINDOW rather than a finished unit: at `src/datasets/packaged_modules/conll/conll.py:76` 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 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 (18 lines × 4) src/datasets/io/csv.py:47— src/datasets/io/csv.py:47-64 | src/datasets/io/json.py:53-70 | src/datasets/io/parquet.py:56-73 | src/datasets/io/text.py:41-58 — before extracting anything, compare `src/datasets/io/csv.py` and `src/datasets/io/json.py` as WHOLE FILES: this scan already matched 3 separate duplicated blocks between them, totalling at least 36 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 `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) src/datasets/arrow_dataset.py:7389— src/datasets/arrow_dataset.py:7389-7406 | src/datasets/arrow_dataset.py:7448-7465 — 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 (13–18 lines × 2) src/datasets/iterable_dataset.py:5590— src/datasets/iterable_dataset.py:5590-5602 | src/datasets/iterable_dataset.py:5670-5687 — 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 `src/datasets/iterable_dataset.py:5590` 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 (14–17 lines × 2) src/datasets/arrow_dataset.py:6758— src/datasets/arrow_dataset.py:6758-6774 | src/datasets/arrow_dataset.py:6858-6871 — 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 `src/datasets/arrow_dataset.py:6758` 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 (17 lines × 2) src/datasets/builder.py:1562— src/datasets/builder.py:1562-1578 | src/datasets/builder.py:1868-1884 — 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. ★ These copies have DRIFTED, and that is worth reading before extracting anything: just after the matched lines, `src/datasets/builder.py:1885` calls `isinstance` and `src/datasets/builder.py:1579` 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 (14 lines × 3) src/datasets/packaged_modules/fasta/fasta.py:276— src/datasets/packaged_modules/fasta/fasta.py:276-289 | src/datasets/packaged_modules/fastq/fastq.py:300-313 | src/datasets/packaged_modules/genbank/genbank.py:709-722 — `src/datasets/packaged_modules/fasta/fasta.py` and `src/datasets/packaged_modules/fastq/fastq.py` are one unit implemented once per sibling directory, so they are most likely parallel implementations of one contract rather than a copy of each other — this scan matched 10 separate duplicated blocks between them, totalling at least 157 lines. If both are selected at run time, neither can be retired in favour of the other, and the lines that DIFFER between them are the reason both exist. The move that pays here is to hoist the identical part into a shared location the whole family can reach and give what differs a parameter or a seam, so a change lands once instead of once per sibling; extracting one helper per block leaves every sibling to drift on its own.
Duplicated block (13 lines × 4) src/datasets/formatting/jax_formatter.py:109— src/datasets/formatting/jax_formatter.py:109-121 | src/datasets/formatting/np_formatter.py:69-81 | src/datasets/formatting/tf_formatter.py:73-85 | src/datasets/formatting/torch_formatter.py:79-91 — before extracting anything, compare `src/datasets/formatting/jax_formatter.py` and `src/datasets/formatting/tf_formatter.py` as WHOLE FILES: this scan already matched 5 separate duplicated blocks between them, totalling at least 45 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. ★ These copies have DRIFTED, and that is worth reading before extracting anything: just before the matched lines, `src/datasets/formatting/torch_formatter.py:78` calls `transpose` and `src/datasets/formatting/jax_formatter.py:108` 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 (13 lines × 2) src/datasets/arrow_dataset.py:6008— src/datasets/arrow_dataset.py:6008-6020 | src/datasets/iterable_dataset.py:5244-5256 — 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 (12–13 lines × 2) src/datasets/iterable_dataset.py:1169— src/datasets/iterable_dataset.py:1169-1180 | src/datasets/iterable_dataset.py:1192-1204 — 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 `src/datasets/iterable_dataset.py:1192` 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 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 (11 lines × 2) src/datasets/arrow_dataset.py:7408— src/datasets/arrow_dataset.py:7408-7418 | src/datasets/arrow_dataset.py:7467-7477 — 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 `src/datasets/arrow_dataset.py:7408` 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 (9–10 lines × 3) src/datasets/packaged_modules/fasta/fasta.py:238— src/datasets/packaged_modules/fasta/fasta.py:238-246 | src/datasets/packaged_modules/fastq/fastq.py:260-268 | src/datasets/packaged_modules/genbank/genbank.py:666-675 — `src/datasets/packaged_modules/fasta/fasta.py` and `src/datasets/packaged_modules/fastq/fastq.py` are one unit implemented once per sibling directory, so they are most likely parallel implementations of one contract rather than a copy of each other — this scan matched 10 separate duplicated blocks between them, totalling at least 157 lines. If both are selected at run time, neither can be retired in favour of the other, and the lines that DIFFER between them are the reason both exist. The move that pays here is to hoist the identical part into a shared location the whole family can reach and give what differs a parameter or a seam, so a change lands once instead of once per sibling; extracting one helper per block leaves every sibling to drift on its own.
Duplicated block (10 lines × 3) src/datasets/packaged_modules/fasta/fasta.py:256— src/datasets/packaged_modules/fasta/fasta.py:256-265 | src/datasets/packaged_modules/fastq/fastq.py:278-287 | src/datasets/packaged_modules/genbank/genbank.py:695-704 — `src/datasets/packaged_modules/fasta/fasta.py` and `src/datasets/packaged_modules/fastq/fastq.py` are one unit implemented once per sibling directory, so they are most likely parallel implementations of one contract rather than a copy of each other — this scan matched 10 separate duplicated blocks between them, totalling at least 157 lines. If both are selected at run time, neither can be retired in favour of the other, and the lines that DIFFER between them are the reason both exist. The move that pays here is to hoist the identical part into a shared location the whole family can reach and give what differs a parameter or a seam, so a change lands once instead of once per sibling; extracting one helper per block leaves every sibling to drift on its own.
Duplicated block (8–9 lines × 7) src/datasets/features/audio.py:270— src/datasets/features/audio.py:270-278 | src/datasets/features/bio_sequence.py:254-261 | src/datasets/features/image.py:257-264 | src/datasets/features/mesh.py:202-209 | src/datasets/features/nifti.py:322-329 | src/datasets/features/pdf.py:208-215 | src/datasets/features/video.py:273-280 — 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 7 call sites, so a change lands once. Read the line range as the matched WINDOW rather than a finished unit: at `src/datasets/features/audio.py:270` 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 (8–9 lines × 3) src/datasets/arrow_dataset.py:915— src/datasets/arrow_dataset.py:915-923 | src/datasets/arrow_dataset.py:964-972 | src/datasets/iterable_dataset.py:3299-3306 — there are 3 copies across 2 file(s) — more copies than files, so at least one file holds the block twice. Extract it once into a single shared function every call site can reach and call it from all 3 sites; resolving a subset leaves the remainder to drift apart. 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 (7–9 lines × 3) src/datasets/packaged_modules/conll/conll.py:102— src/datasets/packaged_modules/conll/conll.py:102-108 | src/datasets/packaged_modules/text/text.py:74-82 | src/datasets/packaged_modules/xml/xml.py:48-56 — 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. ★ These copies have DRIFTED, and that is worth reading before extracting anything: just after the matched lines, `src/datasets/packaged_modules/text/text.py:84` calls `cast`, `schema` and `src/datasets/packaged_modules/conll/conll.py:110` 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 (3–9 lines × 6) src/datasets/features/audio.py:279— src/datasets/features/audio.py:279-281 | src/datasets/features/image.py:267-275 | src/datasets/features/mesh.py:210-213 | src/datasets/features/nifti.py:330-332 | src/datasets/features/pdf.py:216-218 | src/datasets/features/video.py:283-291 — 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. Read the line range as the matched WINDOW rather than a finished unit: at `src/datasets/features/image.py:267` 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. 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. ★ These copies have DRIFTED, and that is worth reading before extracting anything: just before the matched lines, `src/datasets/features/image.py:266` calls `from_arrays`, `is_null`, `is_list` and `src/datasets/features/nifti.py:330` 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 (3–9 lines × 3) src/datasets/packaged_modules/fasta/fasta.py:71— src/datasets/packaged_modules/fasta/fasta.py:71-79 | src/datasets/packaged_modules/fastq/fastq.py:71-79 | src/datasets/packaged_modules/genbank/genbank.py:238-240 — `src/datasets/packaged_modules/fasta/fasta.py` and `src/datasets/packaged_modules/fastq/fastq.py` are one unit implemented once per sibling directory, so they are most likely parallel implementations of one contract rather than a copy of each other — this scan matched 10 separate duplicated blocks between them, totalling at least 157 lines. If both are selected at run time, neither can be retired in favour of the other, and the lines that DIFFER between them are the reason both exist. The move that pays here is to hoist the identical part into a shared location the whole family can reach and give what differs a parameter or a seam, so a change lands once instead of once per sibling; extracting one helper per block leaves every sibling to drift on its own. 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 (7–8 lines × 8) src/datasets/features/audio.py:205— src/datasets/features/audio.py:205-212 | src/datasets/features/audio.py:312-318 | src/datasets/features/image.py:307-313 | src/datasets/features/mesh.py:131-137 | src/datasets/features/mesh.py:240-246 | src/datasets/features/nifti.py:245-251 | src/datasets/features/pdf.py:249-255 | src/datasets/features/video.py:323-329 — there are 8 copies across 6 file(s) — more copies than files, so at least one file holds the block twice. Extract it once into a single shared function every call site can reach and call it from all 8 sites; resolving a subset leaves the remainder to drift apart.
Duplicated block (8 lines × 5) src/datasets/features/bio_sequence.py:111— src/datasets/features/bio_sequence.py:111-118 | src/datasets/features/bio_structure.py:117-124 | src/datasets/features/mesh.py:69-76 | src/datasets/features/nifti.py:122-129 | src/datasets/features/pdf.py:90-97 — 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. Read the line range as the matched WINDOW rather than a finished unit: at `src/datasets/features/bio_sequence.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. 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 lines × 5) src/datasets/features/audio.py:238— src/datasets/features/audio.py:238-256 | src/datasets/features/bio_sequence.py:243-249 | src/datasets/features/nifti.py:299-317 | src/datasets/features/pdf.py:184-203 | src/datasets/features/video.py:243-262 — 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. Note first that the copies are not typed on the same thing: the declarations holding them bind `storage` to `Union[pa.StringArray, pa.StructArray]` in one and `Union[pa.StringArray, pa.StructArray, pa.BinaryArray]` 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 (7 lines × 4) src/datasets/arrow_dataset.py:914— src/datasets/arrow_dataset.py:914-920 | src/datasets/arrow_dataset.py:963-969 | src/datasets/arrow_dataset.py:1113-1119 | src/datasets/iterable_dataset.py:3298-3304 — there are 4 copies across 2 file(s) — more copies than files, so at least one file holds the block twice. Extract it once into a single shared function every call site can reach and call it from all 4 sites; resolving a subset leaves the remainder to drift apart.
Duplicated block (6 lines × 6) src/datasets/features/audio.py:156— src/datasets/features/audio.py:156-162 | src/datasets/features/bio_sequence.py:125-130 | src/datasets/features/bio_structure.py:131-136 | src/datasets/features/image.py:131-137 | src/datasets/features/nifti.py:138-144 | src/datasets/features/pdf.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 all 6 call sites, so a change lands once. Read the line range as the matched WINDOW rather than a finished unit: at `src/datasets/features/audio.py:156` 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 (6 lines × 3) src/datasets/formatting/jax_formatter.py:144— src/datasets/formatting/jax_formatter.py:144-149 | src/datasets/formatting/tf_formatter.py:99-104 | src/datasets/formatting/torch_formatter.py:100-105 — before extracting anything, compare `src/datasets/formatting/jax_formatter.py` and `src/datasets/formatting/tf_formatter.py` as WHOLE FILES: this scan already matched 5 separate duplicated blocks between them, totalling at least 45 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 (5 lines × 3) src/datasets/load.py:438— src/datasets/load.py:438-442 | src/datasets/load.py:635-639 | src/datasets/load.py:884-888 — 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 (5 lines × 4) src/datasets/formatting/jax_formatter.py:162— src/datasets/formatting/jax_formatter.py:162-166 | src/datasets/formatting/np_formatter.py:111-115 | src/datasets/formatting/tf_formatter.py:117-121 | src/datasets/formatting/torch_formatter.py:118-122 — before extracting anything, compare `src/datasets/formatting/jax_formatter.py` and `src/datasets/formatting/tf_formatter.py` as WHOLE FILES: this scan already matched 5 separate duplicated blocks between them, totalling at least 45 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 `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.
README/code drift docs/source/about_dataset_features.mdx:49— docs/source/about_dataset_features.mdx links to `./process#flatten`, but `docs/source/process` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong.
README/code drift docs/source/about_dataset_load.mdx:21— docs/source/about_dataset_load.mdx names `datasets.packaged_modules.csv.Csv`, but no file or folder `datasets.packaged_modules.csv.Csv` exists. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong.
README/code drift docs/source/about_dataset_load.mdx:22— docs/source/about_dataset_load.mdx names `datasets.packaged_modules.json.Json`, but no file or folder `datasets.packaged_modules.json.Json` exists. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong.
README/code drift docs/source/about_dataset_load.mdx:25— docs/source/about_dataset_load.mdx names `datasets.packaged_modules.sql.Sql`, but no file or folder `datasets.packaged_modules.sql.Sql` exists. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong.
README/code drift docs/source/about_dataset_load.mdx:31— docs/source/about_dataset_load.mdx links to `./upload_dataset`, but `docs/source/upload_dataset` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong.
README/code drift docs/source/about_mapstyle_vs_iterable.mdx:39— docs/source/about_mapstyle_vs_iterable.mdx links to `./stream`, but `docs/source/stream` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong.
README/code drift docs/source/about_mapstyle_vs_iterable.mdx:87— docs/source/about_mapstyle_vs_iterable.mdx links to `./tabular_load`, but `docs/source/tabular_load` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong.
README/code drift docs/source/about_mapstyle_vs_iterable.mdx:87— docs/source/about_mapstyle_vs_iterable.mdx links to `./nlp_load`, but `docs/source/nlp_load` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong.
README/code drift docs/source/about_mapstyle_vs_iterable.mdx:87— docs/source/about_mapstyle_vs_iterable.mdx links to `./image_load`, but `docs/source/image_load` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong.
README/code drift docs/source/about_mapstyle_vs_iterable.mdx:87— docs/source/about_mapstyle_vs_iterable.mdx links to `./audio_load]`, but `docs/source/audio_load]` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong.
README/code drift docs/source/about_mapstyle_vs_iterable.mdx:237— docs/source/about_mapstyle_vs_iterable.mdx links to `./use_with_pytorch#stream-data`, but `docs/source/use_with_pytorch` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong.
README/code drift docs/source/access.mdx:157— docs/source/access.mdx links to `access/#slicing`, but `docs/source/access` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong.
README/code drift docs/source/access.mdx:161— docs/source/access.mdx links to `about_mapstyle_vs_iterable`, but `docs/source/about_mapstyle_vs_iterable` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong.
README/code drift docs/source/access.mdx:163— docs/source/access.mdx links to `process`, but `docs/source/process` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong.
README/code drift docs/source/access.mdx:163— docs/source/access.mdx links to `stream`, but `docs/source/stream` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong.
README/code drift docs/source/audio_dataset.mdx:50— docs/source/audio_dataset.mdx links to `repository_structure#split-pattern-hierarchy`, but `docs/source/repository_structure` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong.
README/code drift docs/source/audio_load.mdx:9— docs/source/audio_load.mdx links to `./installation#audio`, but `docs/source/installation` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong.
README/code drift docs/source/audio_load.mdx:89— docs/source/audio_load.mdx links to `./audio_dataset`, but `docs/source/audio_dataset` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong.
README/code drift docs/source/cache.mdx:50— docs/source/cache.mdx documents the environment variable `HF_HUB_CACHE`, but no code, script or configuration file names it. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong.
README/code drift docs/source/cli.mdx:23— docs/source/cli.mdx links to `repository_structure`, but `docs/source/repository_structure` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong.
README/code drift docs/source/create_dataset.mdx:3— docs/source/create_dataset.mdx links to `stream`, but `docs/source/stream` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong.
README/code drift docs/source/create_dataset.mdx:3— docs/source/create_dataset.mdx links to `upload_dataset#upload-with-the-hub-ui`, but `docs/source/upload_dataset` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong.
README/code drift docs/source/document_dataset.mdx:13— docs/source/document_dataset.mdx links to `repository_structure#split-pattern-hierarchy`, but `docs/source/repository_structure` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong.
README/code drift docs/source/document_load.mdx:178— docs/source/document_load.mdx links to `./document_dataset`, but `docs/source/document_dataset` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong.
README/code drift docs/source/harbor_load.mdx:30— docs/source/harbor_load.mdx links to `./installation#harbor`, but `docs/source/installation` does not exist. Checked without a model against the repository: its files and folders, the scripts and dependencies of every package.json, the projects, namespaces, package references and target frameworks of the .NET build, the SDK and Node pins, and the identifiers of every file that is not a document; the documents' own text never counts. A footprint generated at build time, read only by a platform, or living outside this repository is not seen, and this row is then wrong.
Duplicated predicate src/datasets/arrow_writer.py:305— `config.PDFPLUMBER_AVAILABLE and "pdfplumber" in sys.modules` appears character-identically in 2 files — src/datasets/arrow_writer.py, src/datasets/features/features.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 src/datasets/arrow_writer.py:295— `config.PIL_AVAILABLE and "PIL" in sys.modules` appears character-identically in 6 files — src/datasets/arrow_writer.py, src/datasets/features/features.py, src/datasets/formatting/jax_formatter.py, src/datasets/formatting/np_formatter.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 src/datasets/features/features.py:330— `config.TORCHCODEC_AVAILABLE and "torchcodec" in sys.modules` appears character-identically in 5 files — src/datasets/features/features.py, src/datasets/formatting/jax_formatter.py, src/datasets/formatting/np_formatter.py, src/datasets/formatting/tf_formatter.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 src/datasets/formatting/jax_formatter.py:109— `config.TORCHVISION_AVAILABLE and "torchvision" in sys.modules` appears character-identically in 4 files — src/datasets/formatting/jax_formatter.py, src/datasets/formatting/np_formatter.py, src/datasets/formatting/tf_formatter.py, src/datasets/formatting/torch_formatter.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 src/datasets/features/features.py:311— `config.TORCH_AVAILABLE and "torch" in sys.modules` appears character-identically in 4 files — src/datasets/features/features.py, src/datasets/formatting/jax_formatter.py, src/datasets/formatting/np_formatter.py, src/datasets/formatting/tf_formatter.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 src/datasets/builder.py:793— `download_mode or DownloadMode.REUSE_DATASET_IF_EXISTS` appears character-identically in 2 files — src/datasets/builder.py, src/datasets/load.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 src/datasets/packaged_modules/fasta/fasta.py:77— `features is not None and self.config.columns is not None` appears character-identically in 2 files — src/datasets/packaged_modules/fasta/fasta.py, src/datasets/packaged_modules/fastq/fastq.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 src/datasets/formatting/jax_formatter.py:89— `isinstance(value, (np.character, np.ndarray)) and np.issubdtype(value.dtype, np.character)` appears character-identically in 3 files — src/datasets/formatting/jax_formatter.py, src/datasets/formatting/np_formatter.py, src/datasets/formatting/torch_formatter.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 src/datasets/formatting/jax_formatter.py:101— `isinstance(value, (np.number, np.ndarray)) and np.issubdtype(value.dtype, np.floating)` appears character-identically in 3 files — src/datasets/formatting/jax_formatter.py, src/datasets/formatting/tf_formatter.py, src/datasets/formatting/torch_formatter.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 src/datasets/formatting/jax_formatter.py:94— `isinstance(value, (np.number, np.ndarray)) and np.issubdtype(value.dtype, np.integer)` appears character-identically in 3 files — src/datasets/formatting/jax_formatter.py, src/datasets/formatting/tf_formatter.py, src/datasets/formatting/torch_formatter.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 src/datasets/features/audio.py:327— `local_files and is_local_path(x["path"])` appears character-identically in 6 files — src/datasets/features/audio.py, src/datasets/features/image.py, src/datasets/features/mesh.py, src/datasets/features/nifti.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 src/datasets/packaged_modules/fasta/fasta.py:257— `max_batch_bytes is not None and batch_bytes > 0 and batch_bytes + record_bytes > max_batch_bytes` appears character-identically in 3 files — src/datasets/packaged_modules/fasta/fasta.py, src/datasets/packaged_modules/fastq/fastq.py, src/datasets/packaged_modules/genbank/genbank.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 src/datasets/dataset_dict.py:2431— `max_shard_size is not None and num_shards is not None` appears character-identically in 2 files — src/datasets/dataset_dict.py, src/datasets/iterable_dataset.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 src/datasets/download/download_manager.py:212— `os.environ.get("HF_DATASETS_STACK_MULTIPROCESSING_DOWNLOAD_PROGRESS_BARS") == "1" and multiprocessing.current_process()._identity` appears character-identically in 2 files — src/datasets/download/download_manager.py, REDACTED. 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 src/datasets/features/mesh.py:295— `path is not None and os.path.isfile(path)` appears character-identically in 2 files — src/datasets/features/mesh.py, src/datasets/features/video.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 src/datasets/features/audio.py:327— `remote_files and is_remote_url(x["path"])` appears character-identically in 6 files — src/datasets/features/audio.py, src/datasets/features/image.py, src/datasets/features/mesh.py, src/datasets/features/nifti.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 src/datasets/dataset_dict.py:1789— `revision is not None and not revision.startswith("refs/pr/")` appears character-identically in 2 files — src/datasets/dataset_dict.py, src/datasets/iterable_dataset.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 src/datasets/packaged_modules/fasta/fasta.py:180— `self.config.columns is None and features is not None` appears character-identically in 3 files — src/datasets/packaged_modules/fasta/fasta.py, src/datasets/packaged_modules/fastq/fastq.py, src/datasets/packaged_modules/genbank/genbank.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 src/datasets/packaged_modules/parquet/parquet.py:99— `self.config.columns is not None and self.config.features is not None and set(self.config.columns) != set(self.config.features)` appears character-identically in 2 files — src/datasets/packaged_modules/parquet/parquet.py, src/datasets/packaged_modules/vortex/vortex.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 src/datasets/packaged_modules/parquet/parquet.py:146— `self.config.columns is not None and set(self.config.columns) != set(self.info.features)` appears character-identically in 2 files — src/datasets/packaged_modules/parquet/parquet.py, src/datasets/packaged_modules/vortex/vortex.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 src/datasets/packaged_modules/parquet/parquet.py:186— `self.config.features is not None and self.config.columns is not None` appears character-identically in 2 files — src/datasets/packaged_modules/parquet/parquet.py, src/datasets/packaged_modules/vortex/vortex.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 src/datasets/formatting/formatting.py:274— `self.features and column_name in self.features and self.features._column_requires_decoding[column_name]` appears character-identically in 2 files — src/datasets/formatting/formatting.py, src/datasets/formatting/polars_formatter.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 src/datasets/io/csv.py:120— `self.num_proc is None or self.num_proc == 1` appears character-identically in 3 files — src/datasets/io/csv.py, src/datasets/io/json.py, src/datasets/io/sql.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 src/datasets/features/audio.py:156— `value.get("bytes") is not None or value.get("path") is not None` appears character-identically in 7 files — src/datasets/features/audio.py, src/datasets/features/bio_sequence.py, src/datasets/features/bio_structure.py, src/datasets/features/image.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 src/datasets/features/audio.py:136— `value.get("path") is not None and os.path.isfile(value["path"])` appears character-identically in 6 files — src/datasets/features/audio.py, src/datasets/features/bio_sequence.py, src/datasets/features/bio_structure.py, src/datasets/features/image.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.
Orphaned file src/datasets/inspect.py:1— No one still active in this repository holds living knowledge of this file: everyone who made a meaningful change to it has gone quiet, or what the current team did to it (moves, reformatting, log-level or import edits, renames) never needed understanding it. If it breaks, no one currently understands it. Attach the read to the next change that touches it: have a second person review that change, and leave behind a short comment or test recording what the file is for.
Orphaned file src/datasets/packaged_modules/spark/spark.py:1— No one still active in this repository holds living knowledge of this file: everyone who made a meaningful change to it has gone quiet, or what the current team did to it (moves, reformatting, log-level or import edits, renames) never needed understanding it. If it breaks, no one currently understands it. Attach the read to the next change that touches it: have a second person review that change, and leave behind a short comment or test recording what the file is for.
Orphaned file src/datasets/packaged_modules/iceberg/iceberg.py:1— No one still active in this repository holds living knowledge of this file: everyone who made a meaningful change to it has gone quiet, or what the current team did to it (moves, reformatting, log-level or import edits, renames) never needed understanding it. If it breaks, no one currently understands it. Attach the read to the next change that touches it: have a second person review that change, and leave behind a short comment or test recording what the file is for.
Orphaned file benchmarks/benchmark_array_xd.py:1— No one still active in this repository holds living knowledge of this file: everyone who made a meaningful change to it has gone quiet, or what the current team did to it (moves, reformatting, log-level or import edits, renames) never needed understanding it. If it breaks, no one currently understands it. Attach the read to the next change that touches it: have a second person review that change, and leave behind a short comment or test recording what the file is for.
Orphaned file src/datasets/formatting/polars_formatter.py:1— No one still active in this repository holds living knowledge of this file: everyone who made a meaningful change to it has gone quiet, or what the current team did to it (moves, reformatting, log-level or import edits, renames) never needed understanding it. If it breaks, no one currently understands it. Attach the read to the next change that touches it: have a second person review that change, and leave behind a short comment or test recording what the file is for.
Orphaned file src/datasets/parallel/parallel.py:1— No one still active in this repository holds living knowledge of this file: everyone who made a meaningful change to it has gone quiet, or what the current team did to it (moves, reformatting, log-level or import edits, renames) never needed understanding it. If it breaks, no one currently understands it. Attach the read to the next change that touches it: have a second person review that change, and leave behind a short comment or test recording what the file is for.
Orphaned file src/datasets/packaged_modules/sql/sql.py:1— No one still active in this repository holds living knowledge of this file: everyone who made a meaningful change to it has gone quiet, or what the current team did to it (moves, reformatting, log-level or import edits, renames) never needed understanding it. If it breaks, no one currently understands it. Attach the read to the next change that touches it: have a second person review that change, and leave behind a short comment or test recording what the file is for.
Orphaned file src/datasets/utils/tqdm.py:1— No one still active in this repository holds living knowledge of this file: everyone who made a meaningful change to it has gone quiet, or what the current team did to it (moves, reformatting, log-level or import edits, renames) never needed understanding it. If it breaks, no one currently understands it. Attach the read to the next change that touches it: have a second person review that change, and leave behind a short comment or test recording what the file is for.
Orphaned file src/datasets/utils/sharding.py:1— No one still active in this repository holds living knowledge of this file: everyone who made a meaningful change to it has gone quiet, or what the current team did to it (moves, reformatting, log-level or import edits, renames) never needed understanding it. If it breaks, no one currently understands it. Attach the read to the next change that touches it: have a second person review that change, and leave behind a short comment or test recording what the file is for.
Orphaned file src/datasets/utils/stratify.py:1— No one still active in this repository holds living knowledge of this file: everyone who made a meaningful change to it has gone quiet, or what the current team did to it (moves, reformatting, log-level or import edits, renames) never needed understanding it. If it breaks, no one currently understands it. Attach the read to the next change that touches it: have a second person review that change, and leave behind a short comment or test recording what the file is for.
Orphaned file utils/release.py:1— No one still active in this repository holds living knowledge of this file: everyone who made a meaningful change to it has gone quiet, or what the current team did to it (moves, reformatting, log-level or import edits, renames) never needed understanding it. If it breaks, no one currently understands it. Attach the read to the next change that touches it: have a second person review that change, and leave behind a short comment or test recording what the file is for.
Orphaned file benchmarks/benchmark_iterating.py:1— No one still active in this repository holds living knowledge of this file: everyone who made a meaningful change to it has gone quiet, or what the current team did to it (moves, reformatting, log-level or import edits, renames) never needed understanding it. If it breaks, no one currently understands it. Attach the read to the next change that touches it: have a second person review that change, and leave behind a short comment or test recording what the file is for.
Orphaned file src/datasets/utils/deprecation_utils.py:1— No one still active in this repository holds living knowledge of this file: everyone who made a meaningful change to it has gone quiet, or what the current team did to it (moves, reformatting, log-level or import edits, renames) never needed understanding it. If it breaks, no one currently understands it. Attach the read to the next change that touches it: have a second person review that change, and leave behind a short comment or test recording what the file is for.
Orphaned file src/datasets/utils/version.py:1— No one still active in this repository holds living knowledge of this file: everyone who made a meaningful change to it has gone quiet, or what the current team did to it (moves, reformatting, log-level or import edits, renames) never needed understanding it. If it breaks, no one currently understands it. Attach the read to the next change that touches it: have a second person review that change, and leave behind a short comment or test recording what the file is for.
Orphaned file src/datasets/naming.py:1— No one still active in this repository holds living knowledge of this file: everyone who made a meaningful change to it has gone quiet, or what the current team did to it (moves, reformatting, log-level or import edits, renames) never needed understanding it. If it breaks, no one currently understands it. Attach the read to the next change that touches it: have a second person review that change, and leave behind a short comment or test recording what the file is for.
Orphaned file benchmarks/benchmark_map_filter.py:1— No one still active in this repository holds living knowledge of this file: everyone who made a meaningful change to it has gone quiet, or what the current team did to it (moves, reformatting, log-level or import edits, renames) never needed understanding it. If it breaks, no one currently understands it. Attach the read to the next change that touches it: have a second person review that change, and leave behind a short comment or test recording what the file is for.
Off-boarding risk: anonymized user #1 — If anonymized user #1 becomes unavailable, 4 significant file(s) lose their only recent owner: src/datasets/packaged_modules/fastq/fastq.py, src/datasets/packaged_modules/fasta/fasta.py, src/datasets/features/bio_sequence.py, src/datasets/features/bio_structure.py. Pair on, review, or document these before any departure.
D16 · Bus Factor· Further sole-owners (lower concentration) · ×1
Further sole-owners (lower concentration) — 2 other contributor(s) are each the sole owner of a small amount of code below the off-boarding threshold — folded into the bus-factor score and metrics (7 single-owned of 93 analysed files in total, counted over production source files of roughly 2,400 bytes or more, excluding vendored, generated and example/demo trees and test files identified by path convention, largest first; 93 of the 163 production source files in this repository met that bar). They are anonymized user #2 (2 file(s)), anonymized user #3 (1 file(s)) — spread or document their files in the same way, at lower priority than the named off-boarding risks above. The files folded here: src/datasets/info.py, src/datasets/packaged_modules/parquet/parquet.py, src/datasets/packaged_modules/conll/conll.py.
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)` (43983 LoC, 389 module-visible types across 44 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.
D34 · Knowledge Freshness· Further orphaned files (smaller) · ×1
Further orphaned files (smaller) — 16 smaller file(s) also have no living knowledge — folded into the freshness score and metrics rather than raised one row each — most significant first: src/datasets/inspect.py, src/datasets/packaged_modules/spark/spark.py, src/datasets/packaged_modules/iceberg/iceberg.py, benchmarks/benchmark_array_xd.py, src/datasets/formatting/polars_formatter.py, src/datasets/parallel/parallel.py, src/datasets/packaged_modules/sql/sql.py, src/datasets/utils/tqdm.py (and 8 more) (18 orphaned of 93 analysed files in total, counted over production source files of roughly 2,400 bytes or more, excluding vendored, generated and example/demo trees and test files identified by path convention, largest first; 93 of the 163 production source files in this repository met that bar). Attach the read to the next change that touches one of them: have a second person review that change, and leave behind a short comment or test recording what the file is for, so the knowledge comes back at the cost of a change you were making anyway.
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.
M2 · Architecture documentation· No architecture diagram/doc · ×1
No architecture diagram/doc — No C4/Structurizr/PlantUML/Mermaid/Graphviz/D2 diagram, no drawn diagram named for the architecture, no file named `architecture` or `design` in any markup this check reads, and nothing in the README, docs or contributor guides that announces the shape — no `## Architecture` heading, no "architecture overview"/"high-level design" phrasing, no "the architecture is …" introduction, no guided code tour. A shape laid out in prose that never names itself as the architecture is not visible to this check, and neither is one kept outside the repository, so this row reports the absence of a re-findable shape document — not evidence that nobody wrote the shape down.
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 10510 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.
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.)
X23 · Unguarded diagnostic materialisation· Diagnostic log message is built whether or not it is wanted · ×1
Diagnostic log message is built whether or not it is wanted src/datasets/builder.py:1037— `debug` at line 1037 is called with an argument built by `"…".join(self.info.splits)` (line 1037), whose cost grows with the collection it walks. Python evaluates a call's arguments BEFORE entering the method, so that join/projection runs in full every time this line is reached — and then the level check inside the logger discards the string it produced, because debug is a level a shipped configuration normally leaves off. Nothing here decides beforehand whether the message is wanted: no `isEnabledFor(logging.DEBUG)` test, no debug flag, no `lambda` the logger calls only when the level is on.
Interpolated log message defeats structured logging src/datasets/utils/logging.py:59— `warning` at line 59 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.warning("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.warning("order moved", order_id=order_id, status=status)` with structlog.
Silent fallback default on parse failure src/datasets/utils/version.py:75— `__eq__` returns `False` when `(TypeError, ValueError)` is raised, with no log or raise — a malformed value silently becomes that default. Log the input or fail validation. 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.
Dependency hygiene PARTLY measured — Python dependencies read, no exact pin to grade for currency — This repository's Python dependencies were read: 12 shipped distribution(s) across 1 manifest(s) (0 development or optional declaration(s) excluded, because a consumer does not install them). Whether any of them is behind its current release is the dependency-CURRENCY question, and no declaration here carries an EXACT pin to ask it about — every one is a floor or a range, which installs the newest release it admits and therefore cannot be behind one. That is a healthy declaration style, NOT a defect and NOT a gap in this analyzer; there is simply no currency question to answer. This is NOT a finding that these dependencies are current or healthy. Lockfiles are deliberately not read (poetry.lock, uv.lock, Pipfile.lock, pdm.lock): the four disagree about dev/optional membership, and misreading one would charge a test-only package a consumer never receives. Known CVEs in the same dependency graph are a separate question, reported under D30.
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.
none (dependency manifest found, not scanned for vulnerabilities here)
—
none (dependency manifest found, not scanned for vulnerabilities here): not applicable — Not scored — no dependency manifest in a supported ecosystem was read for this repository. A gap in the analyzer's language coverage, NOT a finding that the repository is free of vulnerable dependencies (a Python pyproject.toml/requirements.txt (pip/uv/Poetry) — not scanned yet).
trivy: not applicable — No Infrastructure-as-Code or container manifests found (Dockerfile, Docker Compose, Terraform, Kubernetes/Helm, CloudFormation, ARM, Bicep, Ansible); nothing to scan.
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.
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.
none (dependency manifest found, not scanned for vulnerabilities here)
—
none (dependency manifest found, not scanned for vulnerabilities here): not applicable — Not scored — no dependency manifest in a supported ecosystem was read for this repository. A gap in the analyzer's language coverage, NOT a finding that the repository is free of vulnerable dependencies (a Python pyproject.toml/requirements.txt (pip/uv/Poetry) — not scanned yet).
0
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Run 01a15abe-1ae1-75ec-b786-c531b1a862de · every finding is also locatable in findings.md, and the complete scoring record (with exit codes + durations) in sidecar.json.
Issues: 11 · Warnings: 390 · Recommendations: 96 · Info: 1 — Appendix A · all findings · full markdown report.
Generated by Watchdog — deterministic code-health analysis. 20-10-2026 @ 19:16 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.