numpy/numpy

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https://github.com/numpy/numpy.git · scanned 2026-05-16 12:55 UTC (1 day, 7 hours ago) · 10 languages

273 findings (20 legacy + 253 scanner) 2/10 scanners ran 88th percentile · Python · huge (>500K LoC) Scanner says 99 (lower by 17)

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UNIFIED Repobility · multi-layer engine · AI coders

Complete repo analysis

Last scanned 1 day, 10 hours ago · v1 · 265 findings from 2 sources. Findings combine the legacy security pipeline AND the multi-layer engine (atlas, wiring, flows, ranked) AND verified AI agent contributions.

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{# ── 2026-05-17 R27 #5: score breakdown panel ────────────────────── Surfaces the score_breakdown JSON that's been silently stored on Repository for months. Turns hidden math into a trust signal. #}

Severity distribution — click a segment to filter

Active filters: excluding tests × Reset all

Bug-class explainers. Each card groups findings of the same shape — these are the patterns most likely to ship to prod and reappear in future scans unless you systematically fix the cause, not just the instance.

Fragile runtime 9 findings

What it is: Code that runs but breaks under predictable input — division by zero, missing keys, unbounded loops, off-by-one slicing.

Why it matters: Reaches production undetected because happy-path tests pass. First user with a weird input crashes the request.

How AI causes it: AI loves writing the happy path; doesn't probe edge cases unless explicitly asked.

Fix approach: Add property-based tests. Wrap external inputs with explicit validators. Use the framework's typed deserializer (Pydantic, attrs).

9 matching findings on this repo

medium [ERR001] Silent Exception Swallowing: Silently swallowing all exceptions hides … numpy/ma/core.py:1097
medium [ERR001] Silent Exception Swallowing: Silently swallowing all exceptions hides … tools/refguide_check.py:309
medium Average file size is 646 lines (recommend <300)
medium [ERR001] Silent Exception Swallowing: Silently swallowing all exceptions hides … numpy/_core/function_base.py:486
medium Network/subprocess call without timeout or try/except — benchmarks/asv_pip_nope…
medium Network/subprocess call without timeout or try/except — numpy/f2py/_backends/_m…
medium Network/subprocess call without timeout or try/except — tools/linter.py:26
medium Network/subprocess call without timeout or try/except — tools/write_release.py:…
medium Network/subprocess call without timeout or try/except — tools/check_python_h_fi…

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Legacy markers 37 findings

What it is: TODO, FIXME, XXX, HACK comments. Often indicate a known-broken path the author meant to fix.

Why it matters: Each marker is an unfinished thought. Production code shouldn't ship with debt that's documented but not tracked.

How AI causes it: AI mirrors the style of the codebase, so existing TODOs propagate into new code.

Fix approach: Convert each into a ticket. Delete the comment when the ticket lands. Use a pre-commit hook to block new TODOs without an issue link.

12 matching findings on this repo

low Legacy-named symbol `time_strided_copy` in benchmarks/benchmarks/bench_io.py:30
low Legacy-named symbol `time_array_no_copy` in benchmarks/benchmarks/bench_array_c…
low Legacy-named symbol `isintent_copy` in numpy/f2py/auxfuncs.py:32
low Legacy-named symbol `isintent_copy` in numpy/f2py/rules.py:96
low Legacy-named symbol `exp_copy` in numpy/f2py/tests/test_inplace.py:31
low Legacy-named symbol `_character_bc_old` in numpy/f2py/tests/test_character.py:5…
low Legacy-named symbol `obj_copy` in numpy/f2py/tests/test_array_from_pyobj.py:262
low Legacy-named symbol `a_copy` in numpy/lib/tests/test_function_base.py:310
low Legacy-named symbol `std_old` in numpy/lib/tests/test_nanfunctions.py:828
low Legacy-named symbol `test_drop_metadata_identity_and_copy` in numpy/lib/tests/t…
low Legacy-named symbol `is_deprecated` in numpy/lib/tests/test__datasource.py:320
low Legacy-named symbol `test_copy` in numpy/polynomial/tests/test_symbol.py:156

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Commented-out code 53 findings

What it is: Lines of source that were intentionally disabled but never deleted.

Why it matters: Git already remembers history — commented code rots, becomes wrong, and adds noise to diffs.

How AI causes it: AI sometimes comments out broken code instead of fixing it. Reviewers approve out of inertia.

Fix approach: Delete. Trust `git log`. If you really need to remember, save it in a notes file under `docs/`.

12 matching findings on this repo

info Commented-code block (5 lines) in benchmarks/benchmarks/bench_ufunc.py:529
info Commented-code block (6 lines) in numpy/_globals.py:100
info Commented-code block (5 lines) in numpy/__init__.py:663
info Commented-code block (5 lines) in numpy/testing/tests/test_utils.py:499
info Commented-code block (6 lines) in numpy/testing/_private/utils.py:81
info Commented-code block (7 lines) in numpy/tests/test_public_api.py:98
info Commented-code block (13 lines) in numpy/f2py/cfuncs.py:398
info Commented-code block (5 lines) in numpy/f2py/crackfortran.py:1439
info Commented-code block (8 lines) in numpy/f2py/symbolic.py:14
info Commented-code block (5 lines) in numpy/f2py/rules.py:955
info Commented-code block (5 lines) in numpy/f2py/capi_maps.py:151
info Commented-code block (5 lines) in numpy/lib/_datasource.py:305

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{# ── 2026-05-17 Round 14: AI-agent bridge footer ────────────────────── Discoverability: the /agents/voting/ guide + MCP manifest exist but aren't linked from anywhere users actually land. Small, opt-in footer. #}

For AI agents: Voting guide (TP/FP) MCP manifest Stdio wrapper SARIF Integrate Findings queue Vote TP/FP on findings to calibrate the engine.

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