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Supervertaler/Supervertaler-Workbench

https://github.com/Supervertaler/Supervertaler-Workbench · scanned 2026-06-16 00:26 UTC (2 months, 2 weeks ago)

137 raw signals (0 security + 137 graph)

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Complete repo analysis

Last scanned 2 months, 2 weeks ago · v1 · 119 actionable findings from 1 signal source. 18 repeated signals grouped for readability. Security checks, system graph analysis, and verified AI-agent feedback are merged into one review queue.

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88.9% cov · 119 findings

Severity distribution — click a segment to filter

Active filters: excluding tests × Reset all

Severity: Critical 0 High 1 Medium 17 Low 59 9-Layer: Software Security Quality Integrity Frontend Hardware Data Network Cicd Source: Security checks 0 System graph 119 Crowd 0 Layer: Software 21 Frontend 2 Hardware 3 Security 2 Cicd 1 Quality 90

Exclude dismissed / FP Exclude test files

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.

Duplicates & near-duplicates 2 findings

What it is: Same function copy-pasted into multiple modules with minor variations.

Why it matters: Each copy drifts independently — bug fixes apply to one, miss the others.

How AI causes it: AI completes the same pattern in each file rather than refactoring to a shared helper.

Fix approach: Extract the duplicated logic into the most general module both call sites already import. Add tests at the helper level.

2 matching findings on this repo

low Near-duplicate function bodies in 2 places repo-level
low Near-duplicate function bodies in 3 places

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Legacy markers 6 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.

6 matching findings on this repo

low Very large file: legacy_versions/Supervertaler_v2.5.0-CLASSIC.py (5646 lines)
low Very large file: legacy_versions/Supervertaler_tkinter.py (20659 lines)
info Commented-code block (12 lines) in legacy_versions/Supervertaler_v2.5.0-CLASSIC…
medium Network/subprocess call without timeout or try/except — legacy_versions/Superve…
low Old/deprecated-named symbol `_show_custom_prompts_old` in legacy_versions/Super…
medium Network/subprocess call without timeout or try/except — legacy_versions/Superve…

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Commented-out code 40 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 (12 lines) in legacy_versions/Supervertaler_v2.5.0-CLASSIC…
info Commented-code block (8 lines) in modules/snippet_library.py:209
info Commented-code block (5 lines) in modules/llm_clients.py:1039
info Commented-code block (5 lines) in modules/unified_prompt_manager_qt.py:1262
info Commented-code block (6 lines) in modules/chat_backend.py:131
info Commented-code block (10 lines) in modules/voice_commands.py:162
info Commented-code block (9 lines) in modules/voice_tab.py:132
info Commented-code block (5 lines) in modules/unified_prompt_library.py:406
info Commented-code block (5 lines) in modules/i18n.py:85
info Commented-code block (6 lines) in modules/voice_release_poller.py:180
info Commented-code block (7 lines) in modules/help_system.py:100
info Commented-code block (10 lines) in modules/termlens_widget.py:48

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Config drift 2 findings

What it is: Settings duplicated across env files, Docker compose, K8s, and code defaults, all with slightly different values.

Why it matters: Production behaviour depends on whichever copy your loader reads first. Subtle bugs in staging that don't reproduce in dev.

How AI causes it: AI writes new config from memory rather than reading the existing source.

Fix approach: Pick one source of truth (env vars + a settings module). Have every other place import from there. Lint for duplicates in CI.

2 matching findings on this repo

medium Runtime service client appears to use placeholder configuration
high Agent instruction/config may expose a secret: .claude/settings.local.json .claude/settings.local.json:100

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