golang.org/x/tools v0.50.0 releasedBlog: Finding unreachable functions with deadcode by Alan Donovan Russ Cox leaves google, and opines on LLMs for debugging Coding agent guidelines for modern Go J...

How the Go team’s deadcode tool caught what the compiler never will

Every senior developer has stared at a file and thought: _should I delete this?_ You run `vulture`. You run `deadcode`. They both tell you the file has zero import sites. You hover over the delete key. And then you don't delete it. Because you don't actually know **why** it's there. ## The real question isn't "is it dead?" — it's "why is it dead?" Dead code falls into very different categories: **Category A** — Accidentally orphaned. A file that got left behind when the calling code was removed. Safe to delete immediately. **Category B** — Intentionally parked. "keeping this around until Q2 rollout completes." The holdback condition may or may not still apply. **Category C** — Dynamically loaded. Appears dead to static analysis but gets `importlib.import_module()`'d at runtime. Deleting it breaks production. **Category D** — Replaced but not removed. The function was superseded by a better implementation, the PR was merged, and nobody cleaned up. Safe to delete, but you need to know _what replaced it_ to be confident. Existing tools treat all four identically: "unused." That's the gap fossil fills. ## How fossil works pip install fossil-code fossil explain src/billing/legacy_processor.py fossil runs five stages in under 3 seconds: ### Stage 1: Static Analysis Python's `ast` module builds a symbol table of everything the file exports. Then it scans every other file in the repo for references — imports, calls, attribute access, dynamic patterns (`importlib`, `getattr`, `__import__`). This is not grep; it's an actual AST traversal that understands Python's import semantics. ### Stage 2: Git History Mining `GitPython` traverses `git log --follow` for the target file. It walks commits newest-to-oldest, checking at each step whether any other file in the repo was still referencing the target. The first commit where all references drop to zero is the **death commit**. ### Stage 3: Commit and PR Parsing The death commit message is parsed for PR references (`#441`, `PR 441`, `pull request 441`). If found, the PR title and merge context are extracted from the commit body or (if a GitHub token is configured) via the GitHub API. ### Stage 4: Pattern Detection The file's current content is scanned for deferred-deletion patterns: * `TODO: remove after X` * `keep for now` / `keep around until X` * `DEPRECATED` / `@deprecated` * `will be removed in version X` * `temporary` / `temp fix` For each pattern, fossil attempts to verify the condition: does a PR with that description exist and is it merged? Has a git tag for that version been created? Has the referenced date passed? ### Stage 5: Confidence Scoring 14 weighted signals are aggregated into a 0–100% score: Signal | Weight ---|--- Zero call sites | +30 No dynamic references | +20 Death commit identified | +15 Temporary hold resolved | +10 No reflection patterns | +10 File age > 90 days dead | +8 PR/migration context found | +7 Dynamic import detected | −30 Reflection detected | −20 Modified < 30 days ago | −20 Unresolved "keep for now" | −15 Language unknown (fallback) | −15 Test file references | −10 Ambiguous death commit | −10 The output is a Rich panel with every signal explained, a risk label, and a suggested action. ## Scan an entire codebase fossil scan ./src --threshold 80 Returns a ranked table of all dead files above 80% confidence, with their dead-since date and risk level. Exit code 4 means nothing above the threshold — useful for CI gates. ## Current state and roadmap **Live now (v0.2.0):** * `fossil explain` — full forensic report * `fossil scan` — directory scan with confidence threshold * `fossil clean` — prioritized deletion backlog * Python deep analysis (AST), text fallback for JS/TS/Java/Go * Pattern detection with condition verification * Local SQLite caching (cache hits < 100ms) **Coming next:** * GitHub/GitLab API integration (PR title/body fetch, `--yolo` deletion PR creation) * LLM narration for human-readable explanations * tree-sitter for deep multi-language analysis ## Install pip install fossil-code # Python 3.11+, requires git GitHub: https://github.com/iamvvekverma/fossil The confidence scoring weights are a first attempt — I'd genuinely like feedback on whether the calibration holds for your codebase. What signal should weigh more? What am I missing?

I’m on @deadcode@hachyderm.io (hosted by @jardo@townwizard.net) this week, talking about the good parts of programming that we forgot about. Check it out! https://overcast.fm/+ABHY1U7u5k0 I haven’t listened to it yet. I hope their editor made me sound smart and not the kind of person who will monologue for 20 minutes straight. (I am absolutely the person who monologued for 20 minutes straight.)