Code health

Graph and wiki layers have competition now. Code health is where repowise is unique — and unlike most "code quality" scores, this one is calibrated against real defect history and benchmarked head-to-head against the leading commercial tool in the space.

Repowise scores every file from 1 to 10 using 25 deterministic biomarkers computed over tree-sitter ASTs and git history. No LLM calls, no cloud requirement, no new runtime dependencies — pure Python that finishes in under 30 seconds on a 3,000-file repo.

The biomarkers

Each file starts at 10.0; biomarker findings deduct from the score, with a cap per category so no single category can dominate.

The three repo-level KPIs:

• Hotspot Health — NLOC-weighted average over the top-25% hotspot files.
• Average Health — NLOC-weighted average over all files.
• Worst Performer — the single lowest-scoring file.

Calibrated, not hand-tuned

The biomarker weights are learned offline from a real defect corpus. Each file is scored at the commit immediately before a 6-month defect window (T0, so the measurement can't leak future information), and an L2-regularized logistic regression — with file size (NLOC) as an explicit control — fits each biomarker's defect lift beyond size. Only the learned constants ship; the runtime stays fully deterministic.

The strongest calibrated predictors: co_change_scatter, change_entropy, ownership_risk, and nested_complexity.

Does the score predict real bugs?

Yes — validated across 21 open-source repositories spanning all nine Full-tier languages (Python, TypeScript, JavaScript, Java, Kotlin, Go, Rust, C++, C#):

Head-to-head vs the leading commercial tool

On the same 2,770 files across 9 languages, scored at the same leakage-free commit against the same defect labels, with paired significance tests:

Ranking by repowise health surfaces 2.3× the defects under a fixed review budget (Popt Δ +0.144, recall Δ +0.098, density Δ all paired and significant at p = 0.003).

Using it

repowise health                       # KPIs + lowest-scoring files
repowise health --coverage cov.lcov   # ingest LCOV/Cobertura/Clover → untested-hotspot
repowise health --refactoring-targets # ranked by impact / effort
repowise health --trend               # snapshots + declining / predicted-decline alerts

• Coverage ingestion — LCOV, Cobertura, Clover, or normalized JSON light up the test-coverage biomarkers.
• Trend tracking — a rolling 50-row snapshot history powers Declining Health and Predicted Decline alerts.
• Refactoring targets — deterministic, rule-based, ranked by impact / effort.
• Per-file overrides — .repowise/health-rules.json disables biomarkers per glob.

Your agent reaches the same data through the get_health MCP tool, and a single-line summary shows up in repowise status. Full CLI reference: repowise health.

How it connects to the other layers

Code health isn't a silo — it reuses signals from every other layer:

• Git feeds the organizational biomarkers (ownership, churn, co-change scatter, knowledge loss).
• Graph feeds centrality (a brain_method must be central, not just long) and hidden_coupling.
• Decisions surface as ungoverned_hotspot and stale_governance health findings.

That's why a repowise health score means more than a complexity linter's: it knows not just that a file is complex, but that it's complex, central, churned by many hands, and untested.