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Marius CiepluchandClaude Opus 4.8
444d8c9490
Externalize detection rules to versioned TOML and remove rule-local severity, so rules detect and the central CWE policy decides severity — fixing "rules too influential / not centrally managed". Zero-dependency; detection is byte-identical (benchmark recall 44/47 = 93.6%, FP 0% unchanged) and the 90/10 gate holds. - RulesetStore (ruleset/store.py + ruleset/<lang>/rules.toml): PatternRule as data (rule_id, title, languages, cwe, tags, pattern, status, min_evidence_level, precision_estimate, version) with NO severity; load_ruleset (sorted, with optional rule_policy.json ledger overlay) and a stdlib TOML write_ruleset. The 32 rules were generated from the prior PATTERN_RULES with a verified exact round-trip (pattern/cwe/languages/tags/title). - findings.py: drop the hardcoded PatternRule class + PATTERN_RULES tuple; load the default ruleset from TOML; quick_scan_findings(root, targets, rankings, ruleset, policy) resolves each finding's severity from policy via resolve_severity (SEVERITY_LABEL maps 0-5 -> string) and tags findings with their rule status. - Three-status volume control: StaticFinding gains status (default "enabled"); disabled rules never fire; shadow findings are partitioned out of the report and score in the CLI (written to shadow_findings.json) — no shadow rules by default. - Emission floors (RulesetConfig.min_emit_priority / min_emit_precision, default 0.0 = emit all) replace "every match is a finding". - CLI loads the ruleset as a stage and uses it for the fail-loud policy check, detection, hunter pool, and the score CWE map. Wheel ships the ruleset TOML. 85 tests pass; ruff + mypy clean. Spec tasks 3.1-3.4 checked off. Co-Authored-By: Claude Opus 4.8 <[email protected]>