genotoxic

Triage survived mutation testing results by applying graph analysis to categorize findings into false positives, missing tests, and fuzzing targets. Graph data provides code context to distinguish true coverage gaps from non-viable mutants efficiently.

Find fuzzing targets from mutation testing results by analyzing survived mutants with code graph data. This pinpoints code paths where mutations survived, highlighting areas lacking robust assertions that are prime candidates for fuzzing.

Yes, mutation testing triage works with smart contract, codebase, and system testing workflows. It leverages graph analysis and optional test statement removal to categorize survived mutants across diverse environments to identify false positives and missing coverage.

Analyze mutation testing results using a code graph by running a graph-based analysis tool against the target directory. The graph provides context-aware analysis to automatically categorize survived findings and suggest next steps for improving test coverage.

You do not need necessist, but it is an optional dependency for mutation testing triage. When integrated, necessist removes unnecessary test statements to provide additional coverage insights beyond standard graph analysis of survived mutants.

Survived mutants are flagged as false positives in code analysis when graph data reveals the mutation is non-viable or semantically equivalent. This context-aware triage prevents wasted effort writing tests for code paths that cannot meaningfully fail.