genotoxic

Triage mutation testing survivors by running necessist and code graph analysis to classify findings into false positives, missing unit tests, and fuzzing targets. This approach categorizes survivors based on reachability, blast radius, and complexity to generate a structured markdown report.

Avoid wasting time on equivalent mutants by using code graph analysis to filter harmless survivors. Graph-informed triage evaluates reachability and privilege boundaries to isolate false positives, directing focus toward complex or externally reachable functions that require stronger tests.

Identify fuzzing targets from weak test findings by executing necessist to detect statements that pass after removal. Correlating these signals with mutation testing survivors highlights complex, under-tested behaviors needing fuzzing coverage.

You need code graph analysis to map necessist findings to production functions effectively. Trailmark graph pre-analysis provides the reachability, blast radius, and privilege-boundary context required to assign weak-test findings to actionable engineering tasks.

Mutation testing and necessist correlation work specifically on codebases with an existing green test suite. This prerequisite ensures survived-mutant filtering and passing-after-removal normalization can accurately identify missing test coverage for exposed behaviors.

Without mutation testing results, necessist findings are not corroborated by both tools, reducing triage accuracy. The workflow relies on mutation and necessist correlation to distinguish missing unit tests from fuzzing targets and validate weak-test signals.