robustness-audit

A robustness audit surfaces latent runtime failure modes like crashes, silent error swallowing, and resource leaks in code that passes unit tests. It dispatches parallel subagents across code surfaces and confidence-filters findings to exclude nits.

FMEA-style code review uses a fixed 6-category defect taxonomy to find edge case mishandling, concurrency hazards, and security boundary holes. You need it for pre-deployment hardening passes and assessing unfamiliar or long-unmaintained codebases.

You audit an unfamiliar codebase by running a robustness audit that splits code into 3-5 natural surfaces and reviews them in parallel. It actively verifies high-severity claims against actual source code to minimize false positives.

Code hardening via a robustness audit specifically targets concurrency hazards and resource leaks alongside crashes and security boundary holes. It applies a defect-class guided taxonomy to prime distinct mental models for finding these specific bugs.

To minimize false positives in automated code review, a robustness audit actively checks high-severity findings against actual source code and filters out agent hallucinations. It provides a dedicated verified wrong section to build trust in the reported results.

You can investigate production crashes without live reproduction by running a robustness audit on the source code. It identifies error path misreporting and edge case mishandling that only appear under real-world conditions when direct testing is unavailable.