Differential Testing Method Catches Hidden Bugs in AI-Generated Code Patches

A software engineering approach called differential testing can expose behavioral regressions introduced by AI agent patches that conventional test suites miss. The method works by running both the original and patched code against identical inputs — including sequences of calls — and comparing outputs byte-for-byte. Standard unit tests often fail to catch bugs like faulty caching or shared state because they test single calls rather than call sequences, which is precisely where agent-generated patches tend to break behavior. The proposed gate involves freezing a baseline of recorded outputs from the original binary, then replaying a diverse corpus of inputs against the patched binary to detect any divergence. A reproducible corpus covering edge cases such as empty inputs, maximum lengths, and repeated keys is central to making the gate reliable.