AI refactoring subtly changes output: how do boundary samples reveal regressions?

Turn subtle differences into explicit behavior requirements

This applies to altered ordering, null handling or calculations that diverge only on some inputs. Preserve one concrete difference and determine whether old behavior is a public contract or a defect. Even an apparent improvement can break callers when only refactoring was authorized. Required contract changes must be explicit rather than silently labeled optimization.

Separate permitted changes from required compatibility

Inspect input types, defaults and observable side effects, including floating-point comparisons, stable sorting and error types. The old implementation can be a comparator but is not automatically correct. Resolve disputed behavior with requirements or explainable samples so an erroneous baseline does not force preservation of an actual defect.

Compare independent expectations and boundary samples

Keep representative normal and boundary samples in parameterized tests of externally observable behavior. Where appropriate, check properties such as normalization being unchanged by a second application rather than implementation details. Numeric tolerance must follow business precision requirements, not be loosened arbitrarily to suppress failures. Test through public interfaces.

Check exception types, ordering and numeric boundaries

Cover relevant empty collections, duplicates and extremes and inspect exception types or error statuses. First confirm the sample catches the observed difference, then verify the repair. If a change is explicitly accepted, update documentation and caller checks. Re-recording snapshots from the new output alone does not establish that the change meets requirements.

This article ran no private refactoring code and does not claim property testing exhausts every case. Generated inputs need suitable strategies and reproducible failures rather than meaningless volume. Complex systems also need integration checks. If permitted changes and compatibility boundaries remain unclear, preserve evidence instead of automatically accepting uncertain differences.