How to Refactor Legacy Code with AI

Legacy code is often difficult to change because behavior is only partly documented, tests are sparse, and important assumptions live in production history. AI can summarize files, locate repeated patterns, suggest tests and propose code transformations. It can also miss hidden callers, error behavior, data formats or side effects. A concise explanation from a model is a hypothesis to investigate, not a specification. Refactoring has a specific goal: improve internal structure without changing observable behavior. Martin Fowler describes it as a series of small behavior-preserving transformations. Before editing, identify externally visible behavior through existing tests, logs, fixtures or carefully designed characterization tests. Include edge cases such as empty inputs, malformed data, time zones, ordering and failure handling. If behavior needs to change, treat that as a separate feature or bug fix. Ask AI for one bounded change at a time and tell it what must remain unchanged. Review the complete diff, including generated tests; a test that simply encodes the model’s new behavior does not prove equivalence. Run focused tests after each transformation, then broader suites and static checks. For fragile or poorly understood code, consider adding seams or test doubles so external systems do not make tests nondeterministic. Keep changes small enough to revert or diagnose. Review compatibility details: public APIs, database schemas, serialized formats, logging, performance and security boundaries. Compare before-and-after behavior on representative fixtures. Do not merge a broad rewrite because it is shorter or more modern. A successful refactor leaves the software’s behavior stable while making the next change safer. Human maintainers remain responsible for deciding which old behavior is intentional and which tests actually protect it.