Legacy code, undocumented, inconsistent, built up over years by many different hands, is a genuinely harder test for AI coding assistants than the clean demo repositories most comparisons use.
A model working on legacy code has to infer conventions that were never documented, work around inconsistencies between different eras of the code, and avoid confidently “fixing” things that are actually load-bearing workarounds for reasons lost to institutional memory.
Tools that specifically check output against existing patterns, like Claude Code, tend to perform more reliably here than tools optimized purely for generating idiomatic modern code from scratch, since matching existing (if imperfect) conventions matters more than textbook-clean output in this context.
Before asking an assistant to modify legacy code, ask it to explain what a section actually does and flag anything unusual first. That step often surfaces exactly the kind of load-bearing weirdness a confident but uninformed edit would break.
Legacy code often has weaker test coverage than modern projects, which removes an important safety net. Investing in test coverage for the specific area you’re touching before making AI-assisted changes is worth the extra time here more than almost anywhere else. See GitHub’s own Copilot documentation for more on working with existing codebases.
