AI code refactoring
What is AI code refactoring?
Using AI to improve the internal structure of code, simplifying logic, cutting duplication, splitting overgrown functions, without changing what it does. Refactoring has always meant behaviour-preserving change; AI speeds it and spots patterns across a large codebase. The catch is proving the behaviour really held, which is why strong tests matter more here, not less.
Why it matters
Scale is what makes AI refactoring both attractive and hazardous. A person restructures one module at a time and notices when something feels off. A model can rename, split and reshape across thousands of files in a single pass, which is the whole appeal and also the problem: a subtle behaviour change that a human would catch in one file now lands in hundreds at once. The safety of the operation rests almost entirely on what surrounds it, the test suite that proves behaviour actually held and the discipline to roll the change out in reviewable stages rather than one heroic commit. Done with that scaffolding it clears years of accumulated cruft in an afternoon. Done without it, it launders a bug into every corner of the codebase before anyone notices.
In practice
A deprecated helper needs to go from a large codebase. The model rewrites every call site in one pass. Rather than merge that as a single change, the team gates it behind a full test run and ships it in slices, one package at a time, each reviewed. The refactor that could have altered behaviour everywhere becomes a series of small, checkable steps.
