Refactoring AI-Generated Code: An Approach That Breaks Nothing

Refactoring means improving the internal structure of code without changing its external behavior. With AI-generated code, one extra difficulty comes in: you are restructuring something nobody on the team wrote. That is why the process does not start with the restructuring itself, but with a net of tests that captures current behavior.

What is refactoring, and what is it not?

The definition is narrow, and the narrowness is the point: change the structure, not the behavior. The moment behavior changes, it is no longer refactoring, it is a bug fix or a feature.

This distinction is not pedantry. It is the reason refactoring can be safe: if behavior is supposed to stay unchanged, there is a clear yardstick, the tests that were green before must be green afterward. Mix the two together and you lose that yardstick.

Why is there so little refactoring left in AI-assisted development?

Because refactoring requires understanding the existing codebase, and generated code does not produce that understanding along the way. The numbers are clear: according to GitClear's analysis, the share of moved code, the best available proxy for genuine restructuring, dropped from 21 percent in 2022, through 13 percent (2023), to 3.8 percent in the current year, 2026.

Together the two describe a shift: code gets added next to what exists instead of being restructured. For a model that is the obvious path: writing a new function alongside an existing one carries no risk, restructuring an existing one requires knowing who else relies on it.

How do I refactor code I did not write?

In five steps, and their order is not negotiable.

• 1. Lock in the behavior. Before you change anything: tests that capture the current behavior, including what looks wrong to you. These tests are not a quality judgment, they are a safety net. If one fails later, you know you changed something.
• 2. Map the dependencies. Who calls this code? Who relies on its behavior? Generated code often has more callers than expected, because the same pattern was written multiple times.
• 3. Restructure in small steps. One step, one test run, one commit. A refactoring commit spanning 40 files cannot be reviewed and, in case of doubt, cannot be rolled back.
• 4. Merge duplicates last. Only once the individual spots are clean can you see whether they really do the same thing. Merging two spots that differ in one detail creates exactly the bug you were trying to avoid.
• 5. Ship intermediate states. A refactoring that sits on a branch for three weeks collides with everything that happens in the meantime.

Can I let an AI do the refactoring?

For mechanical restructuring yes, for structural restructuring only under supervision, and in both cases only with the safety net from step 1.

• Well suited: consistent renaming, extracting a function, splitting an overly long file, filling in missing type annotations, unifying error handling.
• Only with review: merging duplicates. The model has to judge whether two spots really do the same thing, a judgment that requires knowledge of the business logic.
• Do not delegate: how modules are cut, the data model, interfaces between components. Those are architectural decisions, not restructuring.

Without test coverage the benefit reverses: a tool that restructures a lot quickly spreads a bug across the entire project at the same speed. That is why step 1 is not a formality, see test coverage for AI code.

How do I get time approved for refactoring?

By not asking for it directly. Requesting refactoring as its own project reliably fails, because it delivers no visible benefit that would show up in a meeting.

What works is tying it to work that is happening anyway: whoever builds a feature in an area cleans up that area along the way. The effort sits inside the feature's estimate, the review happens anyway, and the cleanup happens exactly where work is actually being done, which is exactly where it pays off.

Where that is not enough, the data from your own repository helps: which five files were changed most often in the last quarter, and how complex are they? That list is a cost argument, not a matter of taste. More on this under technical debt from AI code.

Frequently asked questions about refactoring

What is the difference between refactoring and a rewrite?

Refactoring changes the structure step by step while the system stays fully operational throughout. A rewrite replaces a part entirely. Refactoring carries less risk and takes longer; a rewrite pays off once nobody understands anymore what is supposed to be restructured.

Do I need tests for every refactoring?

For every one you cannot undo without noticing. In practice that means: yes. The exception is purely mechanical, tool-assisted renaming within a single file.

How large can a refactoring commit be?

Large enough that a reviewer can read it in one sitting. That is a more solid limit than any line count and in practice leads to small commits.

What do I do if I spot a bug during a refactoring?

Note it down, finish the refactoring, fix the bug in its own commit. Mixing the two costs you the yardstick "tests were green before and are green afterward."

The most dangerous moment during restructuring is the one where behavior changes along the way and nobody notices, because no test asks about it. Code Guardian traces the dependencies before every change and afterward checks whether the diff only touched what it was supposed to touch.

Sources

• Martin Fowler, Refactoring (definition and catalog of restructuring steps).
• GitClear, The Maintainability Gap: 2026 AI Code Quality Research (share of moved code, 2022 to 2026).

As of July 27, 2026. General professional context.