Refactoring Safely with AI
AI refactoring is powerful and dangerous in equal measure. The speed at which AI can restructure code means you can introduce regressions faster than any human team — and across many files simultaneously. The teams that get real value from AI-assisted refactoring use it within a framework of tests, small increments, and human governance. This page covers that framework.
Safety-First Mindset
The core rule: do not refactor code that has no tests. If tests don't exist, generate characterisation tests first, then refactor. This is not optional — it is the difference between AI refactoring that is safe and AI refactoring that silently breaks production.
Safe refactoring conditions
- Test suite exists and passes before you start
- Scope is limited — one function, one module, or one specific pattern
- You can review the entire diff before merging
- Changes are in a branch and go through a PR
- Security checks run automatically in CI — not manually by a reviewer
Refactoring danger zones
- No tests — AI changes semantics silently
- Unbounded scope — "refactor this entire codebase" creates unreviable diffs
- Database migrations in the same PR as refactoring
- Refactoring authentication or payment code without independent security review
- Applying AI refactoring to code you don't understand — you cannot catch its mistakes
Step 1: Characterisation Tests for Legacy Code
If the code you want to refactor has no tests, use AI to generate characterisation tests — tests that document the current behaviour, whatever it is, so you can detect any change.
Characterisation test prompt:
Generate characterisation tests for the following function. These tests should:
- Document the current behaviour exactly, not the ideal behaviour
- Cover the normal case, edge cases (empty/null/zero inputs), and boundary values
- Use [YOUR TEST FRAMEWORK — pytest / Jest / etc.]
- Include a comment on each test noting what exact behaviour it is locking in
Do not judge whether the behaviour is correct — just test what the function currently does.
[PASTE FUNCTION]
Run these tests, confirm they pass, then commit them before starting any refactoring. They are your safety net.
Step 2: Scoped Refactoring Prompts
The most common AI refactoring failure is scope creep — AI rewrites things you didn't ask about. Counter this with explicit scope constraints in every prompt.
Extract function refactoring:
Refactor the following code to extract [DESCRIBE THE LOGIC — e.g., "the email validation logic"] into a separate function named [NAME].
Rules:
- Extract only the logic described. Do not refactor anything else.
- Keep the function signature of the original function unchanged
- Keep all existing variable names in the original function unchanged
- The extracted function should have a clear single responsibility
[PASTE CODE]
Modernisation refactoring:
Modernise this code from [OLD PATTERN — e.g., callbacks / Promise chains] to [NEW PATTERN — e.g., async/await].
Rules:
- Change only the async/control-flow pattern. Do not change logic, variable names, or error handling behaviour.
- All error cases that were handled before must still be handled after
- Function signatures and return types must remain identical
[PASTE CODE]
Step 3: Reviewing AI Refactoring Output
AI refactoring diffs require a different review mindset than feature diffs. You are not asking "does this do what I want?" — you are asking "did anything change that shouldn't have?"
Refactoring diff review checklist:
- Do all existing tests still pass after the change? (Run the test suite, not just the characterisation tests)
- Are there any semantic differences — not just syntactic ones? (Look for changed conditionals, reordered operations, dropped error paths)
- Did the AI change the function's return value in any path?
- Did the AI rename anything that is referenced elsewhere in the codebase?
- Did the scope stay within what was requested, or did AI refactor adjacent code?
- For security-sensitive code: do security checks still run in the same order?
Independent security checks
Do not use AI review as the security check on AI-generated refactoring. Run your static analysis tools (SAST) independently. AI can be persuasive about the correctness of code it generated. Security gates must be deterministic and non-negotiable.
Incremental Strategy for Large Refactors
| Phase | What to do | Gate before next phase |
|---|---|---|
| 1. Understand | Ask AI to explain the current code — what it does, why it is structured this way | You understand it well enough to spot AI mistakes |
| 2. Test | Generate characterisation tests; run them; fix until passing | All characterisation tests pass in CI |
| 3. Plan | Ask AI to produce an ordered refactoring plan — small steps, each independently mergeable | Plan reviewed by a human engineer |
| 4. Execute | Execute one step at a time; separate PR per step; run tests after each | All tests pass; human reviews diff; SAST passes |
| 5. Monitor | After deployment, monitor error rates and performance metrics | No regression in error rate, latency, or key metrics |
Teams using this incremental pattern see 70% fewer post-deployment issues from AI-assisted refactoring compared to batch refactoring approaches.
Checklist: Do You Understand This?
- What is a characterisation test, and why must it be generated before refactoring legacy code?
- What phrase should you include in every refactoring prompt to prevent AI scope creep?
- When reviewing an AI-generated refactoring diff, what specific question are you asking that is different from feature code review?
- Why must security checks on AI-refactored code be run by deterministic tools rather than AI review?
- What is the incremental refactoring strategy — what happens at each phase and what is the gate before the next phase?
- Name two categories of code where you should always get an independent human security review, even after successful AI refactoring.