Use case
How do you approach this?How to Test an AI-Suggested Refactor
Published
Quick answer
Test an AI-suggested refactor by defining what must remain observably true before judging whether the new structure looks cleaner. Capture ordinary behavior, boundary behavior, error handling, and relevant performance or ownership constraints, then run the same evidence against the changed version. A refactor can improve naming while accidentally changing evaluation order, mutation, retries, or error semantics.
Write invariants before moving code
Describe the results, side effects, error modes, and interfaces that callers rely on. Existing tests are useful evidence, but read them for gaps as well as coverage: a refactor can pass a narrow suite while breaking an untested boundary or assumption shared by multiple callers.
Compare behavior rather than surface form
Run representative inputs through the old and new paths when that is safe, and inspect changes in ordering, mutability, exceptions, logging, and resource use. This is especially important when a suggested abstraction combines branches that only looked similar at a glance.
Practice loop
- 01List the observable outputs, side effects, and error cases that must survive the refactor.
- 02Add a boundary test before changing structure if the current suite does not make that behavior clear.
- 03Compare old and new behavior on one ordinary input and one edge input before calling the change safe.
Limits of this page
No finite test set proves a refactor is harmless, particularly across asynchronous, distributed, or performance-sensitive systems. A short exercise can guide a next practice choice, but it cannot establish the source of a surprising result or replace local review and deployment safeguards.
Source context
Editorially reviewed for Unrust using public language documentation, common code-review practice, and small original examples written for this resource system.
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