An AI coding assistant can produce a plausible patch and a confident explanation in the same response. What evidence should accompany that explanation before a team treats the issue as resolved?
A useful baseline might include:
- A reproduction that fails before the change and passes afterward
- The exact tests or commands run, with their outcomes
- An explanation connecting the change to the reported failure
- A clear statement of anything that could not be checked
The difficult part is deciding how much evidence is enough. A typo in a static label and a change to payment processing deserve different levels of verification. Passing tests also leave an important question: do those tests cover the behavior the user actually reported?
For a timing bug, a happy-path test seems especially weak. A stronger check would deliberately reverse the order of events, delay a response, or force a failure at the point where the bug appears.
Would you require a regression test for every AI-generated bug fix, or allow a documented manual check for smaller changes? And should assistants distinguish between “patch prepared,” “automated checks passed,” and “verified in the target environment” instead of compressing all three into “fixed”?
Curious where you would set the minimum bar, especially when the assistant cannot access the production environment.
