How we test AI-generated code
A one-line copy change in a XAIO project recently triggered 43 automated checks before it was accepted. Here is why we think that is the right ratio.
There is a popular assumption that AI-generated code cannot be trusted because nobody reviews it. We think the assumption gets the fix wrong: the answer to unreviewed code is not heroic manual review — it is making verification a built-in property of generation itself.
Tests are generated with the code, not after it
When XAIO builds a feature, it generates the tests alongside the implementation — as one output, from the same understanding of the requirement. That matters, because tests written after the fact tend to test what the code does, not what it should do.
Those tests then run on every change. Not on a schedule, not before big releases — every change. A one-line text edit in a project runs the same suite as a schema migration, because the cost of running checks is near zero and the cost of a silent regression is not.
The layers on top
- Self-healing during generation: build errors are caught and fixed automatically while XAIO works, so broken intermediate states never reach you.
- Per-change checks: the automated suite runs after every applied change, and the result is visible right in the chat — pass counts, failures, and details one click away.
- The release gate: before a publish, tests run against a frozen snapshot of exactly the code that ships, with a fresh test database — plus a security and code-quality review that can block the release.
Why the ratio is the point
Forty-three checks for a copy change sounds excessive until you invert it: verification this cheap means no change is ever too small to verify. That is what turns AI generation from a demo trick into an engineering practice — and it is the reason we publish the check results in the product instead of asking you to trust us.