I built FixProof to verify whether AI-generated bug fixes are actually proven

AI coding tools can generate a fix and tell us the bug is solved.

Did the regression test actually prove the fix?

That question led me to build FixProof, an MIT-licensed open-source developer tool for checking the evidence behind AI-generated bug fixes.

Fail before → pass after → inspect test-integrity warnings → verdict

It runs the selected regression test against the committed code before the fix and again against the committed code after the fix.

A passing test after the change is not enough by itself.

FixProof also looks for warning signs such as:

The goal is to make it harder for a change to appear verified when the regression test never properly reproduced the original problem.

It also includes GitHub Actions integration and a browser-based report viewer.

node bin/fixproof.mjs verify \ --repo../my-project \ --base HEAD~1 \ --head HEAD \ --test tests/regression.test.mjs \ --output fixproof-report.json Enter fullscreen mode Exit fullscreen mode FixProof creates a report describing what happened before and after the change and any integrity warnings it found.

The report can then be opened in the browser viewer.

FixProof does not claim that a verified result means the entire application is bug-free.

Does the selected regression evidence reliably demonstrate this particular fix within the tested scope and environment?

I think keeping that boundary explicit is important, especially when AI coding agents are involved.

GitHub: https://github.com/burhanarif472-lgtm/fixproof

Live demo: https://burhanarif472-lgtm.github.io/fixproof/

The project is open source under the MIT License.

I’m now looking for developers who use AI-assisted coding tools to try FixProof on real bug fixes.

Finding the limitations is more valuable to me right now than simply collecting stars.

If you try it, I’d appreciate hearing what works, what breaks, and what you would change.