Yea, I had this very rough (maybe crazy) idea in mind:
Once you can differentiate through CPython, and let's say you can also differentiate integral types via some approximation, and you have some bug in some Python code, and a failing test case in Python, you can use the output (e.g. exception of the failing test) as an error signal and backpropagate to the Python program code. The Python program code is represented as a chunk of bytes. If there is some meaningful gradient, it could point you to possible source code locations where the bug might be.
Probably the gradient will be quite meaningless though, and that's why the idea does not really work in practice. But I think for some simple examples, it still might work.
For any possible branches in the code (and there are a lot), to get a good approximated gradient, you should visit some of the branches, maybe some MC sampling or so.
Once you can differentiate through CPython, and let's say you can also differentiate integral types via some approximation, and you have some bug in some Python code, and a failing test case in Python, you can use the output (e.g. exception of the failing test) as an error signal and backpropagate to the Python program code. The Python program code is represented as a chunk of bytes. If there is some meaningful gradient, it could point you to possible source code locations where the bug might be.
Probably the gradient will be quite meaningless though, and that's why the idea does not really work in practice. But I think for some simple examples, it still might work.
For any possible branches in the code (and there are a lot), to get a good approximated gradient, you should visit some of the branches, maybe some MC sampling or so.