Machine learning requires a problem that you can have partially correct, so that it can climb the gradient to optimize on. If you can build tests that have an analog instead of pass/fail output, you could, in theory, do it with machine learning.

Beware that machine learning in a single pass/fail is more like having an infinite number of monkeys trying to write the works of Isaac Asimov.

[Edit/Update] All of the tests could be individual values, so non-zero (but nowhere near all ones) might help. Thanks for making me reconsider this, sdenton4.