The code correctness issue is certainly a big problem -- it is simply not enough to get 90% correct. The real world problems often lie in the remaining 10% edge cases.
I have a very different take on how AI can come up with a correct by construction code, with not necessarily using a probabilistic model (Deep learning, for example). I have it written as a blogpost here[0]. The sketch of the idea is that any problem is a data problem, and an algorithm could be discovered, and new code could be generated by projecting it into topological space, finding the code there, and reducing the dimensions back in program space. It could well be a decent application of abstract algebra/algebraic topology to AI and code generation problems.
I have a very different take on how AI can come up with a correct by construction code, with not necessarily using a probabilistic model (Deep learning, for example). I have it written as a blogpost here[0]. The sketch of the idea is that any problem is a data problem, and an algorithm could be discovered, and new code could be generated by projecting it into topological space, finding the code there, and reducing the dimensions back in program space. It could well be a decent application of abstract algebra/algebraic topology to AI and code generation problems.
[0] https://densebit.com/posts/24