>> It would be nice to have an AI that could [write unit tests, or] look over your code and understand and explain where you might have bugs.
What you're describing (outside of the square braces) is algorithmic debugging:
https://en.wikipedia.org/wiki/Algorithmic_program_debugging
It was introduced in the PhD thesis of Ehud Shapiro. There's been a steady trickle of research work since then but it's never formed into a strong current, if I may. One reason for that is of course that Shapiro's thesis was published in 1983. So it's one of the research directions that was cut short by the last AI winter. Lessons to be learned.
Shapiro's thesis is one of two doctoral theses that became the precursors to Inductive Logic Programming, a field at the intersection of logic programming and machine learning. ILP algorithms learn programs from examples and "background knowledge" (i.e. a library of existing programs used as building blocks for new, learned programs).
The way that algorithmic debugging works is that it finds differences between the intended "model" (in logical terms: the consequences) of a program and its actual model. An algorithm that can do that can also walk back up the AST of a program the other way and produce a correct program from examples of its intended inputs and outputs.
That's the kind of stuff I study. Hence my comment above about lack of innovation etc. It's possible to automatically create novel programs with complex structures (recursion and invented sub-programs) and even discover new algorithms in the process and so on -and we know ways to do that right now. But the way to do it is not with a language model trained to predict the next character in a sequence.
As to writing unit tests, the way that most ILP algorithms work is that you give them a set of examples of the inputs and outputs of the program you want to write (e.g. "droplast([alice,and,bob,sitting,on,the,tree], [alice,and,bob,sitting,on,the])") and they write the program for you. I like to think of it as a kind of automatic TDD.