You can pin down existing expected behavior pretty well with automated test cases and even bringing formal verification into the mix. Using LLMs to create those, it's not even a major cost factor. Then you are safe from such regressions. Just like when humans make changes. So, I don't see how this is introducing a difference.
> The tools to write the code does not matter to the user just like the hammer does not matter to the person that needs a home. But if your tools results in a badly constructed house that always needs repair and what not, expect complaints.
Indeed. Though I don't see the connection to my point.
Test cases are sample based, they do not take care of the whole range of value. Type checking in the other hand are set based and sometimes the types are too coarse. In both instances, you need to carefully choose your axiomatic foundation, and if you can do that, that means you’re already aware of both programs and solution. As defining the problem is a major step in problem solving.
> Indeed. Though I don't see the connection to my point.
A lot of AI proponents are touting the tools as better hammers while purposely not saying anything about the house. You need to show how it can help with building a new house and not gush that it’s a new shape, different from all the other hammers in history.
So the focus on line of codes, number of PRs, all the harness talks,… is worth nothing if better software can’t be built.