I believe we can use these types of tools to make software more understandable, and mngr is an example of how to do that.
In our case study, we're using AI to increase our test coverage, and if you look at it, I would argue that we are making it more understandable--now instead of just having 100's of tests, we simply have a document that describes how the software is supposed to work, and the tests are linked to that document, and checked to ensure that they conform.
That means that anyone--not just the author of the software--is now able to read through the high level tutorial description of how the commands work in order to understand what the program should do!
And as for the tests themselves, we've been able to make nice testing infrastructure--like the transcripts and recordings that were highlighted in the post--to make it even easier for us to verify the behavior of the software.
We also have an incredibly detailed style guide and set of tests and guidelines to ensure that the entire code base is consistent, and high quality. You can drop into any of the code and pretty quickly understand what is happening. And if not, claude will do an excellent job of describing how any given component works, and how it relates to the others.
Finally, mngr itself is designed to be fully transparent when it is running--you can literally attach to the coding agent you are running and see exactly what is happening, and the program makes extensive log outputs for everything it does (feel free to open a PR if you'd like to see more!)
It's not perfect formal verification, but it does feel like we're making meaningful progress on making it easier to understand software--not harder.