The question that started it
A weak point in AI-assisted coding is not always broken syntax or a failing test suite. The original concern behind this project was about tests too: if the AI-assisted workflow helps with testing, is that set of tests enough?
If the testing pattern is biased, a bug may sit where the tests do not look. The early idea was adversarial and game-theoretic, even equilibrium-like: treat bugs or failure conditions as if they were trying to stay hidden, then ask what testing or investigation policy would still hold up.
That is not what v0.1.0 proves. The current repository is not a formal game-theoretic debugger, and it does not claim real-world debugging accuracy.
That is the frame of bug-cause-inference-game, a small Python prototype for cost-aware bug-cause investigation. The version discussed here is tag v0.1.0, which points to commit 9e30c93f246602d840c875e975c362e6ab1e7747.
