An interesting exercise. Why do you think an increasing probability distribution makes this equivalent to the halting problem, though? Without loss of generali

An interesting exercise. Why do you think an increasing probability distribution makes this equivalent to the halting problem, though? Without loss of generality, an increasing probability is >= some P after some time T. Being conservative, we can assume the failure probability is zero before T and ==P after T. Of course, we don't actually know P or T, but they must be consistent with us having seen the bug at all. So we can use uninformative priors to define the posterior distribution of these values given the number of times we have seen the bug and the amount of time required in each case. Unless I'm missing something? I once wrote a probabilistic version of git bisect (https://github.com/Ealdwulf/bbchop https://github.com/Ealdwulf/bbchop) for use with intermittent bugs, but it hasn't seen real use.