Wisening Assertions: A live Bayesian reasoning system for probabilistic co...
summary
Claims a practical system that turns expensive runtime assertions into a scalable, probabilistic monitor by computing a blame as probabilistic event per program line via information‑flow/path‑condition analysis then applying Bayesian updates to line correctness priors. Uses observed branch probabilities (e.g. from a branch predictor) to compute omegas, applies the frequency formula (frequency = (target − prior)/(1 − prior)) so that after, say, 43 successful checks a 99% confidence arises, and yields eventually zero overhead while supporting live resets on code change and use branch probabilities for per-assertion pacing.
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