sem

Really fun work, and the writeup on the math is great. The Beta-Bernoulli conjugacy trick making the marginal likelihood closed-form is elegant. We ran benchmarks comparing bisect vs bayesect across flakiness levels. At 90/10, bisect drops to ~44% accuracy while bayesect holds at ~96%. At 70/30 it's 9% vs 67%. The entropy-minimization selection is key here since naive median splitting converges much slower. One thing we found, you can squeeze out another 10-15% accuracy by weighting the prior with code structure. Commits that change highly-connected functions (many transitive dependents in the call graph) are more likely culprits than commits touching isolated code. That prior is free, zero test runs needed. Information-theoretically, the structural prior gives you I_prior bits before running any test, reducing the total tests needed from

usually the whole discussion has been around line-level vs commit-level history, but there's a layer nobody's talking about, and I have been exploring it here these days with https://github.com/Ataraxy-Labs/sem. It gives you entity-level version control. It parses your code into functions, classes, methods using tree-sitter (12 languages so far), computes a structural hash for each entity, and builds a cross-file dependency graph. So sem diff HEAD~1 doesn't give you "+3 -2 in tax.py", it gives you "calculate_tax signature changed, 47 dependents, 3 callers will break". The key insight is distinguishing signature changes from body changes.