You're right, at 300 tests bayesect converges to ~97-100% across the board. I reran with calibration.py and confirmed.

Went a step further and tested graph-weighted priors (per-commit weight proportional to transitive dependents, Pareto-distributed). The prior helps in the budget-constrained regime:

128 commits, 500 trials:

Budget=50, 70/30: uniform 22% → graph 33% Budget=50, 80/20: uniform 71% → graph 77% Budget=100, 70/30: uniform 56% → graph 65% At 300 tests the gap disappears since there's enough data to converge anyway. The prior is worth a few bits, which matters when bits are scarce.

Script: https://gist.github.com/rs545837/b3266ecf22e12726f0d55c56466...