Predictive test selection: A more efficient way to ensure reliability of code changes

Introduced a predictive test selection system that uses machine learning (gradient-boosted decision trees) to probabilistically select regression tests for code changes. This system learns from historical code changes and test outcomes to estimate the likelihood of a test detecting a regression, allowing it to run a smaller subset of tests (one-third of transitively dependent tests) while catching over 99.9% of regressions. It also addresses test flakiness by aggressively retrying failed tests during training data collection.