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Regression testing---rerunning tests on each code version to detect newly-broken functionality---is important and widely practiced. But, regression testing is costly due to the large number of tests and the high frequency of code changes. Regression test selection (RTS) optimizes regression testing by only rerunning a subset of tests that can be affected by changes. Researchers showed that RTS based on program analysis can save substantial testing time for (medium-sized) open-source projects. Practitioners also showed that RTS based on machine learning (ML) works well on very large code repositories, e.g., in Facebook's monorepository. We combine analysis-based RTS and ML-based RTS by using the latter to choose a subset of tests selected by the former. We first train several novel ML models to learn the impact of code changes on test outcomes using a training dataset that we obtain via mutation analysis. Then, we evaluate the benefits of combining ML models with analysis-based RTS on 10 projects, compared with using each technique alone. Combining ML-based RTS with two analysis-based RTS techniques-Ekstazi and STARTS-selects 25.34% and 21.44% fewer tests, respectively. 

Test-case prioritization (TCP) aims to detect regression bugs faster via reordering the tests run. While TCP has been studied for over 20 years, it was almost always evaluated using seeded faults/mutants as opposed to using real test failures. In this work, we study the recent change-aware information retrieval (IR) technique for TCP. Prior work has shown it performing better than traditional coverage-based TCP techniques, but it was only evaluated on a small-scale dataset with a cost-unaware metric based on seeded faults/mutants. We extend the prior work by conducting a much larger and more realistic evaluation as well as proposing enhancements that substantially improve the performance. In particular, we evaluate the original technique on a large-scale, real-world software-evolution dataset with real failures using both cost-aware and cost-unaware metrics under various configurations. Also, we design and evaluate hybrid techniques combining the IR features, historical test execution time, and test failure frequencies. Our results show that the change-aware IR technique outperforms stateof-the-art coverage-based techniques in this real-world setting, and our hybrid techniques improve even further upon the original IR technique. Moreover, we show that flaky tests have a substantial impact on evaluating the change-aware TCP techniques based on real test failures.