Search for: All records

Regression test selection (RTS) speeds up regression testing by only re-running tests that might be affected by code changes. Ideal RTS safely selects all affected tests and precisely selects only affected tests. But, aiming for this ideal is often slower than re-running all tests. So, recent RTS techniques use program analysis to trade precision for speed, i.e., lower regression testing time, or even use machine learning to trade safety for speed. We seek to make recent analysis-based RTS techniques more precise, to further speed up regression testing. Independent studies suggest that these techniques reached a “performance wall” in the speed-ups that they provide. We manually inspect code changes to discover those that do not require re-running tests that are only affected by such changes. We categorize 29 kinds of changes that we find from five projects into 13 findings, 11 of which are semantics-modifying. We enhance two RTS techniques—Ekstazi and STARTS—to reason about our findings. Using 1,150 versions of 23 projects, we evaluate the impact on safety and precision of leveraging such changes. We also evaluate if our findings from a few projects can speed up regression testing in other projects. The results show that our enhancements are effective and they can generalize. On average, they result in selecting 41.7% and 31.8% fewer tests, and take 33.7% and 28.7% less time than Ekstazi and STARTS, respectively, with no loss in safety. 

Regression testing - rerunning tests at 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 high frequency of code changes. Regression test selection (RTS) optimizes regression testing by rerunning only a subset of tests that can be affected by code changes. Researchers showed that RTS based on dynamic and static program analysis can save substantial testing time for (medium-sized) open-source projects. Simultaneously, practitioners showed that RTS based on machine learning (ML) is lightweight and works well on very large software repositories, e.g., in Facebook’s monorepository. We combine analysis-based RTS and ML-based RTS by using ML-based RTS to choose a subset of tests selected by analysis-based RTS. To do so, we first design several novel ML-based RTS techniques that leverage mutation analysis to obtain a training set for learning the impact of code changes on test outcomes. Then, we empirically evaluate, using 10 projects, the benefits of combining various ML models with analysis-based RTS. We also compare combining the techniques with using each technique individually. Combining ML-based RTS with two analysis-based RTS techniques - Ekstazi and STARTS - selects 25.34% and 21.44% fewer tests.