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In recent years, Automated Program Repair (APR) has been extensively studiedin academia and even drawn wide attention from industry. However, APRtechniques can be extremely time consuming since (1) a large number of patchescan be generated for a given bug, and (2) each patch needs to be executed onthe original tests to ensure its correctness. In the literature, varioustechniques (e.g., based on learning, mining, and constraint solving) have beenproposed/studied to reduce the number of patches. Intuitively, every patch canbe treated as a software revision during regression testing; thus, traditionalRegression Test Selection (RTS) techniques can be leveraged to only execute thetests affected by each patch (as the other tests would keep the same outcomes)to further reduce patch execution time. However, few APR systems actually adoptRTS and there is still a lack of systematic studies demonstrating the benefitsof RTS and the impact of different RTS strategies on APR. To this end, thispaper presents the first extensive study of widely-used RTS techniques atdifferent levels (i.e., class/method/statement levels) for 12 state-of-the-artAPR systems on over 2M patches. Our study reveals various practical guidelinesfor bridging the gap between APR and regression testing.

When Automated Program Repair Meets Regression Testing -- An Extensive Study on 2 Million Patches