Regression test selection (RTS) aims to speed up regression testing by rerunning only tests that are affected by code changes. RTS can be performed using static or dynamic analysis techniques. Our prior study showed that static and dynamic RTS perform similarly for medium-sized Java projects. However, the results of that prior study also showed that static RTS can be unsafe, missing to select tests that dynamic RTS selects, and that reflection was the only cause of unsafety observed among the evaluated projects.
In this paper, we investigate five techniques—three purely static techniques and two hybrid static-dynamic techniques—that aim to make static RTS safe with respect to reflection. We implement these reflection-aware (RA) techniques by extending the reflection-unaware (RU) class-level static RTS technique in a tool called STARTS. To evaluate these RA techniques, we compare their end-to-end times with RU, and with RetestAll, which reruns all tests after every code change. We also compare safety and precision of the RA techniques with Ekstazi, a state-of-the-art dynamic RTS technique; precision is a measure of unaffected tests selected.
Our evaluation on 1173 versions of 24 open-source Java projects shows negative results. The RA techniques improve the safety of RU but at very high costs. The purely static techniques are safe in our experiments but decrease the precision of RU, with end-to-end time at best 85.8% of RetestAll time, versus 69.1% for RU. One hybrid static-dynamic technique improves the safety of RU but at high cost, with end-to-end time that is 91.2% of RetestAll. The other hybrid static-dynamic technique provides better precision, is safer than RU, and incurs lower end-to-end time—75.8% of RetestAll, but it can still be unsafe in the presence of test-order dependencies. Our study highlights the challenges involved in making static RTS safe with respect to reflection.
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|Reflection-Aware Static Regression Test Selection|
August ShiUniversity of Illinois at Urbana-Champaign, Milica Hadzi-TanovicTechnische Universitat Munchen, Lingming ZhangThe University of Texas at Dallas, Darko MarinovUniversity of Illinois at Urbana-Champaign, Owolabi LegunsenUniversity of Illinois at Urbana-ChampaignDOI
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Burcu Kulahcioglu OzkanMax Planck Institute for Software Systems (MPI-SWS), Rupak MajumdarMax Planck Institute for Software Systems (MPI-SWS), Simin OraeeMax Planck Institute for Software Systems (MPI-SWS)DOI
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