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.
Wed 23 OctDisplayed time zone: Beirut change
14:00 - 15:30
|Reflection-Aware Static Regression Test Selection|
August Shi University of Illinois at Urbana-Champaign, Milica Hadzi-Tanovic Technische Universitat Munchen, Lingming Zhang The University of Texas at Dallas, Darko Marinov University of Illinois at Urbana-Champaign, Owolabi Legunsen University of Illinois at Urbana-ChampaignDOI
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Burcu Kulahcioglu Ozkan Max Planck Institute for Software Systems (MPI-SWS), Rupak Majumdar Max Planck Institute for Software Systems (MPI-SWS), Simin Oraee Max Planck Institute for Software Systems (MPI-SWS)DOI
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Ranadeep Biswas IRIF, University Paris Diderot & CNRS, Constantin Enea IRIF, University Paris Diderot & CNRSDOI