An empirical comparison of four Java-based regression test selection techniques - Shin, Min Kyung, author; Ghosh, Sudipto, advisor; Moreno Cubillos, Laura, committee member; Vijayasarathy, Leo R., committee member (Colorado State University. Libraries, 2020)
Regression testing is crucial to ensure that previously tested functionality is not broken by additions, modifications, and deletions to the program code. Since regression testing is an expensive process, researchers have developed regression test selection (RTS) techniques, which select and execute only those test cases that are impacted by the code changes. In general, an RTS technique has two main activities, which are (1) determining dependencies between the source code and test cases, and (2) identifying the code changes. Different approaches exist in the research literature to compute dependencies statically or dynamically at different levels of granularity. Also, code changes can be identified at different levels of granularity using different techniques. As a result, RTS techniques possess different characteristics related to the amount of reduction in the test suite size, time to select and run the test cases, test selection accuracy, and fault detection ability of the selected subset of test cases. Researchers have empirically evaluated the RTS techniques, but the evaluations were generally conducted using different experimental settings. This thesis compares four recent Java-based RTS techniques, Ekstazi, HyRTS, OpenClover, and STARTS, with respect to the above-mentioned characteristics using multiple revisions from five open source projects. It investigates the relationship between four program features and the performance of RTS techniques: total (program and test suite) size in KLOC, total number of classes, percentage of test classes over the total number of classes, and the percentage of classes that changed between revisions. The results show that STARTS, a static RTS technique, over-estimates dependencies between test cases and program code, and thus, selects more test cases than the dynamic RTS techniques Ekstazi and HyRTS, even though all three identify code changes in the same way. OpenClover identifies code changes differently from Ekstazi, HyRTS, and STARTS, and selects more test cases. STARTS achieved the lowest safety violation with respect to Ekstazi, and HyRTS achieved the lowest precision violation with respect to both STARTS and Ekstazi. Overall, the average fault detection ability of the RTS techniques was 8.75% lower than that of the original test suite. STARTS, Ekstazi, and HyRTS achieved higher test suite size reduction on the projects with over 100 KLOC than those with less than 100 KLOC. OpenClover achieved a higher test suite size reduction in the subjects that had a fewer total number of classes. The time reduction of OpenClover is affected by the combination of the number of source classes and the number of test cases in the subjects. The higher the number of test cases and source classes, the lower the time reduction.
Keywords: precision; safety; test suite reduction; regression test selection; dynamic analysis; static analysis