Multi-objective Test Case Selection Through Linkage Learning-driven Crossover

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Abstract

Test case selection (TCS) aims to select a subset of the test suite to run for regression testing. The selection is typically based on past coverage and execution cost data. Researchers have successfully used multi-objective evolutionary algorithms (MOEAs), such as NSGA-II and its variants, to solve the problem. These MOEAs use traditional crossovers to create new candidate solutions during the search. Recent studies in evolutionary computation showed that more effective recombinations can be made by using linkage learning. Inspired by these recent advances in this field, we propose a new variant of NSGA-II, called L2-NSGA, that uses linkage learning to optimize test case selection. In particular, we use an unsupervised clustering algorithm to infer promising patterns among the solutions (sub-test suites). Then, these patterns are used in the next iterations of L2-NSGA to create solutions that contain/preserve these inferred patterns. Our results show that our customizations make NSGA-II more effective for test case selection. Furthermore, the test suite sub-sets generated by L2-NSGA are less expensive and more effective (detect more faults) than those generated by MOEAs used in the literature for regression testing.
Original languageEnglish
Title of host publicationSearch-Based Software Engineering - 13th International Symposium, SSBSE 2021
Publication statusPublished - 2021

Keywords

  • Regression Testing
  • Test Case Selection
  • Evolutionary Algorithm
  • Machine Learning

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