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

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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 this problem. These MOEAs use traditional crossover operators to create new candidate solutions through genetic recombination. Recent studies in numerical optimization have shown that better recombinations can be made using machine learning, in particular 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 (subset of test suites). Then, these patterns are used in the next iterations of L2-NSGA to create solutions that preserve these inferred patterns. Our results show that our customizations make NSGA-II more effective for test case selection. The test suite sub-sets generated by L2-NSGA are less expensive and 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, Proceedings
EditorsUna-May O'Reilly, Xavier Devroey
Number of pages16
ISBN (Electronic)978-3-030-88106-1
Publication statusPublished - 2021
Event13th International Symposium Search-Based Software Engineering - Bari, Italy
Duration: 11 Oct 202112 Oct 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12914 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference13th International Symposium Search-Based Software Engineering
Abbreviated titleSSBSE 2021


  • Multi-objective optimization
  • Regression testing
  • Search-based software engineering
  • Test case selection


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