Multi-objective Test Case Selection Through Linkage Learning-based Crossover
This work addresses regression testing efficiency for software developers, but it is incremental as it builds on existing MOEAs with a novel crossover method.
The paper tackled the problem of test case selection for regression testing by proposing L2-NSGA, a variant of NSGA-II that uses linkage learning to optimize selection. The results showed that L2-NSGA generated subsets that were less expensive and detected more faults compared to existing MOEAs.
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 link-age 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.