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.

中文翻译：

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基于链接学习交叉的多目标测试用例选择

测试用例选择 (TCS) 旨在选择测试套件的子集来运行回归测试。选择通常基于过去的覆盖范围和执行成本数据。研究人员已成功使用多目标进化算法 (MOEA)，例如 NSGA-II 及其变体来解决此问题。这些 MOEA 使用传统的交叉算子通过基因重组创建新的候选解决方案。最近在数值优化方面的研究表明，使用机器学习，特别是链接年龄学习可以进行更好的重组。受该领域这些最新进展的启发，我们提出了一种新的 NSGA-II 变体，称为 L2-NSGA，它使用链接学习来优化测试用例选择。特别是，我们使用无监督聚类算法来推断解决方案（测试套件的子集）中的有希望的模式。然后，在 L2-NSGA 的下一次迭代中使用这些模式来创建保留这些推断模式的解决方案。我们的结果表明，我们的定制使 NSGA-II 更有效地选择测试用例。由 L2-NSGA 生成的测试套件子集比文献中用于回归测试的 MOEA 生成的测试套件子集成本更低，并且检测到的故障更多。