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Learning-based mutant reduction using fine-grained mutation operators
Software Testing, Verification and Reliability ( IF 1.5 ) Pub Date : 2021-08-08 , DOI: 10.1002/stvr.1786 Yunho Kim 1 , Shin Hong 2
Software Testing, Verification and Reliability ( IF 1.5 ) Pub Date : 2021-08-08 , DOI: 10.1002/stvr.1786 Yunho Kim 1 , Shin Hong 2
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For mutation testing, the huge cost of running test suites on a large number of mutants has been a serious obstacle. To resolve this problem, we propose a learning-based mutant reduction technique MuTrain. MuTrain uses cost-considerate linear regression (i.e., CLARS) to learn a mutation model, which predicts the mutation score of a test suite based on the mutation testing results of a previous version of a target program. Then, MuTrain applies the mutation model for subsequent versions to predict mutation scores with significantly fewer mutants. For effective mutant reduction and accurate mutation score prediction, MuTrain uses fine-grained mutation operators refined from the existing coarse-grained mutation operators. The experiment results show that MuTrain reduces the number of mutants effectively (i.e., selecting only 1.6% of mutants). Moreover, MuTrain predicts mutation score far more accurately than the existing mutant reduction techniques and random mutant selection. We also found that MuTrain achieves much greater mutant reduction when it uses the fine-grained mutation operators than the traditional coarse-grained mutation operators (i.e., 1.6% vs. 14.6%).
中文翻译：
使用细粒度变异算子的基于学习的变异减少
对于突变测试，在大量突变体上运行测试套件的巨大成本一直是一个严重的障碍。为了解决这个问题，我们提出了一种基于学习的突变减少技术MuTrain。MuTrain使用考虑成本的线性回归（即 CLARS）来学习突变模型，该模型根据目标程序的先前版本的突变测试结果来预测测试套件的突变分数。然后，MuTrain将突变模型应用于后续版本，以预测突变分数明显减少。对于有效的突变减少和准确的突变评分预测，MuTrain使用从现有粗粒度变异算子中提炼出来的细粒度变异算子。实验结果表明，MuTrain有效地减少了突变体的数量（即仅选择了1.6% 的突变体）。此外，MuTrain比现有的突变减少技术和随机突变选择更准确地预测突变分数。我们还发现，MuTrain在使用细粒度变异算子时比传统的粗粒度变异算子实现了更大的变异减少（即 1.6% 对 14.6%）。
更新日期：2021-08-08
中文翻译：
使用细粒度变异算子的基于学习的变异减少
对于突变测试，在大量突变体上运行测试套件的巨大成本一直是一个严重的障碍。为了解决这个问题，我们提出了一种基于学习的突变减少技术MuTrain。MuTrain使用考虑成本的线性回归（即 CLARS）来学习突变模型，该模型根据目标程序的先前版本的突变测试结果来预测测试套件的突变分数。然后，MuTrain将突变模型应用于后续版本，以预测突变分数明显减少。对于有效的突变减少和准确的突变评分预测，MuTrain使用从现有粗粒度变异算子中提炼出来的细粒度变异算子。实验结果表明，MuTrain有效地减少了突变体的数量（即仅选择了1.6% 的突变体）。此外，MuTrain比现有的突变减少技术和随机突变选择更准确地预测突变分数。我们还发现，MuTrain在使用细粒度变异算子时比传统的粗粒度变异算子实现了更大的变异减少（即 1.6% 对 14.6%）。
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