An ensemble-based predictive mutation testing approach that considers impact of unreached mutants,Software Testing, Verification and Reliability

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An ensemble-based predictive mutation testing approach that considers impact of unreached mutants
Software Testing, Verification and Reliability ( IF 1.5 ) Pub Date : 2021-06-02 , DOI: 10.1002/stvr.1784
Alireza Aghamohammadi ₁ , Seyed‐Hassan Mirian‐Hosseinabadi ₁

Affiliation

Predictive mutation testing (PMT) is a technique to predict whether a mutant is killed, using machine learning approaches. Researchers have proposed various methods for PMT over the years. However, the impact of unreached mutants on PMT is not fully addressed. A mutant is unreached if the statement on which the mutant is generated is not executed by any test cases. We aim at showing that unreached mutants can inflate PMT results. Moreover, we propose an alternative approach to PMT, suggesting a different interpretation for PMT. To this end, we replicated the previous PMT research. We empirically evaluated the suggested approach on 654 Java projects provided by prior literature. Our results indicate that the performance of PMT drastically decreases in terms of area under a receiver operating characteristic curve (AUC) from 0.833 to 0.517. Furthermore, PMT performs worse than random guesses on 27% of the projects. The proposed approach improves the PMT results, achieving the average AUC value of 0.613. As a result, we recommend researchers to remove unreached mutants when reporting the results.

中文翻译：

一种考虑未到达突变体影响的基于集成的预测突变测试方法

预测突变测试 (PMT) 是一种使用机器学习方法预测突变体是否被杀死的技术。多年来，研究人员提出了各种 PMT 方法。然而，尚未完全解决未达到的突变体对 PMT 的影响。如果生成突变体的语句没有被任何测试用例执行，则未达到突变体。我们旨在表明未达到的突变体可以夸大 PMT 结果。此外，我们提出了 PMT 的替代方法，提出了对 PMT 的不同解释。为此，我们复制了之前的 PMT 研究。我们对先前文献提供的 654 个 Java 项目的建议方法进行了实证评估。我们的结果表明 PMT 的性能在接受者操作特征曲线 (AUC) 下的面积从 0.833 到 0.517 急剧下降。此外，在 27% 的项目中，PMT 的表现比随机猜测差。所提出的方法改进了 PMT 结果，实现了 0.613 的平均 AUC 值。因此，我们建议研究人员在报告结果时删除未达到的突变体。

更新日期：2021-06-02

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