论文标题
对预测突变测试有效性的威胁：未发现突变体的影响
The Threat to the Validity of Predictive Mutation Testing: The Impact of Uncovered Mutants
论文作者
论文摘要
预测突变测试（PMT）是一种预测是否使用机器学习方法杀死突变体的技术。研究人员在跨项目设置下提出了针对PMT的各种机器学习方法。但是，他们没有考虑发现突变体的影响。如果任何测试用例未执行突变体的陈述，则发现突变体。我们表明，未发现突变体会膨胀以前的PMT结果。此外，我们旨在提出一种改善PMT的替代方法，并建议对跨项目PMT进行不同的解释。我们复制了先前的PMT研究。我们还提出了一种基于随机森林和梯度增强的组合以改善PMT结果的方法。我们对以前PMT文献提供的相同654个Java项目进行了经验评估我们的方法。我们的结果表明，PMT的性能从AUC大幅下降到0.83至0.51。此外，PMT的表现比27％的测试项目的随机猜测要差。提出的方法通过达到0.61的平均AUC值来改善PMT结果。
Predictive Mutation Testing (PMT) is a technique to predict whether a mutant will be killed by using machine learning approaches. Researchers have proposed various machine learning methods for PMT under the cross-project setting. However, they did not consider the impact of uncovered mutants. A mutant is uncovered if the statement on which the mutant is generated is not executed by any test cases. We show that uncovered mutants inflate previous PMT results. Moreover, we aim at proposing an alternative approach to improve PMT and suggesting a different interpretation for cross-project PMT. We replicated the previous PMT research. We also proposed an approach based on the combination of Random Forest and Gradient Boosting to improve the PMT results. We empirically evaluated our approach on the same 654 Java projects provided by the previous PMT literature. Our results indicate that the performance of PMT drastically decreases in terms of AUC from 0.83 to 0.51. Furthermore, PMT performs worse than random guesses on 27% of the test projects. The proposed approach improves the PMT results by achieving the average AUC value of 0.61.
