Test Case Level Predictive Mutation Testing Combining PIE and Natural Language Features

Approaches predicting the results of mutation testing by machine learning have been proposed to reduce the cost of mutation testing. The predictive approaches based on PIE theory and approaches based on natural language have been proposed. However, both PIE-based and natural language-based approaches have disadvantages, leading to a reduction in effectiveness at the test case level prediction. In order to predict at the test case level and improve the effectiveness of prediction, we propose Natural Language and PIE Predictive Mutation Testing (NLPIE-PMT), which combines advantages of PIE-based and natural language-based approaches and predict whether each test case kills each mutant in the cross-version scenario. The experimental results on subjects in Defects4J show that NLPIE-PMT can predict whether each test case kill each mutant with the average F1-score of 0.811, which is 0.135 and 0.046 higher than the PIE-based baseline and the natural language-based baseline respectively. NLPIE-PMT also performs better than the baselines in predicting mutation score.