Mutation testing is a powerful methodology for evaluating test suite quality. In mutation testing, a large number of mutants are generated and executed against the test suite to check the ratio of killed mutants. Therefore, mutation testing is widely believed to be a computationally expensive technique. To alleviate the efficiency concern of mutation testing, in this paper, we propose predictive mutation testing (PMT), the first approach to predicting mutation testing results without mutant execution. In particular, the proposed approach constructs a classification model based on a series of features related to mutants and tests, and uses the classification model to predict whether a mutant is killed or survived without executing it. PMT has been evaluated on 163 real-world projects under two application scenarios (i.e., cross-version and cross-project). The experimental results demonstrate that PMT improves the efficiency of mutation testing by up to 151.4X while incurring only a small accuracy loss when predicting mutant execution results, indicating a good tradeoff between efficiency and effectiveness of mutation testing.

Software testing aims to assure the quality of software under test. To improve the efficiency of software testing, especially regression testing, test-case prioritization is proposed to schedule the execution order of test cases in software testing. Among various test-case prioritization techniques, the simple additional coverage-based technique, which is a greedy strategy, achieves surprisingly competitive empirical results. To investigate how much difference there is between the order produced by the additional technique and the optimal order in terms of coverage, we conduct a study on various empirical properties of optimal coverage-based test-case prioritization. To enable us to achieve the optimal order in acceptable time for our object programs, we formulate optimal coverage-based test-case prioritization as an integer linear programming (ILP) problem. Then we conduct an empirical study for comparing the optimal technique with the simple additional coverage-based technique. From this empirical study, the optimal technique can only slightly outperform the additional coverage-based technique with no statistically significant difference in terms of coverage, and the latter significantly outperforms the former in terms of either fault detection or execution time. As the optimal technique schedules the execution order of test cases based on their structural coverage rather than detected faults, we further implement the ideal optimal test-case prioritization technique, which schedules the execution order of test cases based on their detected faults. Taking this ideal technique as the upper bound of test-case prioritization, we conduct another empirical study for comparing the optimal technique and the simple additional technique with this ideal technique. From this empirical study, both the optimal technique and the additional technique significantly outperform the ideal technique in terms of coverage, but the latter significantly outperforms the former two techniques in terms of fault detection. Our findings indicate that researchers may need take cautions in pursuing the optimal techniques in test-case prioritization with intermediate goals.