Coverage-based quality metric of mutation operators for test suite improvement

Abstract

The choice of mutation operators is a fundamental aspect in mutation testing to guide the tester to an effective test suite. Designing a set of mutation operators is subject to a trade-off between effectiveness and computational cost: a larger mutation population might uncover more faults, but will take longer to analyse. With the aim of resolving this trade-off, several authors have defined an assortment of metrics to determine the most valuable operators. In this work, we extend an existing quality metric by incorporating an additional source of data and coverage information and therefore investigate the extent to which mutants that are often covered but rarely killed can improve the evaluation of mutation operators for the refinement of the test suite. As a case study, we analyse C++ class-level operators based on the new coverage-based quality metric to assess whether the original metric is enhanced. The results when selecting the best-valued operators show that this metric has great potential to help the tester in finding effective mutation operators. In comparison with the metric from which it is derived, the use of coverage data allows to reduce the number of mutants but often loses fewer test cases and, in addition, retains those that seem hard to design.