Evaluating Random Mutant Selection at Class-Level in Projects with Non-Adequate Test Suites
This work addresses mutation testing efficiency for software developers, but it is incremental as it builds on existing random selection methods.
The paper tackled the problem of random mutant selection in mutation testing for projects with non-adequate test suites, showing that uniform random selection underachieves and proposing a weighted random approach that generates more representative samples, with results indicating larger samples for projects with high test adequacy.
Mutation testing is a standard technique to evaluate the quality of a test suite. Due to its computationally intensive nature, many approaches have been proposed to make this technique feasible in real case scenarios. Among these approaches, uniform random mutant selection has been demonstrated to be simple and promising. However, works on this area analyze mutant samples at project level mainly on projects with adequate test suites. In this paper, we fill this lack of empirical validation by analyzing random mutant selection at class level on projects with non-adequate test suites. First, we show that uniform random mutant selection underachieves the expected results. Then, we propose a new approach named weighted random mutant selection which generates more representative mutant samples. Finally, we show that representative mutant samples are larger for projects with high test adequacy.