Dynamic Mutant Subsumption Analysis using LittleDarwin

Many academic studies in the field of software testing rely on mutation testing to use as their comparison criteria. However, recent studies have shown that redundant mutants have a significant effect on the accuracy of their results. One solution to this problem is to use mutant subsumption to detect redundant mutants. Therefore, in order to facilitate research in this field, a mutation testing tool that is capable of detecting redundant mutants is needed. In this paper, we describe how we improved our tool, LittleDarwin, to fulfill this requirement

Evaluating Random Mutant Selection at Class-Level in Projects with Non-Adequate Test Suites

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.Comment: EASE 2016, Article 11 , 10 page

A Model to Estimate First-Order Mutation Coverage from Higher-Order Mutation Coverage

The test suite is essential for fault detection during software development. First-order mutation coverage is an accurate metric to quantify the quality of the test suite. However, it is computationally expensive. Hence, the adoption of this metric is limited. In this study, we address this issue by proposing a realistic model able to estimate first-order mutation coverage using only higher-order mutation coverage. Our study shows how the estimation evolves along with the order of mutation. We validate the model with an empirical study based on 17 open-source projects.Comment: 2016 IEEE International Conference on Software Quality, Reliability, and Security. 9 page