Solving the t -wise Coverage Maximum Problem via Effective and Efficient Local Search-based Sampling

To meet the increasing demand for customized software, highly configurable systems become essential in practice. Such systems offer many options to configure, and ensuring the reliability of these systems is critical. A widely-used evaluation metric for testing these systems is \ (t\) -wise coverage, where \ (t\) represents testing strength, and its value typically ranges from 2 to 6. It is crucial to design effective and efficient methods for generating test suites that achieve high \ (t\) -wise coverage. However, current state-of-the-art methods need to generate large test suites for achieving high \ (t\) -wise coverage. In this work, we propose a novel method called LS-Sampling-Plus that can efficiently generate test suites with high \ (t\) -wise coverage for \ (2 t 6\) while being smaller in size compared to existing state-of-the-art methods. LS-Sampling-Plus incorporates many core algorithmic techniques, including two novel scoring functions, a dynamic mechanism for updating sampling probabilities, and a validity-guaranteed systematic search method. Our experiments on various practical benchmarks show that LS-Sampling-Plus can achieve higher \ (t\) -wise coverage than current state-of-the-art methods, through building a test suite of the same size. Moreover, our evaluations indicate the effectiveness of all core algorithmic techniques of LS-Sampling-Plus. Further, LS-Sampling-Plus exhibits better scalability and fault detection capability than existing state-of-the-art methods.