Combinatorial interaction testing (CIT) is a popular approach to detecting faults in highly configurable software systems. The core task of CIT is to generate a small test suite called a t-way covering array (CA), where t is the covering strength. A major drawback of existing solvers for CA generation is that they usually need considerable time to obtain a high-quality solution, which hinders its wider applications. In this paper, we describe FastCA, an effective and efficient tool for generating constrained CAs. We observe that the high time consumption of existing meta-heuristic algorithms is mainly due to the procedure of score computation. To this end, we present a much more efficient method for score computation. Thanks to this new lightweight score computation method, FastCA can work in the gradient mode to effectively explore the search space. Experiments on a broad range of real-world benchmarks and synthetic benchmarks show that FastCA significantly outperforms state-of-the-art solvers, in terms of both the size of obtained covering array and the run time.

Video: https://youtu.be/-6CuojQIt-k

Repository: https://github.com/jkunlin/FastCATool.git