Search based combinatorial testing

Search techniques can dramatically change our ability to solve a host of problems in applied science and engineering, many search techniques have been developed and applied successfully in many fields, including search based software engineering (SBSE). As a key problem of combinatorial testing, covering array generation has been widely studied and many search techniques have been applied which can be named as search based combinatorial testing (SBCT). SBCT is a branch of search based software testing (SBST) within SBSE. In this paper, to explore the applicability and effectiveness of SBCT, we design six variants from existing search algorithms: Genetic Algorithm, Particle Swarm Optimization and Ant Colony Algorithm by reversing and randomizing their mechanisms. We study their effectiveness in terms of generating a covering array and compare their performance. Experiments show that these search techniques can work well with distinct performance in covering array generation. We believe that these search techniques can be further improved by fine-tuning their configuration and used in broad ranges of area