using static information of programs to partition the input domain in searchbased test data generation

The quality of test data has an important effect on the faultrevealing ability of software testing. searchbased test data generation reformulates testing goals as fitness functions, thus, test data generation can be automated by metaheuristic algorithms. metaheuristic algorithms search the domain of input variables in order to find input data that cover the targets. the domain of input variables is very large, even for simple programs, while this size has a major influence on the efficiency and effectiveness of all searchbased methods. despite the large volume of works on searchbased test data generation, the literature contains few approaches that concern the impact of search space reduction. in order to partition the input domain, this study defines a relationship between the structure of the program and the input domain. based on this relationship, we propose a method for partitioning the input domain. then, to search in the partitioned search space, we select ant colony optimization as one of the important and prosperous metaheuristic algorithms. to evaluate the performance of the proposed approach in comparison with the previous work, we selected a number of different benchmark programs. the experimental results show that our approach has 14.40% better average coverage versus the competitive approach