Mendeley TY _ JOUR ID - 13990710249928 TI - Using Static Information of Programs to Partition the Input Domain in Search-based Test Data Generation JO - Journal of Information Systems and Telecommunication (JIST) JA - ES LA - en SN - 2322-1437 AU - Monemi Bidgoli Atieh AU - haghighi Hasan AD - Shahid Beheshti University AD - Y1 - 2021 PY - 2021 VL - 32 IS - SP - 219 EP - 229 KW - search-based software testing KW - test data generation KW - ant colony optimization KW - input space partitioning DO - N2 - The quality of test data has an important effect on the fault-revealing ability of software testing. Search-based test data generation reformulates testing goals as fitness functions, thus, test data generation can be automated by meta-heuristic algorithms. Meta-heuristic 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 search-based methods. Despite the large volume of works on search-based 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 meta-heuristic 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 UR - rimag.ir/en/Article/15593 L1 - rimag.ir/en/Article/Download/15593 ER -