ABSTRACT In Software Engineering Regression Testing is a mandatory activity. Whenever, a change in existing system occurs and new version appears, the unchanged portions need to be regression tested for any resulting undesirable effects. During process of Regression Testing, same test cases are executed repeatedly for un-modified portion of software. This activity is an overhead and consumes huge resources and budget. To save time and resources, researches have proposed various techniques for Regression Test Suite Optimization. In this research regression test suites are minimized using three Computational Intelligence multi-objective techniques for black box testing methods. These include; 1- Multi-Objective Genetic Algorithms (MOGA), 2- Non-Dominated Sorting Genetic Algorithm (NSGA-II) and 3- Multi-Objective Particle Swarm Optimization (MOPSO). Said techniques are applied on two published case studies and through experimentation, the quality of these techniques is analyzed. Four quality metrics are defined to perform this analysis. The results of research show that MOGA is better for reducing the size and thus execution time of the regression test suites as compared to MOPSO and NSGA-II. It was also found that use of MOGA, NSGA-II and MOPSO are not safe for regression test suite optimization. This is because fault detection rate and requirement coverage is reduced after optimization of Regression Test Suites.
