APPLYING PARTICLE SWARM OPTIMIZATION ALGORITHM FOR TEST SUITE MINIMIZATION IN SOFTWARE TESTING

Software engineering is a discipline which promises to produce quality software that exceeds customer expectation. To make these pledge realities, software testing is indispensable. More efficient and effective testing is conducted through automated testing which involves the use of automatically generated test cases. In Regression testing due to modification in any module the size of test suite generated increases because of regeneration of redundant test cases thus running all the test cases in a test suite requires a large amount of effort and time and becomes infeasible to run all test cases as a result various methods have been proposed to address these Test Suite Minimization (TSM) problem. Most of the studies have focused on removing of redundant test cases with reduction in fault detection capability of the test suite and there is limited evidence of the application of optimization techniques which are able to reduce test suite based on fault coverage information without reducing the fault detection capability and with faster execution time, lowering execution cost. In this regard we proposed a novel techniques particle swarm optimization (PSO) for TSM which minimize the suite without reducing the fault detection capability. We conducted two experiments in experiment one, we compared our technique with four TSM techniques Greedy Algorithm for weighted set cover (G_WSC), Greedy Algorithmic (G), Harrold-Gupta-Soffa (HGS) heuristic algorithms, and Greedy, Redundant, Essential (GRE) algorithm based on the size of reduced set and execution cost.