Implementing test case selection and reduction techniques using meta-heuristics

Regression Testing is an inevitable and very costly maintenance activity that is implemented to make sure the validity of modified software in a time and resource constrained environment. Execution of entire test suite is not possible so it is necessary to apply techniques like Test Case Selection and Test Case Prioritization to select and prioritize a minimum set of test cases, fulfilling some chosen criteria, that is, covering all possible faults in minimum time and other. In this paper a test case reduction hybrid Particle Swarm Optimization (PSO) algorithm has been proposed. This PSO algorithm uses GA mutation operator while processing. PSO is a swarm intelligence algorithm based on particles behavior. GA is an evolutionary algorithm (EA). The proposed algorithm is an optimistic approach which provides optimum best results in minimum time.