Hybrid Particle Swarm Optimization for Regression Testing

Regression Testing ensures that any enhancement made to software will not affect specified functionality of software. The execution of all test cases can be long and complex to run; this makes it a costlier process. The prioritization of test cases can help in reduction in cost of regression testing, as it is inefficient to rerun each and every test case. In this research paper, the criterion considered is of maximum fault coverage in minimum execution time. In this research paper, the Hybrid Particle Swarm Optimization (HPSO) algorithm has been used, to make regression testing efficient. The HPSO is a combination of Particle Swarm Optimization (PSO) technique and Genetic Algorithms (GA), to widen the search space for the solution. The Genetic Algorithm (GA) operators provides optimized way to perform prioritization in regression testing and on blending it with Particle Swarm Optimization (PSO) technique makes it effective and provides fast solution. The Genetic Algorithm (GA) operator that has been used is Mutation operator which allows the search engine to evaluate all aspects of the search space. Here, AVERAGE PERCENTAGE OF FAULTS DETECTED (APFD) metric has been used to represent the solution derived from HPSO for better transparency in proposed algorithm.