An efficient specific update search domain based glowworm swarm optimization for test case prioritization

Software testing is an important activity that is carried out during the software development life cycle. Regression testing means re-executing test cases from existing test suites to assure that the modifications done to the existing software have no adverse effects. During regression testing, new test cases are not created but previously created test cases are reexecuted. The ideal regression testing is to rerun all the test cases, but due to time and cost constraints only a subset of test cases are rerun based on regression testing techniques. The various regression testing techniques are test case minimization, test case selection and test case prioritization. In this paper, an approach to solve test case prioritization based on efficient swarm intelligence approach called Glowworm Swarm Optimization (GSO) is proposed. This research work focuses on a conception of definite updating search field at glowworm updating position stage. Based on the Specific Update search domain based GSO (SU-GSO) approach, an optimal number of test cases to be executed on Software Under Test (SUT) is obtained. The objectives of this research work are to maximize the path coverage and fault coverage for getting the optimal prioritized test cases. The resulting solution guarantees an optimal ordering of test cases and the performance of the proposed SU-GSO is compared with other optimization techniques such as Particle Swarm Optimization (PSO) and artificial Bee Colony Optimization (BCO).