A Novel Real Coded Genetic Algorithm for Software Mutation Testing

Information Technology has rapidly developed in recent years and software systems can play a critical role the symmetry of technology. Regarding field testing, white-box unit-level testing constitutes backbone all other techniques, as be entirely implemented by considering source code each System Under Test (SUT). In mutants used; these are artificially generated faults seeded SUT that behave similarly to realistic ones. Executing test cases against results adequacy (mutation) score case. Efficient Genetic Algorithm (GA)-based methods have been proposed address different problems unit and, particular, issues mutation techniques. this research paper, new approach, which integrates path coverage-based method with novel idea tracing Fault Detection Matrix (FDM) achieve maximum coverage, is proposed. The real coded GA for designed highest Mutation Score, it thus named RGA-MS. approach two phases: data initially stored an optimized suite. next phase, suite executed kill present SUT. aims minimum dataset, having at same time Score removing duplicate covering mutants. on SUTs used testing. We proved RGA-MS cover number cases. Furthermore, generate generation compared algorithms. addition, covered less no duplicates. Ultimately, optimal trained Score. find coverage well delete redundant