Searching and evolving test cases using moth flame optimisation for mutation testing

摘要

Generally, mutation testing has been considered effective to test the adequacy of the test suite over a set of artificial faults. These faults are created by applying different mutation operators of mutation testing and manually finding the test suites for revealing these faults is a costly and extensive process. Meta-heuristic techniques may curtail this cost by searching the optimal test suite in search space. These techniques iteratively find and evolve the solution towards an optimal solution. These techniques perform better when blended with mutation testing. This paper proposes and employs a novel mutation-based test generation approach, MFO-MT, inspired by moths' behaviour. Moths fly and search for a better solution in a spiral motion around the flames. The proposed approach is implemented and tested for various Java programs. The approach gives encouraging results when compared with genetic algorithm and random testing.