Comparative evaluation of genetic algorithm-based test case optimization

Software testing is a crucial phase in software development process although it consumes more time and cost of software development. Researchers have proposed several approaches focusing on helping software testers to reduce the execution time and cost of the testing process. Test case optimization is a multi-objective approach that has become one of the best solutions to overcome these problems. Test case optimization focusing on reducing the number of test cases in the test suite that may reduce the overall testing time, cost and effort of software testers especially in regression testing. This paper presents the comparative evaluation between test case optimization techniques that are based on Genetic Algorithm (GA). The evaluation is based on five criteria i.e. technique objectives, applied fitness function, contributions, the percentage of the reduced test cases, fault detection capability, and technique limitations. The evaluation results able identify the gaps in the existing GA-based test case optimization approaches and provide insight in determining the potential research directions in this area.