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 GAbased test case optimization approaches and provide insight in determining the potential research directions in this area.Keywords: Test case optimization, regression testing, multi-objectives, genetic algorithm, software testin

Enhanced non-dominated sorting genetic algorithm for test case optimization

Due to inevitable software changes, regression testing has become a crucial phase in software development process. Many software testers and researchers agreed that regression testing process consumes more time and cost during software development. Test case optimization has become one of the best solutions to overcome problems in regression testing. Test case optimization is focusing on reducing number of test cases in the test suite that may reduce the overall testing time, cost and effort of software testers. It considers multiple objectives and provides several numbers of optimal solution based on objectives of the testing. Therefore, this research aims at developing an alternative solution of test case optimization technique using NSGA II with fitness scaling as an additional function. Fitness scaling function is applied in NSGA II to eliminate pre-mature convergence among set of solution in the evolution of offspring in NSGA II which may produce more efficient fitness value. This research focuses on regression testing optimization by implementing weight of test cases and fault detection rate per test case as its objective function for optimization purposes. The proposed technique is applied to the GUI-based testing case study. The result shows that Pareto front produced by enhanced NSGA II give more wider set of solution that contains more alternatives and provide better trade-off among solutions. The evaluation shows that enhanced NSGA II perform better compared to conventional NSGA II by increasing the percentage of the reduced test cases with 25% and yield lower fault detection loss with 1.64% which indicating that set of reduced test cases using enhanced NSGA II is able to maintain the fault detection capability in the system under test