Study the Impact of Parameter Settings and Operators Role for Genetic Algorithm Based Test Case Prioritization

Abstract

Test case prioritization schedules the test execution order for regression testing of updated software. To boost the efficiency of prioritizing the test cases, the optimization approaches are used. Most of the researchers have used genetic algorithms for optimizing the prioritization process. In this paper, we are empirically analyzing the effect of different parameter settings and operators rates so as to increase the efficiency and effectiveness of the test case prioritization done with the help of genetic algorithm. A real world application is taken for experimental purpose and APFD metric is used for evaluating the performance measurement. It is found that the tournament selection scheme performed well than other selection schemes. The crossover rates and mutation rates also have significant impact on the technique. The product of population size and generation size proportionate to the fitness function evaluations so their values affect the final result and time overheads.