Prioritizing and Minimizing the Test Cases using the Dragonfly Algorithms

Abstract

Regression testing is a necessary but costly process. It involves re-running all of the test cases each time the software is updated. The resources and time needed for retesting can be decreased by minimizing redundancy and prioritizing the test cases. Furthermore, optimization procedures enhance the efficacy of test case prioritization and minimization. In this research, we have proposed a discrete and combinatorial dragonfly algorithm. In addition, its hybrid version is created with a particle swarm optimization algorithm. The suggested approaches are compared to the random search, genetic algorithm, particle swarm optimization and the bat algorithm. The assessment is done on four subject programs of differing sizes. The simulation results show that the proposed methods are more efficient and effective than the compared algorithms. Furthermore, the hybrid algorithm has a compact distribution as seen by boxplots and interval plots of the average percentage of fault detection and the test minimization percentage.