A Comparative Analysis of Bat and Genetic Algorithms for Test Case Prioritization in Regression Testing

Abstract

Regression testing is carried out to ensure that software modifications do not introduce new potential bugs to the existing software. Existing test cases are applied in the testing, such test cases can run into thousands, and there is not much time to execute all of them. Test Case Prioritization (TCP) is a technique to order test cases so that the test cases potentially revealing more faults are performed first. With TCP being deemed an optimization problem, several metaheuristic nature-inspired algorithms such as Bat, Genetic, Ant colony, and Firefly algorithms have been proposed for TCP. These algorithms have been compared theoretically or based on a single metric. This study employed an experimental design to offer an in-depth comparison of bat and genetic algorithms for TCP. Unprioritized test cases and a brute-force approach were used for comparison. Average Percentage Fault Detection (APFD)- a popular metric, execution time and memory usage were used to evaluate the algorithms’ performance. The study underscored the importance of test case prioritization and established the superiority of the Genetic algorithm over the bat algorithm for TCP in APFD. No stark differences were recorded regarding memory usage and execution time for the two algorithms. Both algorithms seemed to scale well with the growth of test cases.