Optimization of Test Cases: A Meta-Heuristic Approach

Abstract

Software testing is considered as one of the most challenging and time consuming activity involved in the software development process. Testing involves the creation of test cases that helps to explore the defects present in the software system. Test cases play a major role in the testing process to uncover the critical errors that exist in the system. Thus, it is necessary to prioritize test cases for effective testing. This paper aims at performing a comparative study for the optimization of test cases involved in software development using Meta-heuristic techniques. The weightage of each test case is determined,which helps in optimizing the test cases. The performance is evaluated by comparing two metaheuristic algorithms, namely Particle Swarm Optimization (PSO) and Genetic Algorithm (GA).The result shows PSO outperforms GA in terms of accuracy, error rate, and execution time for the chosen test case optimization problem.