Regression Test Case Selection: A Comparative Analysis of Metaheuristic Algorithms

Regression testing is an activity of finding bugs in the modified parts of the software and releases the software versions timely to avoid further risks. Retesting of all existing test cases including obsolete and redundant test cases is increasing the cost and efforts of the overall process. In order to reduce this cost and time, optimization algorithms are playing a vital role. This paper focuses on the performance analysis of three recent metaheuristic algorithms: Cuckoo Search, Crow Search Algorithm, and Harris Hawks Optimization to solve the RTCS problem for selecting the test cases. Fault coverage and execution time parameters have been selected for performance evaluation. The experiments are performed and analyzed on standard SIR repository. The results and statistical tests show that Cuckoo Search and Crow Search Algorithm significantly give better results for different parameters of RTCS problem than Harris Hawks Optimization (HHO). The Cuckoo Search outperformed on fault coverage, and Crow Search Algorithm outperformed on time parameter.