Tri-level regression testing using nature-inspired algorithms

• Published in: Innovations in systems and software engineering Vol. 17; no. 1; pp. 1 - 16
• Main Authors: Bajaj, Anu, Sangwan, Om Prakash
• Format: Journal Article
• Language: English
• Published: London Springer London 01.03.2021; Springer Nature B.V
• Subjects: Artificial Intelligence; Computer Applications; Computer Science; Genetic algorithms; Optimization techniques; Original Article; Particle swarm optimization; Public domain; Regression; Search algorithms; Software Engineering; Software testing; Test case minimization; Gravitational search algorithm; Test case prioritization; Genetic algorithm; Nature-inspired algorithms; Regression testing; Test case selection; Search-based software testing; Particle swarm optimization
• ISSN: 1614-5046; 1614-5054
• DOI: 10.1007/s11334-021-00384-9

A software needs to be updated to survive in the customers’ ever-changing demands and the competitive market. The modifications may produce undesirable changes that require retesting, known as regression testing, before releasing it in the public domain. This retesting cost increases with the growth of the software test suite. Thus, regression testing is divided into three techniques: test case prioritization, selection, and minimization to reduce costs and efforts. The efficiency and effectiveness of these techniques are further enhanced with the help of optimization techniques. Therefore, we present the regression testing using well-known algorithms, genetic algorithm, particle swarm optimization, a relatively new nature-inspired approach, gravitational search algorithm, and its hybrid with particle swarm optimization algorithm. Furthermore, we propose a tri-level regression testing, i.e., it performs all the three methods in succession. Nature-inspired algorithms prioritize the test cases on code coverage criteria. It is followed by selecting the modification-revealing test cases based on the proposed adaptive test case selection approach. The last step consists of the removal of redundant test cases. The hybrid algorithm performed well for the average percentage of statement coverage, and the efficiency of genetic algorithm and particle swarm optimization is better comparatively. The proposed test case selection method can select at least 75% modification-revealing test cases using nature-inspired algorithms. Additionally, it minimizes the test suite with full statement coverage and almost negligible fault coverage loss. Overall, the simulation results show that the proposed hybrid technique outperformed the other algorithms.