An efficient regression test suite optimisation approach using adaptive salp swarm optimisation

Arun Prakash Agrawal, Ankur Choudhary and Hari Mohan Pandey

International Journal of Business Information Systems, 2023, vol. 43, issue 4, 486-506

Abstract: Software keeps evolving to increase return on investment (ROI) in software development. This gives rise to continuous testing in order to keep the software operational for a longer period and has become a major challenge for software industry. To address this issue, we need to optimise the regression testing cost. However, many heuristic and metaheuristic approaches have been proposed in literature, yet there is room for improvement as they suffer from the problems of high computational cost and questionable testability. In this paper, authors propose an adaptive salp swarm optimisation algorithm to solve regression test suite optimisation problem and is an enhancement of salp swarm optimisation algorithm. Extensive experiments are conducted on benchmarked open source testing datasets to evaluate the performance of proposed approach and have been compared statistically with state of the art approaches - bat, salp swarm, and cuckoo search with respect to fault detection effectiveness and execution time.

Keywords: regression testing; test case selection; heuristics; metaheuristics; nature inspired approach; adaptive salp swarm optimisation; ASSO; salp swarm optimisation; bat search optimisation; cuckoo search. (search for similar items in EconPapers)
Date: 2023
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