A population division based multi-task sine cosine algorithm for test redundancy reduction optimization - Kamal Z., Zamli; Kader, Md Abdul; Mekeng, Ambros Magnus Rudolf (Association for Computing Machinery, 2024)
Test redundancy occurs when one requirement is covered by more than one test. Potentially affecting the testing costs while at the same time delaying the software release, test redundancy is often undesirable. Many recent works have dealt with the test redundancy reduction problem as an optimization problem. Consequently, a plethora of work utilizing meta-heuristic algorithms as the backbone algorithm for addressing the test redundancy reduction problem can be seen in the literature. Although useful, many existing meta-heuristic-based algorithms have focused on solving the test redundancy reduction problem as a single task problem (i.e., one-test redundancy task at-a-time). To cater for simultaneous test redundancy reduction from multiple software development projects, a multi-task-based test redundancy reduction algorithm is desirable. This paper explores the design and implementation of a multi-task sine cosine algorithm (MT-SCA) for test redundancy reduction optimization. More precisely, MT-SCA exploits a population division-based approach to achieve multi-task capability. Experimental results demonstrate that MT-SCA gives comparable test reduction size and execution time against its single solution and other metaheuristic counterparts.