Multi-Task Test Redundancy Reduction Optimization Problem based on the Brown Bear Algorithm

A condition test redundancy occurs when many tests address one or more requirements. In addition to cost factors, test redundancy can be undesirable during software testing activities. The test redundancy reduction problem is regarded as an optimization problem, and numerous previous studies have addressed it by utilizing a variety of metaheuristic algorithms as the foundational solution implementation (e.g., Brown Bear Algorithm, Sine Cosine algorithm, Particle Swarm Optimization, etc.). Implementations based on metaheuristics have proven to be effective in addressing test redundancy and minimizing testing costs thus far. Despite their usefulness, the majority of current metaheuristic solutions concentrate primarily on test redundancy reduction as a single optimization effort. In this study, brown bear based used to study about multitask optimization. In implementation of brown bear algorithm, we measure based on two research question: the correctness of multitasks brown bear on TRR; and the comparison with sequential multitask. This study proven the brown bear based manage to fulfill all requirements needed and the performance of multitask brown bear improve the time taken in software testing. As conclusion, multitask brown bear minimized the number of testcases to be tested and reduce testing costs.