Meta-heuristics are suited for fast search space exploration since mutation testing test suite generation is an NP-complete optimization issue. Genetic Algorithms and Particle Swarm Optimization are frequently used in search-based software testing, however Fish School Search (FSS) for mutation-driven test data generation is underexplored. This paper recommends altering the Fish School Search algorithm to enhance mutation coverage while reducing computing cost by tailoring individual and collective movement operators to mutation scores. The suggested method is compared to Genetic Algorithms and Particle Swarm Optimization utilizing mutation score as the fitness parameter. Two benchmark programs from the Software-Artifact Infrastructure Repository and four open-source scientific tools are tested. Comparative findings show that the revised FSS technique produces competitive mutation scores with reduced computing effort and quicker convergence. Fish School Search seems to be an effective mutation-based test suite optimization meta-heuristic. The FSS-based test data generation technique was experimentally tested on five benchmark systems, including JTcas and Median, using MuJava mutants. GA, PSO, and FSS all killed mutations similarly across population sizes (N = 10, 20) and iteration counts (10, 20, 40). However, FSS regularly outperformed convergence efficiency. With N = 10 and 10 iterations, FSS lowered convergence time by 84% compared to GA (449,698 ms vs. 2,891,923 ms on JTcas) and 78% compared to PSO. FSS reached convergence in 529,412 ms under 40 iterations, N = 20, whereas GA and PSO took 4,755,045 and 4,257,545 ms, respectively. FSS converged quicker in most benchmark scenarios, saving processing power while retaining mutation scores. .
Kumar et al. (Thu,) studied this question.