The Bat Algorithm (BA), inspired by the echolocation behavior of bats, has gained prominence as a promising search-based technique for global optimization. This paper explores its application in automatic test suite generation (TSG) through a creative methodology that inserts mutants in the original data and verifies correctness using mathematical constraints. It further provides a comparative analysis against three established search-based algorithms (SBAs): Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC) algorithm. The analysis focuses on test suite size as a primary metric of the algorithm's effectiveness. Through rigorous experimentation, the performance of the Bat Algorithm has been validated in producing better results compared to the baseline algorithms. It outperformed the performance in terms of reduction in test suite size, achieving a 71.8%, 55.82%, and 18.99% reduction compared to GA, PSO, and ABC, respectively. These results highlight its potential for enhancing TSG in various domains of computer science.
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