| Summary: | Test Suite Reduction (TSR) is a critical optimization challenge in software testing that aims to reduce the number of test cases while maintaining maximum requirement coverage. Traditional algorithms, such as the Rat Swarm Optimizer (RSO), struggle with scalability, especially when dealing with large datasets. Additionally, RSO is unable to solve multiple tasks simultaneously, which leads to an increased time to complete the optimization process across multiple datasets. To resolve this constraint, this paper introduces the Multi-Factorial Rat Swarm Optimizer (MFRSO), which combines Multi-Factorial Optimization (MFO) principles to allow knowledge transfer between tasks, thus increasing optimization efficiency. The performance of MFRSO was compared to that of RSO on five datasets of varied sizes, with results averaging over ten runs. Experimental results show that MFRSO consistently delivered a higher Percentage of Test Suite Reduction (PTSR) while maintaining full requirement coverage, as opposed to RSO, which loses efficiency significantly with larger datasets. Furthermore, MFRSO reduced the optimization time compared to RSO, indicating its scalability and reliability. Future work will investigate adaptive knowledge transfer methods and apply MFRSO to dynamic test suite settings.
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