Optimizing Variable-Strength Combinatorial Test Suite Generation via Fuzzy Adaptive Sine Cosine Algorithm (FASCA)

Combinatorial test suite generation is a critical aspect of software testing, particularly for systems with variable-strength interactions. Traditional optimization algorithms often struggle to efficiently generate minimal test suites while maintaining coverage. This research introduces the Fuzzy Adaptive Sine Cosine Algorithm (FASCA), a variant of the Sine Cosine Algorithm (SCA), tailored specifically for optimizing variable-strength combinatorial test suite generation. FASCA incorporates fuzzy adaptation mechanisms to dynamically balance exploration and exploitation, addressing the limitations of standard SCA in handling diverse interaction strengths. Experimental evaluations demonstrate that FASCA significantly reduces test suite size and execution time compared to the original SCA, offering a more efficient solution for combinatorial test optimization. These results highlight FASCA's potential as a robust tool for enhancing the efficiency of software testing processes.