Integration of Grey Wolf Optimizer Algorithm with Combinatorial Testing for Test Suite Generation

Combinatorial Testing (CT) is a software testing technique designed to detect defects in complex systems by efficiently covering diverse combinations of input parameters within given time and resource constraints. A common strategy in CT is t-way testing, which ensures that all possible interactions among any t parameters are tested at least once. The Grey Wolf Optimization Algorithm (GWOA) is a nature-inspired metaheuristic that has been successfully applied to various optimization problems. In this study, we introduce the Combinatorial Grey Wolf Optimization Algorithm (CGWOA), which integrates GWOA with CT to enhance test suite generation. Effectiveness of CGWOA is evaluated through experiments on a real-world software system, where it is found that the number of test cases was reduced by 98%, from 3000 to 40, while still ensuring complete 2-way interaction coverage. Experimental results demonstrate that CGWOA consistently produces smaller test suites compared to pure computation methods such as Jenny, IPOG, IPOG-D and TConfig, as CGWOA consistently outperformed, especially in handling both lower and higher interaction strengths. In scenarios with binary parameters, CGWOA delivered the smallest test suites, while in more complex configurations, even in MCA settings, it showed impressive scalability, outperforming the other algorithms. Statistical analysis using the Wilcoxon signed-rank test revealed that the proposed approach significantly outperforms existing methods, with all p-values less than 0.02 after applying the Holm correction. The experimental results demonstrate that the proposed CGWOA approach advances software testing by efficiently minimizing the number of test cases required to achieve complete test coverage.