Implementation of Sand Cat Swarm Optimization for Uniform T-Way Test Suite Generation

Abstract

T-way combinatorial testing is an essential approach for optimizing test suite generation by systematically covering parameter interactions while minimizing test cases. Various metaheuristic strategies have been introduced to improve test suite generation, with an increasing focus on balancing exploration and exploitation for efficient test selection. This study investigates the Sand Cat Swarm Optimization (SCSO) algorithm as a metaheuristic strategy for t-way test suite generation. Inspired by the hunting behavior of sand cats, SCSO dynamically adjusts sensitivity factors to improve test suite generation efficiency. To evaluate SCSO’s performance, 30 benchmark experiments were conducted across four groups of t-way configurations with t varying from 2 to 6 and v ranging from 2 to 10. Each configuration was executed five times, and the smallest test suite size was selected for analysis. Experimental results demonstrate that SCSO outperforms 15.79% of competing strategies, achieves comparable performance in 42.11% of cases, and is outperformed in 42.11% of benchmark comparisons. These findings highlight SCSO’s capability to generate competitive test suites, particularly in t-way interaction testing. The statistical evaluations, including Wilcoxon Rank and Friedman Mean Rank tests, further validate SCSO’s performance in comparison to other metaheuristic approaches. Although SCSO effectively reduces test suite size while maintaining interaction coverage, further enhancements are necessary to improve its adaptability and computational efficiency across diverse configurations. Future work should focus on refining SCSO’s exploration mechanisms to optimize search efficiency and extend its applicability in combinatorial test generation.