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Search-based software engineering that involves the deployment ofmeta-heuristics in applicable software processes has been gaining wideattention. Recently, researchers have been advocating the adoption ofmeta-heuristic algorithms for t-way testing strategies (where t points theinteraction strength among parameters). Although helpful, no singlemeta-heuristic based t-way strategy can claim dominance over its counterparts.For this reason, the hybridization of meta-heuristic algorithms can help toascertain the search capabilities of each by compensating for the limitationsof one algorithm with the strength of others. Consequently, a newmeta-heuristic based t-way strategy called Hybrid Artificial Bee Colony(HABCSm) strategy, based on merging the advantages of the Artificial Bee Colony(ABC) algorithm with the advantages of a Particle Swarm Optimization (PSO)algorithm is proposed in this paper. HABCSm is the first t-way strategy toadopt Hybrid Artificial Bee Colony (HABC) algorithm with Hamming distance asits core method for generating a final test set and the first to adopt theHamming distance as the final selection criterion for enhancing the explorationof new solutions. The experimental results demonstrate that HABCSm providessuperior competitive performance over its counterparts. Therefore, this findingcontributes to the field of software testing by minimizing the number of testcases required for test execution.

HABCSm: A Hamming Based t-way Strategy Based on Hybrid Artificial Bee Colony for Variable Strength Test Sets Generation