  Search-based software engineering that involves the deployment of
meta-heuristics in applicable software processes has been gaining wide
attention. Recently, researchers have been advocating the adoption of
meta-heuristic algorithms for t-way testing strategies (where t points the
interaction strength among parameters). Although helpful, no single
meta-heuristic based t-way strategy can claim dominance over its counterparts.
For this reason, the hybridization of meta-heuristic algorithms can help to
ascertain the search capabilities of each by compensating for the limitations
of one algorithm with the strength of others. Consequently, a new
meta-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 to
adopt Hybrid Artificial Bee Colony (HABC) algorithm with Hamming distance as
its core method for generating a final test set and the first to adopt the
Hamming distance as the final selection criterion for enhancing the exploration
of new solutions. The experimental results demonstrate that HABCSm provides
superior competitive performance over its counterparts. Therefore, this finding
contributes to the field of software testing by minimizing the number of test
cases required for test execution.
