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Abstract
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Nowadays, a large number of useful algorithms have been proposed for generating covering arrays, which is a branch of combinatorial testing. The main challenge of covering arrays is to produce an array with the minimum number of test samples (efficiency) in a reasonable time (effectiveness) for large systems. Strategies for generating covering arrays are generally divided into two main categories: computational and meta-heuristic. Computational strategies usually have high efficiency but produce weak results in terms of effectiveness, while meta-heuristic strategies have suitable efficiency but weak effectiveness. Among the available strategies, the DPSO strategy has the best efficiency results, but lacks adequate effectiveness, and the GS strategy has suitable effectiveness but lacks efficiency. In general, a strategy that combines efficiency and effectiveness is not evident. In this research, we attempted to generate a suitable test sequence in terms of efficiency and effectiveness by combining the Biogeography-Based Optimization (BBO) algorithm and the Particle Swarm Optimization (PSO) algorithm. Additionally, a simple and effective minimization function was used to increase efficiency. Evaluation results indicate that the proposed solution yields desirable results in terms of efficiency and effectiveness, though we do not claim it to be the best.
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