Effective Covering Array Generation Using an Improved Particle Swarm Optimization

In the test case generation process of combinatorial testing, particle swarm optimization (PSO) is widely concerned for its simple implementation and fast convergence rate; however, it often falls into local optimum due to premature convergence. To attack this problem, a novel adaptive value measurement strategy is adopted by weighing the relationship between current test cases and historical test cases. The test case with the minimum average hamming distance is selected as the optimal test case, and the inertial weight linear differential decrease strategy is developed to ensure better inertial weight in different search stages, further to improve the capability of generating smaller covering arrays. In addition, we integrate the simulated annealing strategy into the improved PSO to improve the ability of particles jumping out of the local optimum, and an innovative approach for generating a better covering array is proposed. Experiments on 16 classical random strength covering arrays suggest that our approach outperforms six other techniques in terms of effectiveness.