Particle swarm optimization algorithm for test case automatic generation based on clustering thought

In order to improve the efficiency and quality of software test case automatic generation, a kind of particle swarm optimization was proposed. It had adaptive optimization based on the clustering thought. The algorithm divided the population into two types which were main particle and secondary particle when the algorithm was executed. They used different search strategies so that the algorithm expanded the search scope of particles to speed up the algorithm running. The experimental result shows that the proposed algorithm has more advantages and is more effective than the other contrastive algorithms in the software test case automatic generation.