Research of Automatic Test Case Generation Algorithm Based on Improved Particle Swarm Optimization

摘要

The software testing is an important way to find bugs, and guarantee the quality and reliability of software. Automatic software case generation can effectively improve test efficiency, reduce test time and cost of development, so it has been widely concerned. Aiming at premature convergence and local optimum problems of automatic software case generation based on particle swarm optimization algorithm, an automatic test case generation algorithm based on improved position and particle swarm optimization is proposed. The proposed algorithm can effectively solve the premature convergence problem by dynamically adjusting the inertia factor and Morlet variation to change the position of particles. Meanwhile, neighbor position information is used to solve the locally optimum problem. Simulation demonstrates that compared with genetic algorithm, artificial immune algorithm and standard particle swarm optimization algorithm, the proposed algorithm is the best in the term of iterations and overhead time.