A novel chaos discrete particle swarm optimization algorithm for test suite reduction

Abstract

Aiming at providing the more measure of coverage with the least number of test cases for software regression testing, this paper presents a novel chaos discrete particle swarm optimization algorithm(CDPSO) for test suite reduction, which combines discrete particle swarm optimization (DPSO) with a chaos searching strategy. In the algorithm, particle swarm is initialized by chaotic series, and the position of the particle is produced by stochastic algorithm. Moreover, it introduces chaos to DPSO, making every particle select a suitable search direction from PSO search mechanism and chaos search mechanism, to avoid PSO getting into local best and appearing premature convergence. Finally, the classic example is used to illustrate the performance of the proposed algorithm. The experimental results indicate that the CDPSO algorithm can achieve higher performance, faster speed than PSO, GE, H and GRE, and has nothing to do with the initial value‥