Software test cases generation based on improved particle swarm optimization

摘要

The analysis of test case generation based on particle swarm algorithm introduced the group self-activity feedback (SAF) operator and Gauss mutation (G) changing inertia weight to improve the performance of particle swarm optimization (PSO). Using the improved algorithm in software test case, experiments show that the introduction of a single path fitness function structure and multi-path fitness calculation of parallel thinking are superior to the iteration time in single path test than standard PSO, and more efficient in multi-path test case generation.