南京理工大学学报（自然科学版）2011，Vol.35Issue(3)：376-381,6.
基于混沌退火粒子群优化算法的路径测试数据生成
Path Test Data Generation Based on Chaos Anneal Particle Swarm Optimization Algorithm
陈策 1赵春霞2
作者信息
- 1. 南京理工大学,计算机科学与技术学院,江苏南京210094
- 2. 63961部队,北京100012
- 折叠
摘要
Abstract
A kind of evolutionary test method based on the particle swarm optimization (PSO)algorithm is proposed for the automatic generation of appointed path software test data.Path adaptive value for optimization searching is calculated using code insertion of branch functions and control execution strategy of program path.The shortcoming that the standard PSO algorithm is easy to fall into local optima and can't find the test data is overcome by introducing chaos search, simulated annealing and prematurity convergence judgment mechanism.The experiments of the automatic generation of test data on a triangle judgment program show:when the biggest iterative time Tmax is 500, the hit probability of chaos anneal particle swarm optimization(CAPSO) algorithm and standard PSO algorithm is 99％ and 95％; when Tmax is 2000, the hit probability of CAPSO algorithm and standard PSO algorithm is 100％ and 95％ ;the increase of Tmax can't improve the hit probability of the standard PSO algorithm, but the CAPSO algorithm can shake off the local extremum and find the test data satisfying the request.关键词
粒子群优化/模拟退火/混沌搜索/早熟收敛判断/软件测试/路径测试Key words
particle swarm optimization/ simulated annealing/ chaos search/ prematurity convergence judgment /software tests /path tests分类
信息技术与安全科学引用本文复制引用
陈策,赵春霞..基于混沌退火粒子群优化算法的路径测试数据生成[J].南京理工大学学报（自然科学版）,2011,35(3):376-381,6.
