[1]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,(03):376-381.
 CHEN Ce,ZHAO Chun-xia.Path Test Data Generation Based on Chaos Anneal Particle Swarm Optimization Algorithm[J].Journal of Nanjing University of Science and Technology,2011,(03):376-381.
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基于混沌退火粒子群优化算法的路径测试数据生成
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《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
期数:
2011年03期
页码:
376-381
栏目:
出版日期:
2011-06-30

文章信息/Info

Title:
Path Test Data Generation Based on Chaos Anneal Particle Swarm Optimization Algorithm
作者:
陈策12赵春霞1
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
Author(s):
CHEN Ce12ZHAO Chun-xia1
1.School of Computer Science and Technology,NUST,Nanjing 210094,China;2.Unit 63961 of PLA,Beijing 100012,China
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
Keywords:
particle swarm optimization simulated annealing chaos search prematurity convergence judgment software tests path tests
分类号:
TP311.53
摘要:
为实现指定路径的软件测试数据自动生成,提出了一种基于粒子群优化(PSO)算法的演化测试方法。利用分支函数插装和强制路径执行策略,得到用于优化搜索的路径适应值。通过引入混沌搜索、模拟退火和早熟收敛判断机制,克服了标准PSO算法易陷入局部最优而无法找到测试数据的缺陷。三角形判断程序的测试数据自动生成实验表明:在最大迭代次数Tmax为500时,混沌退火粒子群优化(CAPSO)算法的命中概率为99%,标准PSO的命中概率为95%;在Tmax为2 000时,CAPSO算法的命中概率为100%,标准PSO算法的命中概率为95%左右;继续增大Tmax不能使标准PSO算法的命中概率提高,而CAPSO算法总能摆脱局部极值找到满足要求的测试数据。
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.

参考文献/References:

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备注/Memo

备注/Memo:
作者简介:陈策( 1975 - ) ,男,博士生,主要研究方向: 软件工程、软件测试,E-mail: pyschce@ sina. com; 通讯作者: 赵春霞 ( 1964 - ) ,女,教授,博士生导师,主要研究方向: 人工智能、模式识别,E-mail: zhaochunxia@126. com。
更新日期/Last Update: 2012-06-30