电子学报2017，Vol.45Issue(10)：2473-2483,11.DOI:10.3969/j.issn.0372-2112.2017.10.023
一种基于Kalman滤波和粒子群优化的测试数据生成方法
A Test Data Generation Method Based on Kalman Filter and Particle Swarm Optimization Algorithm
摘要
Abstract
A test data generation method named multi-neighborhood Kalman filter PSO (MNKFPSO) was proposed to reduce the evolution number and to improve the success rate of path coverage.Particles except the global best one update themselves' positions using Kalman filter.One of them is allotted to a fixed neighborhood.A designated particle learns from the global best particle,others learn from the best in one neighborhood.And the global best particle's position changes by a simple PSO which discards the particle velocity.The experimental results show that it can generate test data covering the specified path in the less evolutionary using MNKFPSO and has high success rate of path coverage even though the paths difficult to cover.The algorithm also exhibits a stable performance.关键词
测试数据生成/粒子群优化/Kalman滤波/邻域拓扑Key words
test data generation/particle swarm optimization (PSO)/Kalman filter/neighborhood topology分类
信息技术与安全科学引用本文复制引用
薛猛,姜淑娟,张争光,钱俊彦,张艳梅,曹鹤玲..一种基于Kalman滤波和粒子群优化的测试数据生成方法[J].电子学报,2017,45(10):2473-2483,11.基金项目
国家自然科学基金(No.61502497,No.61562015,No.61673384,No.61602154) （No.61502497,No.61562015,No.61673384,No.61602154）
中国博士后科学基金(No.2015M581887) （No.2015M581887）
广西可信软件重点实验室研究课题(No.KX201530) （No.KX201530）
南京大学计算机软件新技术国家重点实验室开放课题(No.KFKT2014B19) （No.KFKT2014B19）
徐州市科技计划项目(No.KC15SM051) （No.KC15SM051）
河南省高等学校重点科研项目计划资助(No.16A520005) （No.16A520005）
