A Test Data Generation Method Based on Kalman Filter and Particle Swarm Optimization Algorithm

被引：0

作者：

Xue M. ^{[1
]}

Jiang S.-J. ^{[1
,2
]}

Zhang Z.-G. ^{[1
]}

Qian J.-Y. ^{[2
]}

Zhang Y.-M. ^{[1
,3
]}

Cao H.-L. ^{[4
]}

机构：

[1] School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, Jiangsu

[2] Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin, 541004, Guangxi

[3] State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210093, Jiangsu

[4] College of Information Science and Engineering, Henan University of Technology, Zhengzhou, 450001, Henan

来源：

Jiang, Shu-Juan (shjjiang@cumt.edu.cn) | 1600年 / Chinese Institute of Electronics卷 / 45期

关键词：

Kalman filter; Neighborhood topology; Particle swarm optimization (PSO); Test data generation;

D O I：

10.3969/j.issn.0372-2112.2017.10.023

中图分类号：

学科分类号：

摘要：

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. © 2017, Chinese Institute of Electronics. All right reserved.

引用

页码：2473 / 2483

页数：10

共 37 条

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