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

XUE Meng; JIANG Shu-juan; ZHANG Zheng-guang; QIAN Jun-yan; ZHANG Yan-mei; CAO He-ling

doi:10.3969/j.issn.0372-2112.2017.10.023

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

更新时间：2025-07-16

- A Test Data Generation Method Based on Kalman Filter and Particle Swarm Optimization Algorithm
- Acta Electronica Sinica Vol. 45, Issue 10, Pages: 2473-2483(2017)
- 作者机构：
  
  1. 中国矿业大学计算机科学与技术学院,江苏,徐州,221116
  2. 桂林电子科技大学广西可信软件重点实验室,广西,桂林,541004
  3. 南京大学计算机软件新技术国家重点实验室,江苏,南京,210093
  4. 河南工业大学信息科学与工程学院,河南,郑州,450001
  5. 中国矿业大学计算机科学与技术学院,江苏,徐州,221116
  6. 桂林电子科技大学广西可信软件重点实验室,广西,桂林,541004
  7. 南京大学计算机软件新技术国家重点实验室,江苏,南京,210093
  8. 河南工业大学信息科学与工程学院,河南,郑州,450001
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61502497, No.61562015, No.61673384, No.61602154);China Postdoctoral Science Foundation (No.2015M581887);Research Project of Guangxi Key Laboratory of Trusted Software (No.KX201530);Open Project of State Key Laboratory for Novel Software Technology at Nanjing University (No.KFKT2014B19);Xuzhou Science and Technology planning Project (No.KC15SM051);Key Scientific Research Programs of colleges and universities of Henan Province (No.16A520005)
- DOI：10.3969/j.issn.0372-2112.2017.10.023
  CLC： TP311.5
- Published：2017
- 稿件说明：
移动端阅览
XUE Meng, JIANG Shu-juan, ZHANG Zheng-guang, et al. A Test Data Generation Method Based on Kalman Filter and Particle Swarm Optimization Algorithm[J]. Acta Electronica Sinica, 2017, 45(10): 2473-2483.
DOI：

XUE Meng, JIANG Shu-juan, ZHANG Zheng-guang, et al. A Test Data Generation Method Based on Kalman Filter and Particle Swarm Optimization Algorithm[J]. Acta Electronica Sinica, 2017, 45(10): 2473-2483. DOI： 10.3969/j.issn.0372-2112.2017.10.023.

摘要

为减少进化代数，提高路径覆盖成功率，提出了多邻域Kalman滤波PSO测试数据生成方法.在该方法中将粒子固定划分到不同邻域中，各邻域内指定一个粒子向全局最优粒子学习，其余各粒子向所在邻域中最优粒子学习，而全局最优粒子利用无速度项的简化PSO进化.在此过程中，除全局最优粒子外的各粒子利用Kalman滤波方程更新粒子的位置.实验表明，相较于基本PSO和其他PSO方法，即使是覆盖困难的路径，本文方法也具有进化代数少、路径覆盖成功率高及性能稳定的特点.

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.

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