Test Case Generation Based on Orthogonal Exploration and Particle Swarm Optimization

WANG Ling-sai; JIANG Shu-juan; ZHANG Yan-mei; YU Qiao

doi:10.3969/j.issn.0372-2112.2014.12.002

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Test Case Generation Based on Orthogonal Exploration and Particle Swarm Optimization

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- Test Case Generation Based on Orthogonal Exploration and Particle Swarm Optimization
- Acta Electronica Sinica Vol. 42, Issue 12, Pages: 2345-2351(2014)
- 作者机构：
  
  中国矿业大学计算机科学与技术学院,江苏,徐州,221116
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.60970032);333 High-level Talents Cultivation Project in Jiangsu Province;Blue Project in Jiangsu Province;Supported by Fundamental Research Funds for the Central Universities (No.2013NB17)
- DOI：10.3969/j.issn.0372-2112.2014.12.002
  CLC： TP311
- Published：2014
- 稿件说明：
移动端阅览
WANG Ling-sai, JIANG Shu-juan, ZHANG Yan-mei, et al. Test Case Generation Based on Orthogonal Exploration and Particle Swarm Optimization[J]. Acta Electronica Sinica, 2014, 42(12): 2345-2351.
DOI：

WANG Ling-sai, JIANG Shu-juan, ZHANG Yan-mei, et al. Test Case Generation Based on Orthogonal Exploration and Particle Swarm Optimization[J]. Acta Electronica Sinica, 2014, 42(12): 2345-2351. DOI： 10.3969/j.issn.0372-2112.2014.12.002.

摘要

针对粒子群优化算法易出现早熟收敛的问题

本文提出一种基于正交搜索的粒子群优化测试用例生成方法.首先

利用奇异值分解来预测种群的进化方向

在其正交方向进行搜索

可避免已搜索过的区域

有助于跳出局部最优;然后

对粒子速度项进行改进

使其与正交方向保持一致

保证种群可持续受到正交方向的影响

有利于减少奇异值分解次数

降低时间消耗;最后

对每代最优个体进行局部搜索

以增强算法局部搜索能力.实验证明

本文方法在覆盖率、运行时间、进化代数等指标上均有优势.

Abstract

To solve the problem of premature convergence

this paper presents a method of generating test cases based on orthogonal exploration and particle swarm optimization.First

singular value decomposition is used to estimate the evolution direction and drives the population towards orthogonal direction

so that our method can avoid searching those traversed areas so far and jump out of local optimum.Then

we change the velocity so that it is able to be consistent with the orthogonal direction

and as a result

the population can be affected continually

which can decrease the frequency of singular value decomposition and reduce the time consumption.Finally

the local search is used for the best particle in each generation.The experimental results show that our method has advantages in coverage

running time

and the number of generations.

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