|本期目录/Table of Contents|

[1]李晨晨,丁佐华.基于IRAPSO的组合测试用例生成方法[J].浙江理工大学学报,2019,41-42(自科一):72-78.
 LI Chenchen,DING Zuohua.Test case generation method based on improved reduced  adaptive particle swarm optimization[J].Journal of Zhejiang Sci-Tech University,2019,41-42(自科一):72-78.
点击复制

基于IRAPSO的组合测试用例生成方法()
分享到:

浙江理工大学学报[ISSN:1673-3851/CN:33-1338/TS]

卷:
第41-42卷
期数:
2019年自科一期
页码:
72-78
栏目:
出版日期:
2018-12-15

文章信息/Info

Title:
Test case generation method based on improved reduced  adaptive particle swarm optimization
文章编号:
1673-3851 (2019) 01-0072-07
作者:
李晨晨丁佐华
浙江理工大学信息学院,杭州 310018
Author(s):
LI Chenchen DING Zuohua
School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China
关键词:
组合测试覆盖表约简粒子群算法进化方程约简自适应算法
分类号:
TP311
文献标志码:
A
摘要:
最小覆盖表的生成是组合测试研究领域的一个关键问题,虽然粒子群优化算法是生成最小覆盖表的方法之一,但该算法存在易陷入局部最优和搜索精度低等问题。针对该问题提出了一种改进的约简自适应粒子群算法。该方法首先对粒子群优化算法的进化方程进行约简,消去其速度项,得到约简的粒子群进化方程;然后提出了惯性权重的自适应调整策略并且在适应值策略中引入汉明距,以提高该算法生成测试用例的覆盖率。与已有算法的比较结果表明,该算法在克服粒子群优化算法易陷入局部最优等问题的同时能够在较短的时间内生成规模更小的覆盖表。

参考文献/References:

[1] Nie C, Leung H. A survey of combinatorial testing[J]. Acm Computing Surveys, 2011, 43(2):1-29.
[2] Kuhn D R, Reilly M J. An investigation of the applicability of design of experiments to software testing[C]// Proceedings of the 27th Software Engineering. Greenbelt:IEEE Software Engineering Workshop,2002:91-95.
[3] Cohen M B, Gibbons P B, Mugridge W B, et al. Constructing test suites for interaction testing[C]// Proceedings of the 25th International Conference on Software Engineering, Portland. IEEE Computer Society,2003:38-48.[4] TorresJimenez J, RodriguezTello E. New bounds for binary covering arrays using simulated annealing[J]. Information Sciences, 2012, 185(1):137-152.
[5] Shiba T, Tsuchiya T, Kikuno T. Using artificial life techniques to generate test cases for combinatorial testing[C]// Computer Software and Applications Conference. Proceedings of 25th International, Hong Kong. IEEE Computer Society,2004:72-77.
[6] Chen X, Gu Q, Li A, et al. Variable strength interaction testing with an ant colony system approach[C]// Proceedings of Software Engineering Conference, Penang. IEEE Computer Society, 2009:160-167.
[7] Chen X, Gu Q, Zhang X, et al. Building prioritized pairwise interaction test suites with ant colony optimization[C]// Proceedings of 9th International Conference on Quality Software, Jejudo. IEEE Computer Society,2009:347-352.
[8] Mccaffrey J D. An empirical study of pairwise test set generation using a genetic algorithm[C]// Proceedings of Seventh International Conference on Information Technology, Las Vegas. IEEE Computer Society,2010:992-997.
[9] Flores P, Cheon Y. PWiseGen: Generating test cases for pairwise testing using genetic algorithms[C]// Proceedings of IEEE International Conference on Computer Science and Automation Engineering, Shanghai. Institute of Electrical and Electronics Engineers, 2011:747-752.

备注/Memo

备注/Memo:
收稿日期: 2018-09-08
网络出版日期: 2018-11-01
基金项目: 国家自然科学基金项目(61751210,61572441)
作者简介: 李晨晨(1992-),女,山东泰安人,硕士研究生,主要从事软件测试方面的研究
通信作者: 丁佐华,E-mail:zouhuading@hotmail.com
更新日期/Last Update: 2019-03-13