Generating covering arrays using ant colony optimization: exploration and mining

被引：0
|
作者：
Zeng M.-F. [1 ]
Chen S.-Y. [1 ]
Zhang W.-Q. [1 ]
Nie C.-H. [1 ]
机构：
[1] State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing
来源：
Nie, Chang-Hai (changhainie@nju.edu.cn) | 2016年 / Chinese Academy of Sciences卷 / 27期
基金：
中国国家自然科学基金; 高等学校博士学科点专项科研基金;
关键词：
Ant colony optimization; Combinatorial testing; Covering array; Evolutionary algorithm; Parallel computing; Software testing;
D O I：
10.13328/j.cnki.jos.004974
中图分类号：
学科分类号：
摘要：
Generation of covering arrays, which has been solved by many mathematical methods and greedy algorithms as well as search based algorithms, is one of significant problems in combinatorial testing. As an effective evolutionary search algorithm for solving combinatorial optimization problems, ant colony optimization has also been used to generate covering arrays. Existing research shows ant colony optimization suitable for generating general covering arrays, variable strength covering arrays and the prioritization of covering arrays. Unfortunately, compared with other methods, ant colony optimization doesn't have significant advantages. To further explore and mine the potential of ant colony optimization in generating covering arrays, this paper focuses on four levels of improvement: 1) the integration of ant colony variants; 2) parameter tuning; 3) the adjustment of solution structure and the improvement of evolutionary strategy; 4) using parallel computing to save executing time. The experimental results show that ant colony optimization is much more effective in generating covering arrays after the improvements. © Copyright 2016, Institute of Software, the Chinese Academy of Sciences. All rights reserved.
引用
收藏
页码：855 / 878
页数：23
相关论文
共 22 条
  • [1] Nie C.H., Concepts and Methods of Software Testing, pp. 1-22, (2013)
  • [2] Nie C.H., Combinatorial Testing, pp. 1-119, (2015)
  • [3] McCaffrey J.D., Generation of pairwise test sets using a genetic algorithm, Proc. of the Int'l Conf. on Computer Software and Applications (COMPSAC), pp. 626-631, (2009)
  • [4] Nie C.H., Leung H., A survey of combinatorial testing, ACM Computing Surveys (CSUR), 43, 2, (2011)
  • [5] Yu L., Tai K.C., In-Parameter-Order: A test generation strategy for pairwise testing, Proc. of the High-Assurance Systems Engineering Symp., pp. 254-261, (1998)
  • [6] Bryce R.C., Colbourn C.J., The density algorithm for pairwise interaction testing, SoftwareTesting, Verification and Reliability, 17, 3, pp. 159-182, (2007)
  • [7] Dorigo M., Maniezzo V., Colorni A., Ant system: Optimization by a colony of cooperating agents, IEEE Trans. on Systems, Man, and Cybernetics (Part B Cybernetics), 26, 1, pp. 29-41, (1996)
  • [8] Dorigo M., Stutzle T., Ant Colony Optimization, pp. 1-151, (2004)
  • [9] Cohen D.M., Dalal S.R., Fredman M.L., Patton G.C., The AETG system: An approach to testing based on combinatorial design, IEEE Trans. on Software Engineering (TSE), 23, 7, pp. 437-444, (1997)
  • [10] Shiba T., Tsuchiya T., Kikuno T., Using artificial life techniques to generate test cases for combinatorial testing, Proc. of the Computer Software and Applications Conf., pp. 72-77, (2004)
123