Review on the application of intelligent optimization in software testing

被引：0
|
作者：
Yao X.-J. [1 ,2 ]
Tian T. [3 ]
Dang X.-Y. [4 ]
Sun B.-C. [1 ,5 ]
Gong D.-W. [5 ]
机构：
[1] School of Mathematics, China University of Mining and Technology, Xuzhou
[2] State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing
[3] School of Computer Science and Technology, Shandong Jianzhu University, Jinan
[4] School of Information Engineering (School of Big Data), Xuzhou University of Technology, Xuzhou
[5] School of Information and Control Engineering, China University of Mining and Technology, Xuzhou
来源：
Kongzhi yu Juece/Control and Decision | 2022年 / 37卷 / 02期
关键词：
Evolutionary testing; Intelligent optimization; Optimization algorithm; Software testing; Test data generation;
D O I：
10.13195/j.kzyjc.2021.1361
中图分类号：
学科分类号：
摘要：
Software testing is a critical and time-consuming process during software development, whose key is to generate test data that meet specific criteria. With the increasing complexity of software, software testing is becoming more and more difficult. Recent years, it is a hot topic in software engineering using intelligent optimization, such as genetic algorithms, to test complex software. This paper mainly summarizes the application of intelligent optimization in software testing. Firstly, the basic principles and methods of software testing are introduced. Then, the research progress of intelligent optimization in different testing fields is introduced. Next, the research progress of software testing based on different intelligent optimization methods is analyzed. Finally, the challenges and prospects in this field are given. Copyright ©2022 Control and Decision.
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页码：257 / 266
页数：9
相关论文
共 90 条
  • [1] Li D, Gong Y Z., A summary about software testing methods, Journal of Armored Force Engineering Institute, 17, 2, pp. 9-13, (2003)
  • [2] Michael C C, McGraw G, Schatz M A., Generating software test data by evolution, IEEE Transactions on Software Engineering, 27, 12, pp. 1085-1110, (2001)
  • [3] Harman M, Jia Y, Zhang Y Y., Achievements, open problems and challenges for search based software testing, IEEE 8th International Conference on Software Testing, Verification and Validation, (2015)
  • [4] Harman M, McMinn P., A theoretical and empirical study of search-based testing: Local, global, and hybrid search, IEEE Transactions on Software Engineering, 36, 2, pp. 226-247, (2010)
  • [5] Bertolino A., Software testing research: Achievements, challenges, dreams, Future of Software Engineering, pp. 85-103, (2007)
  • [6] Cai K Y, Dong Z, Liu K., On several issues in software reliability testing, Chinese Journal of Engineering Mathematics, 25, 6, pp. 967-978, (2008)
  • [7] Du W J, Wang Z J, Gao F., Basic course of software testing, pp. 1-12, (2016)
  • [8] Xu R Z., Software reliability engineering, pp. 75-108, (2007)
  • [9] Chen T Y, Kuo F C, Merkel R G, Et al., Adaptive random testing: The ART of test case diversity, Journal of Systems and Software, 83, 1, pp. 60-66, (2010)
  • [10] Zhang D P, Nie C H, Xu B W., Optimal allocation of test case considering testing-resource in partition testing, Journal of Nanjing University: Natural Sciences, 41, 5, pp. 553-561, (2005)
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