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.
引用
收藏
页码：257 / 266
页数：9
相关论文
共 90 条
  • [31] Nishtha J, Bharti S, Shweta R., Systematic literature review on search based mutation testing, E-Informatica Software Engineering Journal, 11, 1, pp. 59-76, (2017)
  • [32] Wang B, Xiong Y F, Shi Y, Et al., Faster mutation analysis via equivalence modulo states, Proceedings of the 26th ACM SIGSOFT International Symposium on Software Testing and Analysis, pp. 295-306, (2017)
  • [33] Silva R A, Senger de Souza S D R, Lopes de Souza P S., A systematic review on search based mutation testing, Information and Software Technology, 81, pp. 19-35, (2017)
  • [34] Delgado-Perez P, Medina-Bulo I., Search-based mutant selection for efficient test suite improvement: Evaluation and results, Information and Software Technology, 104, pp. 130-143, (2018)
  • [35] Zhang G J, Gong D W, Yao X J., Test case generation based on mutation analysis and set evolution, Chinese Journal of Computers, 38, 11, pp. 2318-2331, (2015)
  • [36] Souza F C M, Papadakis M, Le Traon Y, Et al., Strong mutation-based test data generation using hill climbing, Proceedings of the 9th International Workshop on Search-Based Software Testing, pp. 45-54, (2016)
  • [37] Ayari K, Bouktif S, Antoniol G., Automatic mutation test input data generation via ant colony, Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, pp. 1074-1081, (2007)
  • [38] Letko Z., Sophisticated testing of concurrent programs, The 2nd International Symposium on Search Based Software Engineering, pp. 36-39, (2010)
  • [39] Bhattacharya N, El-Mahi O, Duclos E, Et al., Optimizing threads schedule alignments to expose the interference bug pattern, Search Based Software Engineering
  • [40] Qi X F, Zhou H Y., A splitting strategy for testing concurrent programs, IEEE 26th International Conference on Software Analysis, Evolution and Reengineering, pp. 388-398, (2019)
123456789