Computer Engineering and Applications ›› 2018, Vol. 54 ›› Issue (17): 16-23.DOI: 10.3778/j.issn.1002-8331.1804-0123

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Systematic review of test data generation based on intelligent optimization algorithm

XUE Meng, JIANG Shujuan, WANG Rongcun   

  1. Mine Digitization Engineering Research Center of the Ministry of Education, School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China
  • Online:2018-09-01 Published:2018-08-30


薛  猛,姜淑娟,王荣存   

  1. 中国矿业大学 计算机科学与技术学院 矿山数字化教育部工程研究中心,江苏 徐州 221116

Abstract: Software testing is a very effective means of quality assurance and test data generation plays a key role in it. Test data generation based on intelligent optimization algorithm provides an effective solution to the problem of automated test data generation. And genetic algorithm and particle swarm optimization algorithm are the two most frequently used optimization algorithms in these methods based on intelligent optimization algorithm. Firstly, the current research results are summed up and the research status quo is analyzed in particular. Secondly, test data generation tools:AUSTIN and EvoSuite are introduced simply. Finally, the existing problems and future researches are discussed tentatively.

Key words: software testing, test data generation, intelligent optimization algorithm, Genetic Algorithm(GA), Particle Swarm Optimization(PSO)

摘要: 软件测试是一种极为有效的软件质量保证手段。测试数据生成是软件测试的关键。基于智能优化算法的测试数据生成方法为自动化的测试数据生成提供了解决问题的一个有效手段。首先重点总结归纳了在基于智能优化算法的测试数据生成中使用最为频繁的两种算法:遗传算法和粒子群优化算法的研究成果,分析了研究现状,接着简单介绍了基于智能优化算法的测试数据生成工具:AUSTIN和EvoSuite,最后对存在的问题及未来的研究内容进行了尝试性的探讨。

关键词: 软件测试, 测试数据生成, 智能优化算法, 遗传算法, 粒子群优化