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[1]佘嘉纯,刘涛,梅偌玮,等.基于改进的人工鱼群算法的软件测试数据自动生成算法[J].成都信息工程大学学报,2019,(06):595-599.[doi:10.16836/j.cnki.jcuit.2019.06.006]
　SHE Jiachun,LIU Tao,MEI Ruowei,et al.Test Data Automatic Generation based on Modified Artificial Fish Swarm Algorithm[J].Journal of Chengdu University of Information Technology,2019,(06):595-599.[doi:10.16836/j.cnki.jcuit.2019.06.006]

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基于改进的人工鱼群算法的软件测试数据自动生成算法

成都信息工程大学学报[ISSN:1006-6977/CN:61-1281/TN] 卷: 期数: 2019年06期页码: 595-599 栏目: 电子信息科学与技术出版日期: 2019-12-25

Title:: Test Data Automatic Generation based on Modified Artificial Fish Swarm Algorithm

文章编号:: 2096-1618(2019)06-0595-05

作者:: 佘嘉纯¹; 刘涛¹; 梅偌玮¹; 胡诗朋¹; 李静²; 徐自励²; 王国强²; (1.成都信息工程大学电子工程学院,四川成都 610225; 2.中国民航局第二研究所,四川成都 610041)

Author(s):: SHE Jiachun¹; LIU Tao¹; MEI Ruowei¹; HU Shipeng¹; LI Jing²; XU Zili²; WANG Guoqiang²; (1.College of Electronic Engineering, Chengdu University of Information Technology, Chengdu 610225,China; 2.The Second Research Institute of CAAC,Chengdu 610041,China)

关键词:: 人工鱼群算法; 软件测试; 测试数据; 路径导向

Keywords:: artificial fish swarm algorithm; software testing; test data; path-oriented

分类号:: TP311.55

DOI:: 10.16836/j.cnki.jcuit.2019.06.006

文献标志码:: A

摘要:: 针对软件测试领域的数据自动生成问题,提出一种改进人工鱼群算法(AFSA)的方案。在对人工鱼群算法的觅食行为、群聚行为、追尾行为中引入动态步长的观点,让迭代次数和效率得到进一步的优化。用改进的人工鱼群算法解决直角三角形判别程序的测试数据生成问题,结果表明在不同数量的种群下改进的人工鱼群算法的收敛速度快,求解精度高,优于传统的遗传算法(GA)、粒子群优化算法(PSO)等方法。

Abstract:: Aiming at the problem of automatic data generation in software testing field, an improved artificial fish swarm algorithm(AFSA)is proposed. The dynamic step is proposed into the praying, swarming and following behaviors of artificial fish swarm algorithm, which further optimizes the iteration times and efficiency. The improved artificial fish swarm algorithm is used to solve the problem of test data generation of right triangle discriminant program. The results show that the improved AFSA has advantage of fast convergence speed and high accuracy under different populations over the traditional GA and PSO methods.

参考文献/References:

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备注/Memo

收稿日期:2019-09-19基金项目:四川省教育厅科研资助项目(18ZA0111); 四川省科技厅-四川省省院省校科技合作研发项目(2018JZ0030)

更新日期/Last Update: 2019-12-25