应用混沌果蝇算法的路径覆盖测试用例优化技术研究
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摘要
提出一种基于混沌果蝇的路径覆盖测试用例生成方法.鉴于果蝇优化算法与遗传算法等常用算法属于同一类型智能算法,且果蝇优化算法具有计算量小,复杂度低,寻优精度高等优点,故将果蝇优化算法运用到软件测试领域内,通过路径覆盖来实现测试数据的自动生成;并且针对果蝇优化算法表现出的易陷入局部最优问题,融入了一种新的混沌策略,对每次迭代过程中最优个体进行了改进,在保留优秀个体的同时,增加种群的多样性,优化全局搜索能力.最后,本文通过两组仿真实验,分别对比了在不同迭代次数下的覆盖率,及覆盖全部目标路径下的评价次数与运行时间,均取得较好的实验效果,验证了本文方法在路径覆盖测试领域内的有效性.
This article proposes a method for generation of Software Testing Data Based on Path Coverage of chaotic fruit fly. Given that the fruit fly optimization algorithm,the genetic algorithm and other common algorithms belong to the same type of intelligence algorithm,besides,the fruit fly optimization algorithm has some characteristics such as small calculated quantity,less complication,and high optimization accuracy,I try to use the fruit fly optimization algorithm in the software testing area for the first time,and to realize testing datum's automatic generation through path coverage; Moreover,for the issue that the fruit fly optimization algorithm is easily trapped in the local optimum,I take in a new chaotic strategy: make improvement to optimal individual in every iterative process,add population diversities and optimize the capability of global search while keeping excellent individual. In the end,through two simulation experiments,I respectively compare coverage under/with different iterations and evaluation times and runtime with all the target paths covered. As both experiments are well performed and good results are obtained,the validity of method in this article is testified in path coverage testing area.
This article proposes a method for generation of Software Testing Data Based on Path Coverage of chaotic fruit fly. Given that the fruit fly optimization algorithm,the genetic algorithm and other common algorithms belong to the same type of intelligence algorithm,besides,the fruit fly optimization algorithm has some characteristics such as small calculated quantity,less complication,and high optimization accuracy,I try to use the fruit fly optimization algorithm in the software testing area for the first time,and to realize testing datum's automatic generation through path coverage; Moreover,for the issue that the fruit fly optimization algorithm is easily trapped in the local optimum,I take in a new chaotic strategy: make improvement to optimal individual in every iterative process,add population diversities and optimize the capability of global search while keeping excellent individual. In the end,through two simulation experiments,I respectively compare coverage under/with different iterations and evaluation times and runtime with all the target paths covered. As both experiments are well performed and good results are obtained,the validity of method in this article is testified in path coverage testing area.
引文
[1]Dong Yue-hua,Dai Yu-qian.Automatic software test data generation based on DPPSO[J].Journal of Chinese Computer Systems,2015,36(9):2015-2020.
[2]Xanthakis S,Ellis C,Skourlas C,et al.Application of genetic algorithms to softw are testing[C].Perry D,Jeffery R,Notkin D,eds.Proceedings of the 5th International Conference on Softw are Engineering and its Applications.Los Alamitos:IEEE,1992:625-636.
[3]Xia Chun-yan,Zhang Yan,Song Li.Evolutionary generation of test data for paths coverage based on node probability[J].Journal of Softw are,2016,27(4):802-813.
[4]Ding Rui,Dong Hong-Bin,Zhang Yan,et al.Fast automatic generation method for softw are testing data based on key-point path[J].Journal of Softw are,2016,27(4):814-827.
[5]Gong Dun-wei,Zhang Yan.Novel evolutionary generation approach to test data for multiple paths coverage[J].Acta Electronica Sinica,2010,38(6):1299-1304.
[6]Zhang Yan,Gong Dun-wei.Evolutionary generation of test data for path coverage based on automatic reduction of search space[J].Acta Electronica Sinica,2012,40(5):1011-1016.
[7]Zhang Yan,Gong Dun-wei.Evolutionary generation of test data for paths coverage based on scarce data capturing[J].Chinese Journal of Computers,2013,36(12):2429-2440.
[8]Pachauri A,Srivasatava G.Towards a parallel approach for test data generation for branch coverage w ith genetic algorithm using the extended path prefix strategy[C].International Conference on Computing for Sustainable Global Development.IEEE,2015:1786-1792.
[9]Peng Ye-ping,Zeng Bi.Software test data generation for multiple paths based on genetic algorithms[J].Applied M echanics&M aterials,2012,263-266:1969-1973.
[10]Jakkrit Kaewyotha,Wararat Songpan.Finding the critical path with loop structure for a basis path testing using genetic algorithm[M].Recent Advances in Information and Communication Technology2015,Springer International Publishing,2015:41-52.
[11]You Feng,Zhao Rui-lian,Lv Shan-shan.Output domain based automatic test case generation[J].Journal of Computer Research and Development,2016(3):541-549.
[12]Ghiduk Ahmed S.Automatic generation of basis test paths using variable length genetic algorithm[J].Information Processing Letters,2014,114(6):304-316.
[13]Zhang Wei-xiang,Wei Bo,Du Hui-sen.Test case prioritization method based on genetic algorithm[J].Journal of Chinese Computer Systems,2015,36(9):1998-2002.
[14]Fan Wen-chao.Fruit fly optimization algorithm-a new evolutionary computation approach[M].Taiw an:Tsang Hai Publishing,2011.
[15]Tang Xian-lun,Zhou Wei,Zhang Heng,et al.Robot soccer defensive strategy based on multi-objective chaotic PSO[J].Journal of System Simulation,2014,26(1):51-61.
[16]Fan Jiu-lun,Zhang Xue-feng.Piecewise logistic chaotic map and its performance analysis[J].Acta Electronica Sinica,2009,37(4):720-725.
[17]Cheng Yu-huei.Evaluation of sine-based chaotic strategy for adapting inertia w eight of particle sw arm optimization[J].Lecture Notes in Engineering&Computer Science,2015,1(1):36-40.
[18]Rezaee Jordehi A.Chaotic bat swarm optimisation(CBSO)[J].Applied Soft Computing,2015,26(C):523-530.
[19]Chuang Li-yeh,Tsai Sheng-wei,Yang Cheng-hong.Improved binary particle sw arm optimization using catfish effect for feature selection[J].Expert Systems w ith Applications,2011,38(10):12699-12707.
[20]Wu Chuan,Gong Dun-wei.Evolutionary generation of Test data for regression testing based on path correlation[J].Chinese Journal of Computers,2015(11):2247-2261.
[21]Zhang Yan.Theories and methods of evolutionary generation of test data for path coverage[D].Beijing:China University of M ining and Technology,2012.
[1]董跃华,戴玉倩.一种改进PSO的软件测试数据自动生成算法[J].小型微型计算机系统,2015,36(9):2015-2020.
[3]夏春艳,张岩,宋丽.基于节点概率的路径覆盖测试数据进化生成[J].软件学报,2016,27(4):802-813.
[4]丁蕊,董红斌,张岩,等.基于关键点路径的快速测试用例自动生成方法[J].软件学报,2016,27(4):814-827.
[5]巩敦卫,张岩.一种新的多路径覆盖测试数据进化生成方法[J].电子学报,2010,38(6):1299-1304.
[6]张岩,巩敦卫.基于搜索空间自动缩减的路径覆盖测试数据进化生成[J].电子学报,2012,40(5):1011-1016.
[7]张岩,巩敦卫.基于稀有数据扑捉的路径覆盖测试数据进化生成方法[J].计算机学报,2013,36(12):2429-2440.
[11]尤枫,赵瑞莲,吕珊珊.基于输出域的测试用例自动生成方法研究[J].计算机研究与发展,2016(3):541-549.
[13]张卫祥,魏波,杜会森.一种基于遗传算法的测试用例优先排序方法[J].小型微型计算机系统,2015,36(9):1998-2002.
[14]潘文超.果蝇最佳化演算法—最新演化式计算技术[M].台湾:沧海书局,2011.
[15]唐贤伦,周维,张衡,等.一种基于多目标混沌PSO的机器人足球防守策略[J].系统仿真学报,2014,26(1):51-61.
[16]范九伦,张雪锋.分段Logistic混沌映射及其性能分析[J].电子学报,2009,37(4):720-725.
[20]吴川,巩敦卫.基于路径相关性的回归测试数据进化生成[J].计算机学报,2015(11):2247-2261.
[21]张岩.路径覆盖测试数据进化生成理论与方法[D].北京:中国矿业大学,2012.
[2]Xanthakis S,Ellis C,Skourlas C,et al.Application of genetic algorithms to softw are testing[C].Perry D,Jeffery R,Notkin D,eds.Proceedings of the 5th International Conference on Softw are Engineering and its Applications.Los Alamitos:IEEE,1992:625-636.
[3]Xia Chun-yan,Zhang Yan,Song Li.Evolutionary generation of test data for paths coverage based on node probability[J].Journal of Softw are,2016,27(4):802-813.
[4]Ding Rui,Dong Hong-Bin,Zhang Yan,et al.Fast automatic generation method for softw are testing data based on key-point path[J].Journal of Softw are,2016,27(4):814-827.
[5]Gong Dun-wei,Zhang Yan.Novel evolutionary generation approach to test data for multiple paths coverage[J].Acta Electronica Sinica,2010,38(6):1299-1304.
[6]Zhang Yan,Gong Dun-wei.Evolutionary generation of test data for path coverage based on automatic reduction of search space[J].Acta Electronica Sinica,2012,40(5):1011-1016.
[7]Zhang Yan,Gong Dun-wei.Evolutionary generation of test data for paths coverage based on scarce data capturing[J].Chinese Journal of Computers,2013,36(12):2429-2440.
[8]Pachauri A,Srivasatava G.Towards a parallel approach for test data generation for branch coverage w ith genetic algorithm using the extended path prefix strategy[C].International Conference on Computing for Sustainable Global Development.IEEE,2015:1786-1792.
[9]Peng Ye-ping,Zeng Bi.Software test data generation for multiple paths based on genetic algorithms[J].Applied M echanics&M aterials,2012,263-266:1969-1973.
[10]Jakkrit Kaewyotha,Wararat Songpan.Finding the critical path with loop structure for a basis path testing using genetic algorithm[M].Recent Advances in Information and Communication Technology2015,Springer International Publishing,2015:41-52.
[11]You Feng,Zhao Rui-lian,Lv Shan-shan.Output domain based automatic test case generation[J].Journal of Computer Research and Development,2016(3):541-549.
[12]Ghiduk Ahmed S.Automatic generation of basis test paths using variable length genetic algorithm[J].Information Processing Letters,2014,114(6):304-316.
[13]Zhang Wei-xiang,Wei Bo,Du Hui-sen.Test case prioritization method based on genetic algorithm[J].Journal of Chinese Computer Systems,2015,36(9):1998-2002.
[14]Fan Wen-chao.Fruit fly optimization algorithm-a new evolutionary computation approach[M].Taiw an:Tsang Hai Publishing,2011.
[15]Tang Xian-lun,Zhou Wei,Zhang Heng,et al.Robot soccer defensive strategy based on multi-objective chaotic PSO[J].Journal of System Simulation,2014,26(1):51-61.
[16]Fan Jiu-lun,Zhang Xue-feng.Piecewise logistic chaotic map and its performance analysis[J].Acta Electronica Sinica,2009,37(4):720-725.
[17]Cheng Yu-huei.Evaluation of sine-based chaotic strategy for adapting inertia w eight of particle sw arm optimization[J].Lecture Notes in Engineering&Computer Science,2015,1(1):36-40.
[18]Rezaee Jordehi A.Chaotic bat swarm optimisation(CBSO)[J].Applied Soft Computing,2015,26(C):523-530.
[19]Chuang Li-yeh,Tsai Sheng-wei,Yang Cheng-hong.Improved binary particle sw arm optimization using catfish effect for feature selection[J].Expert Systems w ith Applications,2011,38(10):12699-12707.
[20]Wu Chuan,Gong Dun-wei.Evolutionary generation of Test data for regression testing based on path correlation[J].Chinese Journal of Computers,2015(11):2247-2261.
[21]Zhang Yan.Theories and methods of evolutionary generation of test data for path coverage[D].Beijing:China University of M ining and Technology,2012.
[1]董跃华,戴玉倩.一种改进PSO的软件测试数据自动生成算法[J].小型微型计算机系统,2015,36(9):2015-2020.
[3]夏春艳,张岩,宋丽.基于节点概率的路径覆盖测试数据进化生成[J].软件学报,2016,27(4):802-813.
[4]丁蕊,董红斌,张岩,等.基于关键点路径的快速测试用例自动生成方法[J].软件学报,2016,27(4):814-827.
[5]巩敦卫,张岩.一种新的多路径覆盖测试数据进化生成方法[J].电子学报,2010,38(6):1299-1304.
[6]张岩,巩敦卫.基于搜索空间自动缩减的路径覆盖测试数据进化生成[J].电子学报,2012,40(5):1011-1016.
[7]张岩,巩敦卫.基于稀有数据扑捉的路径覆盖测试数据进化生成方法[J].计算机学报,2013,36(12):2429-2440.
[11]尤枫,赵瑞莲,吕珊珊.基于输出域的测试用例自动生成方法研究[J].计算机研究与发展,2016(3):541-549.
[13]张卫祥,魏波,杜会森.一种基于遗传算法的测试用例优先排序方法[J].小型微型计算机系统,2015,36(9):1998-2002.
[14]潘文超.果蝇最佳化演算法—最新演化式计算技术[M].台湾:沧海书局,2011.
[15]唐贤伦,周维,张衡,等.一种基于多目标混沌PSO的机器人足球防守策略[J].系统仿真学报,2014,26(1):51-61.
[16]范九伦,张雪锋.分段Logistic混沌映射及其性能分析[J].电子学报,2009,37(4):720-725.
[20]吴川,巩敦卫.基于路径相关性的回归测试数据进化生成[J].计算机学报,2015(11):2247-2261.
[21]张岩.路径覆盖测试数据进化生成理论与方法[D].北京:中国矿业大学,2012.