基于相似路径的动态引导测试数据生成策略
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摘要
为了有效提高满足路径覆盖的测试数据质量,提出一种利用相似路径间启发信息的引导测试数据生成策略。首先,对初始测试数据与路径节点间的覆盖情况进行分析,区分出难易覆盖路径;其次,设计了一种路径相似度的计算方法,分析得出路径相关启发信息,并将该启发信息用于遗传算法寻优过程中;然后,构造带有权重影响因子的适应度评价函数,结合保留精英个体思想,设计自适应遗传算子并定向引导个体交叉变异;最后,将该策略应用于多个基准程序和工业程序,并与Ahmed方法、多路径覆盖方法和EGA方法比较。仿真实验结果表明,该策略在运行时间、路径覆盖率和已有测试数据的利用率上均有优势。
In order to effectively improve the quality of the test data that satisfies the path coverage,a guided test data generation strategy based on heuristic information between similar paths is proposed.Firstly,the difficult coverage paths were distinguished by analyzing the coverage between the initial test data and the path nodes.Secondly,apath similarity calculation method was designed to get path-related heuristic information,and the heuristic information was used for genetic algorithm optimization.Thirdly,a fitness evaluation function with the weight impact factor was constructed.In combination with the individual idea of retaining elites,the adaptive genetic operator was design,and the individual crossvariation was guided directionally.Finally,this strategy was applied in multiple benchmarks and industrial processes,and compared with Ahmed method,multipath coverage method and EGA method.The simulation results showed that the strategy had great advantages in running time,path coverage and utilization of existing test data.
In order to effectively improve the quality of the test data that satisfies the path coverage,a guided test data generation strategy based on heuristic information between similar paths is proposed.Firstly,the difficult coverage paths were distinguished by analyzing the coverage between the initial test data and the path nodes.Secondly,apath similarity calculation method was designed to get path-related heuristic information,and the heuristic information was used for genetic algorithm optimization.Thirdly,a fitness evaluation function with the weight impact factor was constructed.In combination with the individual idea of retaining elites,the adaptive genetic operator was design,and the individual crossvariation was guided directionally.Finally,this strategy was applied in multiple benchmarks and industrial processes,and compared with Ahmed method,multipath coverage method and EGA method.The simulation results showed that the strategy had great advantages in running time,path coverage and utilization of existing test data.
引文
[1]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.
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[9]高雪笛,周丽娟,张树东,等.基于改进遗传算法的测试数据自动生成的研究[J].计算机科学,2017,44(3):209-214.
[10]张娜,胡国亨,包晓安,等.基于多种群进化算法的测试用例优先级排序研究[J].浙江理工大学学报,2018,39(2):218-223.
[11]吴川,巩敦卫,姚香娟.基于分支覆盖的回归测试路径选择[J].软件学报,2016,27(4):839-854.
[12]史娇娇,姜淑娟.基于遗传算法的动态可变参数的测试数据自动生成工具[J].计算机科学,2012,39(5):124-127.
[13]张娜,姚澜,包晓安,等.多目标优化的测试用例优先级在线调整策略[J].软件学报,2015,26(10):2451-2464.
[14]曹凯,陈国虎,江桦,等.自适应引导进化遗传算法[J].电子与信息学报,2014,36(8):1884-1890.
[15]谢晓园,徐宝文,史亮,等.面向路径覆盖的演化测试用例生成技术[J].软件学报,2009,20(12):3117-3136.
[16]Alshraideh M A,Mahafzah B A,Salman H S E,et al.Using genetic algorithm as test date generator for stored PL/SQL program units[J].Journal of Software Engineering&Applications,2015,6(2):65-73
[17]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,2015,38(11):2247-2261.
[2]单锦辉,姜瑛,孙萍.软件测试研究进展[J].北京大学学报(自然科学版),2005,41(1):134-145.
[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]Ahmed M A,Hermadi I.GA-Based multiple paths test data generator[J].Computer&Operations Research,2008,35(10):3107-3127.
[5]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.
[6]Pachauri A, Srivastava G. Automated test data generation for branch testing using genetic algorithm:An improved approach using branch ordering,memory and elitism[J].Journal of Systems&Software,2013,86(5):1191-1208.
[7]Gong D W,Zhang W Q,Zhang Y.Evolutionary generation of test data for multiple paths coverage[J].Chinese Journal of Electronics,2011,19(2):233?237.
[8]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.
[9]高雪笛,周丽娟,张树东,等.基于改进遗传算法的测试数据自动生成的研究[J].计算机科学,2017,44(3):209-214.
[10]张娜,胡国亨,包晓安,等.基于多种群进化算法的测试用例优先级排序研究[J].浙江理工大学学报,2018,39(2):218-223.
[11]吴川,巩敦卫,姚香娟.基于分支覆盖的回归测试路径选择[J].软件学报,2016,27(4):839-854.
[12]史娇娇,姜淑娟.基于遗传算法的动态可变参数的测试数据自动生成工具[J].计算机科学,2012,39(5):124-127.
[13]张娜,姚澜,包晓安,等.多目标优化的测试用例优先级在线调整策略[J].软件学报,2015,26(10):2451-2464.
[14]曹凯,陈国虎,江桦,等.自适应引导进化遗传算法[J].电子与信息学报,2014,36(8):1884-1890.
[15]谢晓园,徐宝文,史亮,等.面向路径覆盖的演化测试用例生成技术[J].软件学报,2009,20(12):3117-3136.
[16]Alshraideh M A,Mahafzah B A,Salman H S E,et al.Using genetic algorithm as test date generator for stored PL/SQL program units[J].Journal of Software Engineering&Applications,2015,6(2):65-73
[17]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,2015,38(11):2247-2261.
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