Evolutionary Generation of Test Data for Paths Coverage Based on Node Probability
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National Natural Science Foundation of China (61573362); Science-Research Project for the Doctoral Foundation of Mudanjiang Normal University (MNUB201414); Science-Research Project of Mudanjiang Normal University (QN201601, QY2014001)

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    Abstract:

    Path coverage testing is one of the most important software testing methods. This paper presents a process of using genetic algorithms to generate path coverage test data. When an individual traverses the node that might be contained in the unteachable paths(which are determined based on the correlation of conditional statements), the higher the probability the node exists in the unreachable paths, the higher degree of traversing the individual has; and, the individual with higher degree of traversing should be protected. The fitness function of genetic algorithms is designed based on the individual traversing degree, so the efficiency of generating test data is improved. The proposed method is applied to benchmark and industrial programs, and is compared with other methods. The experimental results show that the proposed method is efficient in generating test data for path coverage.

    Reference
    [1] Lenz AR, Pozo A, Vergilio SR. Linking software testing results with a machine learning approach. Engineering Applications of Artificial Intelligence, 2013,26:1631-1640.[doi:10.1016/j.engappai.2013.01.008]
    [2] Zhang Y, Gong DW. Evolutionary generation of test data for paths coverage based on scarce data capturing. Chinese Journal of Computers, 2013,36(12):2429-2440(in Chinese with English abstract).
    [3] Suresh Y, Rath SK. A genetic algorithm based approach for test data generation in basis path testing. The Int'l Journal of Soft Computing and Software Engineering, 2013,3(3):326-332.[doi:10.7321/jscse.v3.n3.49]
    [4] Hedley D, Hennell MA. The causes and effects of infeasible paths in computer programs. In:Proc. of the 8th Int'l Conf. on Software Engineering. Los Alamitos:IEEE Computer Society Press, 1985. 259-266.
    [5] Zhang J, Wang XX. A constraint solver and its application to path feasibility analysis. Int'l Journal of Software Engineering and Knowledge Engineering, 2001,11(2):139-156.[doi:10.1142/S0218194001000487]
    [6] Forgacs I, Bertolino A. Feasible test path selection by principal slicing. In:Proc. of the 6th European Software Engineering Conf. on Held Jointly with the 5th ACM Symp. on the Foundations of Software Engineering. 1997. 378-394.[doi:10.1007/3-540-63531-9_26]
    [7] Gupta R, Gopinath P. Correlation analysis techniques for refining execution time estimates of real-time applications. In:Proc. of the 11th IEEE Workshop on Real-Time Operating Systems and Software. 1994. 54-58.[doi:10.1109/RTOSS.1994.292561]
    [8] Zhuang XT, Zhang T, Pande S. Using branch correlation to identify infeasible paths for anomaly detection. In:Proc. of the 39th Annual IEEE/ACM Int'l Symp. on Microarchitecture. 2006. 113-122.[doi:10.1109/MICRO.2006.48]
    [9] Santone A, Vaglini G. Formula-Based abstractions and symbolic execution for model checking programs. Microprocessors and Microsystems, 2004,28:69-76.[doi:10.1016/S0141-9331(03)00127-3]
    [10] Pourvatan B, Sirjani M, Hojjat H, Arbab F. Automated analysis of reo circuits using symbolic execution. Electronic Notes in Theoretical Computer Science, 2009,255:137-158.[doi:10.1016/j.entcs.2009.10.029]
    [11] Chen T, Mitra T, Roychoudhury A, Suhendra V. Exploiting branch constraints without exhaustive path enumeration. In:Proc. of the 5th Int'l Workshop on Worst-Case Execution Time Analysis. 2005. 46-49. https://www.researchgate.net/publication/30815525_Exploiting_Branch_Constraints_without_Exhaustive_Path_Enumeration
    [12] Ngo MN, Tan HBK. Heuristics-Based infeasible path detection for dynamic test data generation. Information and Software Technology, 2008,50(7-8):641-655.[doi:10.1016/j.infsof.2007.06.006]
    [13] Delahaye M, Botella B, Gotlieb A. Infeasible path generalization in dynamic symbolic execution. Information and Software Technology, 2015,58:403-418.[doi:10.1016/j.infsof.2014.07.012]
    [14] Yao XJ. Theory of evolutionary generation of test data for complex software and applications[Ph.D. Thesis]. Beijing:China University of Mining and Technology, 2011(in Chinese with English abstract).
    [15] Ahmed MA, Hermadi I. GA-Based multiple paths test data generator. Computers and Operations Research, 2008,35(10):3107-3124.[doi:10.1016/j.cor.2007.01.012]
    [16] Sofokleous AA, Andreou AS. Automatic, evolutionary test data generation for dynamic software testing. The Journal of System and Software, 2008,81(11):1883-1898.[doi:10.1016/j.jss.2007.12.809]
    [17] Malhotra R, Garg M. An adequacy based test data generation technique using genetic algorithms. Journal of Information Processing Systems, 2011,7(2):363-384.[doi:10.3745/JIPS.2011.7.2.363]
    [18] Irfan S, Ranjan P. A concept of out degree in CFG for optimal test data using genetic algorithm. In:Proc. of the 1st Int'l Conf. on Recent Advances in Information Technology. 2012. 436-441.[doi:10.1109/RAIT.2012.6194634]
    [19] Xie XY, Xu BW, Shi L, Nie CH. Genetic test case generation for path-oriented testing. Ruan Jian Xue Bao/Journal of Software, 2009,20(12):3117-3136(in Chinese with English abstract). http://www.jos.org.cn/1000-9825/580.htm[doi:10.3724/SP.J.1001. 2009.00580]
    [20] Zhang Y. Theories and methods of evolutionary generation of test data for path coverage[Ph.D. Thesis]. Beijing:China University of Mining and Technology, 2012(in Chinese with English abstract).
    [21] McMinn P. Evolutionary search for test data in the presence of state behaviour[Ph.D. Thesis]. University of Sheffield, 2005.
    附中文参考文献:
    [2] 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.计算机学报,2013,36(12):2429-2440.
    [14] 姚香娟.复杂软件测试数据进化生成理论及应用[博士学位论文].北京:中国矿业大学,2011.34-53.
    [19] 谢晓园,徐宝文,史亮,聂长海.面向路径覆盖的演化测试用例生成技术.软件学报,2009,20(12):3117-3136. http://www.jos.org. cn/1000-9825/580.htm[doi:10.3724/SP.J.1001.2009.00580]
    [20] 张岩.路径覆盖测试数据进化生成理论与方法[博士学位论文].北京:中国矿业大学,2012.16-20.
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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.软件学报,2016,27(4):802-813

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History
  • Received:August 20,2015
  • Revised:October 15,2015
  • Online: January 14,2016
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