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Abstract:

Genetic Algorithm (GA for short), which simulates the process of natural evolution to search and achieve the optimal solution, is often employed to generate test cases. Therefore, a GA-based test case generation policy, which introduces the concept of node probability coverage as the detection method for nodes in unreachable paths, is proposed in this work. Moreover, in the application under test, complex decisions and nested structures often lead to different execution difficulty of each statement. Therefore, a path coverage method based on contact layer proximity is presented, which quantifies the difficulty difference of different statements using contact vector. Besides, contact layer proximity and node probability coverage are combined to design the fitness function in GA. Then, the experiments about two classical benchmark cases, namely triangle-classifying program and bubble sort program, are conducted. The result is compared and analyzed with a similar method, namely the method of node probability coverage. It is shown that the proposed test case generation method is more efficient. Finally, a plug-in using the proposed test case generation method is developed.

 

References: 15

      1. B. Bako, A. Borchert, N. Heidenbluth, and J. Mayer, “Plugin-Based Systems with Self-Organized Hierarchical Presentation,” in International Conference on Software Engineering Research and Practice, pp. 577-584, Las Vegas, Nevada, USA, June 2006
      2. D. Beyer, T. A. Henzinger, R. Jhala, and R. Majumdar, “An Eclipse Plug-in for Model Checking,” in IEEE International Workshop on Program Comprehension, pp. 251-255, Bari, Italy, June 2004
      3. W. U. Chuan and D. W. Gong, “Evolutionary Generation of Test Data for Regression Testing Based on Path Correlation,” Chinese Journal of Computers, vol. 38, no. 11, pp. 2247-2261, November 2015 (in Chinese with English abstract)
      4. D. W. Gong and L. N. Ren, “Evolutionary Generation of Regression Test Data,” Chinese Journal of Computers, vol. 37, no. 3, pp. 489-499, March 2014 (in Chinese with English abstract)
      5. M. Greiler and A. V. Deursen. “What Your Plug-in Test Suites Really Test: an Integration Perspective on Test Suite Understanding,” Empirical Software Engineering, vol. 18, no. 5, pp. 859-900, October 2013
      6. M. Greiler, A. V. Deursen, and M. A. Storey, “Test confessions: A Study of Testing Practices for Plug-in Systems,” in IEEE International Conference on Software Engineering, pp. 244-254, Zurich, Switzerland, June 2012
      7. M. Greiler, H. G. Gross, Van Deursen A, “Understanding Plug-in Test Suites From an Extensibility Perspective,” in The 17th Working Conference on Reverse Engineering, pp. 67-76, Beverly, MA, USA, October 2010
      8. M. Jahn, R. Wolfinger, and H. Mössenböck, “Extending Web Applications with Client and Server Plug-ins,” in International Conference on Software Engineering, pp. 33-44, Paderborn, February 2010
      9. S. J. Jiang, L. S. Wang, M. Xue, Y. M. Zhang, Q. Yu, and H. R. Yao, “Test Case Generation Based on Combination of Schema Using Particle Swarm Optimization,” Journal of Software, vol. 27, no. 4, pp. 785-801, April 2016 (in Chinese with English abstract)
      10. H. V. Nguyen, C. Kästner, and T. N. Nguyen, “Exploring Variability-aware Execution for Testing Plugin-based Web Applications,” in ACM International Conference on Software Engineering, pp. 907-918, Orlando, Florida, USA, May 2014
      11. C. Wu, D. W. Gong, and X. J. Yao, “Selection of Paths for Regression Testing Based on Branch Coverage,” Journal of Software, vol. 27, no. 4, pp. 839-854, April 2016 (in Chinese with English abstract)
      12. C. Y. Xia, Y. Zhang, and L. Song, “Evolutionary Generation of Test Data for Paths Coverage Based on Node Probability,” Journal of Software, vol. 27, no. 4, pp. 802-813, April 2016 (in Chinese with English abstract)
      13. X. J. Yao, “Theory of Evolutionary Generation of Test Data for Complex Software and Applications,” China University of Mining and Technology, Xuzhou, China, April 2011 (in Chinese with English abstract)
      14. X. J. Yao, D. W. Gong, and B. Li, “Evolutional Test Data Generation for Path Coverage by Integrating Neural Network,” Journal of Software, vol. 27, no. 4, pp. 828-838, April 2016 (in Chinese with English abstract)
      15. G. J. Zhang, D. W. Gong, and X. J. Yao, “Test Case Generation Based on Mutation Analysis and Set Evolution,” Chinese Journal of Computers, vol. 38, no. 11, pp. 2318-2331, November 2015 (in Chinese with English abstract)

           

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