User Session-Based Test Case Generation and Optimization Using Genetic Algorithm


An approach to generating and optimizing test cases is proposed for Web application testing based on user sessions using genetic algorithm. A large volume of meaningful user sessions are obtained after purging their irrelevant information by analyzing user logs on the Web server. Most of the redundant user sessions are also removed by the reduction process. For test reuse and test concurrency, it divides the user sessions obtained into different groups, each of which is called a test suite, and then prioritizes the test suites and the test cases of each test suite. So, the initial test suites and test cases, and their initial executing sequences are achieved. However, the test scheme generated by the elementary prioritization is not much approximate to the best one. Therefore, genetic algorithm is employed to optimize the results of grouping and prioritization. Meanwhile, an approach to generating new test cases is presented using crossover. The new test cases can detect faults caused by the use of possible conflicting data shared by different users.

Share and Cite:

Z. Qian, "User Session-Based Test Case Generation and Optimization Using Genetic Algorithm," Journal of Software Engineering and Applications, Vol. 3 No. 6, 2010, pp. 541-547. doi: 10.4236/jsea.2010.36062.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] E. Hieatt and R. Mee, “Going Faster: Testing the Web Ap- plication,” IEEE Software, Vol. 19, No. 2, 2002, pp. 60-65.
[2] D. C. Kung, C. H. Liu and P. Hsia, “An Object-Oriented Web Test Model for Testing Web Applications,” Proceedings of the 1st Asia-Pacific Conference on Web Applications, New York, 2000, pp. 111-120.
[3] J. Offutt, Y. Wu. and X. Du, et al., “Bypass Testing of Web Applications,” Proceedings of the 15th IEEE Inter- national Symposium on Software Reliability Engineering, Bretagne, November 2004.
[4] C. H. Liu, D. C. Kung and P. Hsia, “Object-Based Data Flow Testing of Web Applications,” Proceedings of the 1st Asia-Pacific Conference on Quality Software, Hong Kong, 2000, pp. 7-16.
[5] C. Elbaum, G. Rothermel and S. Karre, et al., “Leveraging User Session Data to Support Web Application Testing,” IEEE Transaction on Software Engineering, California, May 2005.
[6] R. Godin, R. Missaoui and H. Alaoui, “Incremental Concept Formation Algorithms Based on Galois (Concept) Lattices,” Computational Intelligence, Vol. 11, No. 2, 1995, pp. 246-267.
[7] S. Khor and P. Grogono, “Using a Genetic Algorithm and Formal Concept Analysis to Generate Branch Coverage Test Data Automatically,” Proceedings of the Inter- national Conference on Automated Software Engin- eering, Austria, 2004, pp. 346-349.
[8] H. H. Sthamer, “The Automatic Generation of Software Test Data Using Genetic Algorithms,” PhD. Dissertation, University of Glamorgan, Wales, 1996.
[9] D. Berndt, J. Fisher and L. Johnson, et al., “Breeding Software Test Cases with Genetic Algorithms,” Pro- ceedings of the 36th Hawaii International Conference on System Sciences, 2003, pp. 17-24.
[10] R. P. Pargas and M. J. Harrold, “Test-Data Generation Using Genetic Algorithms,” Journal of Software Testing, Verification and Reliability, Vol. 9, No. 4, 1999, pp. 263-282.
[11] X. X. Jia, J. Wu, M. Z. Jin, et al., “Some Experiment Analysis of Using Generic Algorithm in Automatic Test Data Generation,” in Chinese, Journal of Chinese Com- puter Systems, Vol. 28, No. 3, 2007, pp. 520-525.
[12] D. J. Berndt and A. Watkins, “Investigating the Perfor- mance of Genetic Algorithm-Based Software Test Case generation,” Proceedings of the International Symposium on High Assurance Systems Engineering, Florida, 2004, pp. 261-262.
[13] J. H. Holland, “Adaptation in Natural and Artificial System,” University of Michigan Press, Michigan, 1975.

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.