Genetic Algorithm Approach to Optimize Test Cases

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2020 by IJETT Journal
Volume-68 Issue-10
Year of Publication : 2020
Authors : Baswaraju Swathi, Dr.Harshvardhan Tiwari
DOI :  10.14445/22315381/IJETT-V68I10P219


MLA Style: Baswaraju Swathi, Dr.Harshvardhan Tiwari  "Genetic Algorithm Approach to Optimize Test Cases" International Journal of Engineering Trends and Technology 68.10(2020):112-116. 

APA Style:Baswaraju Swathi, Dr.Harshvardhan Tiwari. Genetic Algorithm Approach to Optimize Test Cases  International Journal of Engineering Trends and Technology, 68(10),112-116.

Test case generation is considered a significant and complex aspect in software testing, ensuring the quality of a software product. In a highly competitive environment, web applications have become crucial to most enterprises, demanding the application`s quality. The characteristics of such applications include client-server, distributed, and dynamic. Hence meticulous testing of a web-based application becomes necessary. Many strategies have been proposed to address the issues w.r.t test case generation for web applications. One such strategy is a Genetic Algorithm (GA), which is an evolutionary technique. In this paper, we analyze the test suit generation as a complex problem and derive the test cases with traditional test case generation approaches where the common generation problems are addressed with a case study. Further Genetic Algorithm approach, the parameters which can enhance the test case generation is proposed. The various encoding and selection techniques of GA are considered. The parameter of fitness function which determines the success of GA is analyzed. Finally the evaluation criteria code coverage is examined to assess the test effectiveness.


[1] NichaKosindrdecha and JirapunDaengdej, 2010, “A Test Case Generation Process and Technique,” Journal of Software Engineering,4:265-287, DOI: 10.3923/jse.2010.265.287.
[2] Nabuco M., Paiva A.C.R. (2014), “Model-Based Test Case Generation for Web Applications,” In Murgante B. et al. (eds) Computational Science and Its Applications – ICCSA 2014. ICCSA 2014. Lecture Notes in Computer Science, vol 8584. Springer, Cham.
[3] Arora A., Sinha M, “Web Application Testing: A Review on Techniques, Tools, and the State of Art,” International Journal of Scientific & Engineering Research, Volume 3, Issue 2, February- 2012 1ISSN 2229-5518.
[4] Arun Sharma, Rajesh Kumar, “Towards Multi-Faceted Test Cases Optimization,” Journal of Software Engineering and Applications 4:550-557 · January 2011.
[5] M. Farina, K. Deb, and P. Amato, “Dynamic multiobjective optimization problems: test cases, approximations, and applications,” IEEE Transactions on Evolutionary Computation, vol. 8, no. 5, pp. 425-442, Oct. 2004, doi: 10.1109/TEVC.2004.831456.
[6] V. Antinyan, J. Derehag, A. Sandberg and M. Staron, “Mythical Unit Test Coverage, in IEEE Software,” vol. 35, no. 3, pp. 73-79, May/June 2018, doi: 10.1109/MS.2017.3281318.
[7] BaswarajuSwathi, Harshvardhan Tiwari, “Test Case Generation Process using Soft Computing Techniques,” International Journal of Innovative Technology and Exploring Engineering (IJITEE), ISSN: 2278- 3075, Volume-9 Issue-1, November 2019.
[8] A. Shahbazi and J. Miller, "Black-Box String Test Case Generation through a Multiobjective Optimization" in IEEE Transactions on Software Engineering, vol. 42, no. 04, pp. 361-378, 2016.doi: 10.1109/TSE.2015.2487958
[9] Rozmie R. Othman, Kamal Z Zamli, “T-Way Strategies and Its Applications for Combinatorial Testing,” International Journal on New Computer Architectures and Their Applications(IJNCAA).
[10] HirohideHaga, Akihisa Suehiro, “Automatic test case generation based on genetic algorithm and mutation analysis,” 2012 IEEE International Conference on Control System, Computing and Engineering.
[11] Shirole M., Kumar R, “A Hybrid Genetic Algorithm Based Test Case Generation Using Sequence Diagrams,” In Ranka S. et al. (eds) Contemporary Computing. IC3 2010. Communications in Computer and Information Science, vol 94. Springer, Berlin, Heidelberg.
[12] Deepak Kumar, ManuPhogat, “Genetic Algorithm Approach For Test Case Generation Randomly: A Review,” International Journal of Computer Trends and Technology (IJCTT) – Volume 49 Number 4 July 2017.
[13] A. Shukla, H. M. Pandey, and D. Mehrotra, "Comparative review of selection techniques in genetic algorithm," 2015 International Conference on Futuristic Trends on Computational Analysis and Knowledge Management (ABLAZE), Noida, 2015, pp. 515-519, doi: 10.1109/ABLAZE.2015.7154916.
[14] ArinaAfanasyeva, Maxim Buzdalov, “Choosing Best Fitness Function with Reinforcement Learning,” 2011 10th International Conference on Machine Learning and Applications and Workshops.
[15] ShvetaParnami, Krishna Swaroop Sharma, “Empirical Validation of Test Case Generation Based on All- Edge Coverage Criteria,” International Journal of Computer Applications, September 2015.
[16] Panichella, A., Kifetew, F. M., &Tonella, P. (2018). “Automated Test Case Generation as a Many- Objective Optimisation Problem with Dynamic Selection of the Targets,” IEEE Transactions on Software Engineering, 44(2),122–158.

code coverage, evolutionary technique, fitness function, Genetic Algorithm, test case generation