Abstract
Software testing is an important phase in software development. It involves two activities, test data generation and test execution. Test data generation is a NP-complete problem as we have to find a lot of test data to validate our system. Also those test data should be adequate in nature. In this paper, we present a method to generate test data automatically from initial test data and then testing these test data against the software under test (SUT) for adequacy criteria. First, we generate a test data set randomly. Then, we apply genetic algorithm to find a better test data set iteratively. We stop at the position where our test data set satisfies the stopping condition or it completed maximum iterations. We test the generated test data against the software to check its adequacy. The test data generated by our approach are more capable of finding the synchronization and loop faults. A case study is given to illustrate our approach.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Malhotra, R., Garg, M.: An adequacy based test data generation technique using genetic algorithms. J. Inf. Process. Syst. 7(2), 363–384 (2011)
Sommerville I.: Software Engineering, 7th edn., Addison-wesley
Mathur, A.P.: Foundation of Software Testing, 1st edn. Pearson Education, New Jersey (2008)
Mall, R.: Fundamentals of Software Engineering, 3rd edn. PHI, New Delhi (2009)
Goldberg, D.E.: Genetic Algorithms: in Search, Optimization and Machine Learning. Addison Wesley, Boston (1989)
Gupta, N.K., Rohil, M.K.: Using Genetic algorithm for unit testing of object oriented software. First International Conference on Emerging trends in Engineering and technology, IEEE, 2008
Emanuelle, F., Menezes, R., Braga, M.: Using Genetic algorithms for test plans for functional testing. In: Proceeding of 44th annual southeast regional conference, ACM, pp. 140–145, 2006
Srivastava, P.R., Kim, T.H.: Application of genetic algorithm in software testing. Int. J. Softw. Eng. Appl. 3(4), 87–95 (2009)
Hermadi, I., Ahmed, M.A.: Genetic algorithm based test data generator. Congr. Evolut. Comput. 1, 85–91 (2003)
Berndt, D.J., Fisher, J., Johnson, L., Pinglikar, J., Watkins, A.: Breeding software test cases with genetic algorithms. In: Proceedings of the 36th Annual Hawaii International Conference on System Science, Hawaii, Jan, 2003
Lin, J.C., Yeh, P.L.: Using genetic algorithms for test case generation in path testing. In: Proceedings of the 9th Asian Test Symposium, Taiwan, Dec, 2000
Baresal, A., Sthamer, H., Schmidt, M.: Fitness function design to improve evolutionary testing. In: Proceedings of the genetic and evolutionary computation conference, 2002
Rajappa, V., Biradar, A., Panda, S.: Efficient software test case generation using genetic algorithm based graph theory. First International Conference on Emerging Trends in Engineering and Technology, pp. 298–303, 2008
Sabharwal, S., Kumar, R., Sharma, C.: Applying genetic algorithm for prioritization of test case scenarios derived from UML diagrams. Int. J. Comput. Sci. 8(3), 433–444 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer India
About this paper
Cite this paper
Swain, S., Mohapatra, D.P. (2014). Genetic Algorithm-Based Approach for Adequate Test Data Generation. In: Mohapatra, D.P., Patnaik, S. (eds) Intelligent Computing, Networking, and Informatics. Advances in Intelligent Systems and Computing, vol 243. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1665-0_43
Download citation
DOI: https://doi.org/10.1007/978-81-322-1665-0_43
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-1664-3
Online ISBN: 978-81-322-1665-0
eBook Packages: EngineeringEngineering (R0)