Abstract
One of the major challenge and time-consuming work is optimum test data generation to assure software quality. Researchers have proposed several methods over years to generate automatically solution which have different drawbacks. In this paper, we propose Genetic Algorithm (GA) based tester with different parameters to automate the structural-oriented test data generation on the basis of internal program structure. Our proposed fitness function is intended to traverse paths of the program as more as possible. This integration improves the GA performance in search space exploration and exploitation fields with faster convergence. At last, we present some results according to our experiment which were promising in term of structural coverage and time order.
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
Preview
Unable to display preview. Download preview PDF.
References
Babamir, F.S., Babamir, S.M.: Applying Redundancy Frequency to Verify Program based on Genetic Algorithm. In: 18th Iranian Computer and Electrical Engineering ICEE, Isfahan, IRAN (2010)
Babamir, F.S., Babamir, S.M.: A GA based-method to Generate Test Data of Program Paths. In: 15th National Computer Conference CSICC, Tehran, IRAN (2010)
Babamir, S.M., Babamir, F.S.: A Genetic-based Algorithm to Optimum Generation of Data for Program Paths Testing. In: 14th National Computer Conference CSICC, Tehran, IRAN (2009)
Goldberg, D.E.: Genetic Algorithm in a Search Optimization and Machine Learning. Addison Wesley, Reading (1989)
Bern, D., Fisher, J., Johnson, L., Pinglikar, J., Watkins, A.: Breeding Software Test Cases with Genetic Algorithm. In: IEEE Proceedings of the Hawaii International Conference on System Science, Hawaii (2003)
Srivastava, P.R., Kim, T.: Applied to Genetic Algorithm in Software Engineering. International Journal of Software Engineering and its Applications 3(4), 87–96 (2009)
Watson, A.H., McCabe, T.J.: Structural Testing: A Testing Methodology Using the Climatic Complexity Metric. Computer Systems Laboratory, International Institute of Standards and Technology Gaithersburg, MD 20899001 (1996)
Ahmed, M.A., Hermadi, I.: GA-based Multiple Paths Test Data Generator. Journal of Computers & Operations Research 35(10), 3107–3124 (2008)
Mitchell, M.: An Introduction to Genetic Algorithm. MIT, Reading (1999)
Holland, J.: Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor (1975)
Bueno, P.M.S., Jino, M.: Identification of Potentially Infeasible Program Paths by Monitoring the Search for Test Data. In: Proceedings of the 15th IEEE International Conference on Automated Software Engineering (ASE 2000), Grenoble, France, September 11-15, pp. 209–218 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Babamir, F.S., Hatamizadeh, A., Babamir, S.M., Dabbaghian, M., Norouzi, A. (2010). Application of Genetic Algorithm in Automatic Software Testing. In: Zavoral, F., Yaghob, J., Pichappan, P., El-Qawasmeh, E. (eds) Networked Digital Technologies. NDT 2010. Communications in Computer and Information Science, vol 88. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14306-9_54
Download citation
DOI: https://doi.org/10.1007/978-3-642-14306-9_54
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14305-2
Online ISBN: 978-3-642-14306-9
eBook Packages: Computer ScienceComputer Science (R0)