Abstract
Automatic test case generation is a major problem in software testing. Evolutionary structural testing is an approach to automatically generate test cases that uses a Genetic Algorithm (GA) which is guided by the data flow dependencies in the program to search for test data to cover the def-use association. The Particle Swarm Optimization (PSO) approach is a swarm intelligence technique which can be used to generate test data automatically. We have proposed an algorithm to generate test cases using PSO for data flow testing. We have simulated both the evolutionary and swarm intelligence techniques. From the experiments it has been observed that PSO outperforms GA in 100% def-use coverage percentage.
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
Girgis, M.R.: Automatic test data generation for data flow testing using genetic algorithm. Journal of Universal Computer Science 11(6), 898–915 (2005)
Pargas, R.P., Horrold, M.J., Peck, R.R.: Test data generation using genetic algorithm. The Journal of Software Testing,Verification and Reliability (1999)
Michael, C.C., McGraw, G., Schatz, M.A.: Generating software test data by evolution. IEEE Transactions on Software Engineering 27(12), 1085–1110 (2001)
Pei, M., Goodman, E.D., Gao, Z., Zhong, K.: Automated software test data generation using genetic algorithm. Technical report, GARGE of Michigan State University (1994)
Jones, B.F., Sthamer, H.H., Eyres, D.E.: Automatic structural testing using genetic algorithms. Software Engineering Journal 8(9), 299–306 (1996)
Roper, M., Maclean, I., Brooks, A., Miller, J., Wood, M.: Genetic algorithm and the automatic generation of test data. Technical report, University of Strathelyde (1995)
Watkins, A.E.L.: A tool for automatic generation of test data using genetic algorithm. In: Software Quality Conference, Dundee, Scotland (1995)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, pp. 1942–1948. IEEE Press, Los Alamitos (1995)
Windisch, A., Wappler, S., Wegener, J.: Applying paricle swarm optimization to software testing. In: GECCO, London, England, United Kingdom. ACM, New York (2007)
Agrawal, K., Srivastava, G.: Towards software test data generation using discrete quantum particle swarm optimization. In: ISEC, Mysore, India (February 2010)
Li, A., Zhang, Y.: Automatic generating all-path test data of a program based on pso. In: World Congress on Software Engineering. IEEE, Los Alamitos (2009)
Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: 6th International Symposium on Micromachine Human Science, pp. 39–43 (1995)
Rapps, S., Weyuker, E.J.: Selecting software test data using data flow information. IEEE Transactions on Software Enggineering 11(4), 367–375 (1985)
Allen, F.E., Cocke, J.: A program data flow analysis procedure. Communication of the ACM 19(3), 137–147 (1976)
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
Nayak, N., Mohapatra, D.P. (2010). Automatic Test Data Generation for Data Flow Testing Using Particle Swarm Optimization. In: Ranka, S., et al. Contemporary Computing. IC3 2010. Communications in Computer and Information Science, vol 95. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14825-5_1
Download citation
DOI: https://doi.org/10.1007/978-3-642-14825-5_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14824-8
Online ISBN: 978-3-642-14825-5
eBook Packages: Computer ScienceComputer Science (R0)