Abstract
Black-box (functional) test cases are identified from functional requirements of the tested system, which is viewed as a mathematical function mapping its inputs onto its outputs. While the number of possible black-box tests for any non-trivial program is extremely large, the testers can run only a limited number of test cases under their resource limitations. An effective set of test cases is the one that has a high probability of detecting faults presenting ina computer program.In this paper, we introduce a new, computationally intelligent approach to automated generation of effective test cases based on a novel, Fuzzy-Based Age Extension of Genetic Algorithms (FAexGA). The basic idea is to eliminate "bad" test cases that are unlikely to expose any error, while increasing the number of "good" test cases that have a high probability of producing an erroneous output. The promising performance of the FAexGA-based approach is demonstrated on testing a complex Boolean expression.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Arabas, J., Michalewicz, Z., Mulawka, J.: GAVaPS – a Genetic Algorithm with varying population size. In: Proc. of the first IEEE Conference on Evolutionary Computation, pp. 73–78 (1994)
Deb, K., Agrawal, S.: Understanding interactions among genetic algorithm parameters. In: Banzhaf, W., Reeves, C. (eds.) Foundations of Genetic Algorithm, vol. 5, pp. 265–286. Morgan Kaufmann, San Francisco (1998)
DeMillo, R.A., Offlut, A.J.: Constraint-Based Automatic Test Data Generation. IEEE Transactions on Software Engineering 17(9), 900–910 (1991)
Eibon, A.E., Hinterding, R., Michalewicz, Z.: Parameter Control in Evolutionary Algorithm. IEEE Transactions on Evolutionary Computation, 124–141 (1999)
Elbaum, S., Malishevsky, A.G., Rothermel, G.: Prioritizing Test Cases for Regression Testing. In: Proc. of ISSTA 2000, pp. 102–112 (2000)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)
Herrera, F., Lozano, M.: Adaptation of genetic algorithm parameters based on fuzzy logic controllers. In: Herrera, F., Verdegay, J. (eds.) Genetic Algorithms and Soft Computing, pp. 95–125. Physica Verlag, Heidelberg (1996)
Herrera, F., Magdalena, L.: Genetic Fuzzy Systems: A Tutorial, vol. 13, pp. 93–121. Tatra Mt. Math. Publ, Slovakia (1997)
Holland, J.H.: Genetic Algorithms. Scientific American 267(1), 44–150 (1992)
Jorgensen, P.C.: Software Testing: A Craftsman’s Approach, 2nd edn. CRC Press, Boca Raton (2002)
Klir, G.J., Yuan, B.: Fuzzy Sets and Fuzzy Logic: Theory and Applications. Prentice-Hall Inc., Englewood Cliffs (1995)
Last, M., Eyal, S.: A Fuzzy-Based Lifetime Extension of Genetic Algorithms. Fuzzy Sets and Systems 149(1), 131–147 (2005)
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 3rd Revised and Extended Edition. Springer, Heidelberg (1999)
Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1996)
National Institute of Standards & Technology. The Economic Impacts of Inadequate Infrastructure for Software Testing. Planning Report 02-3 (May 2002)
Patton, R.: Software Testing. SAMS (2000)
Pfleeger, S.L.: Software Engineering: Theory and Practice, 2nd edn. Prentice-Hall, Englewood Cliffs (2001)
Reeves, C.R.: Using Genetic Algorithms with Small Populations. In: Proc. of the Fifth Int. Conf. On Genetic Algorithms and Their Applications, pp. 92–99. Morgan Kaufmann, San Francisco (1993)
Schroeder, P.J., Korel, B.: Black-Box Test Reduction Using Input-Output Analysis. In: Proc. of ISSTA 2000, pp. 173–177 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Last, M., Eyal, S., Kandel, A. (2006). Effective Black-Box Testing with Genetic Algorithms. In: Ur, S., Bin, E., Wolfsthal, Y. (eds) Hardware and Software, Verification and Testing. HVC 2005. Lecture Notes in Computer Science, vol 3875. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11678779_10
Download citation
DOI: https://doi.org/10.1007/11678779_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-32604-5
Online ISBN: 978-3-540-32605-2
eBook Packages: Computer ScienceComputer Science (R0)