Abstract
Several researchers are using evolutionary search methods to search for test data with which to test a program. The fitness or cost function depends on the test goal but almost invariably an important component of the cost function is an estimate of the cost of satisfying a predicate expression as might occur in branches, exception conditions, etc. This paper reviews the commonly used cost functions and points out some deficiencies. Alternative cost functions are proposed to overcome these deficiencies. The evidence from an experiment is that they are more reliable.
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References
B. F. Jones, H. Sthamer, and D.E. Eyres. Automatic structural testing using genetic algorithms. Software Engineering Journal, 11(5):299–306, 1996.
B. Korel. Automated software test data generation. IEEE Transactions on Software Engineering, 16(8):870–879, August 1990.
G. McGraw, C. Michael, and M Schatz. Generating software test data by evolution. Technical Report RSTR-018-97-01, RST Corporation, Suite 250, 21515 Ridgetop Circle, Sterling VA 20166, 1998.
C. Michael, G. McGraw, M. Schatz, and C. Walton. Genetic algorithms for dynamic test data generation. Technical Report RSTR-003-97-11, RST Corporation, Suite 250, 21515 Ridgetop Circle, Sterling VA 20166, 1997.
R. P. Pargas, M. J. Harrold, and R. P. Peck. Test-data generation using genetic algorithms. Software Testing, Verification and Reliability, 9:263–282, 1999.
Judea Pearl. Probabilistic reasoning in intelligent systems. Morgan Kaufmann, 1988.
N. Tracey, J. Clark, and K. Mander. Automated program flaw finding using simulated annealing. Software Engineering Notes, 23(2):73–81, March 1998.
N. Tracey, J. Clark, K. Mander, and J. McDermid. Automated test data generation for exception conditions. Software — Practice and Experience, 30:61–79, 2000.
J Wegener, A. Baresel, and H. Sthamer. Evolutionary test environment for automatic structural testing. Information and Software Technology, 43:841–854, 2001.
D. Whitley. The genitor algorithm and selective pressure: why rank based allocation of reproductive trials is best. Proceedings of the Third International Conference GAs., pages 116–121, 1989.
L. A. Zadeh. Fuzzy logic and approximate reasoning. Synthese, 30:407–428, 1975.
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Bottaci, L. (2003). Predicate Expression Cost Functions to Guide Evolutionary Search for Test Data. In: Cantú-Paz, E., et al. Genetic and Evolutionary Computation — GECCO 2003. GECCO 2003. Lecture Notes in Computer Science, vol 2724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45110-2_149
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DOI: https://doi.org/10.1007/3-540-45110-2_149
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