Skip to main content

Predicate Expression Cost Functions to Guide Evolutionary Search for Test Data

Part of the Lecture Notes in Computer Science book series (LNCS,volume 2724)

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.

Keywords

  • Cost Function
  • Logical Negation
  • Simple Program
  • Test Data Generation
  • Exception Condition

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/3-540-45110-2_149
  • Chapter length: 10 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   74.99
Price excludes VAT (USA)
  • ISBN: 978-3-540-45110-5
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   99.00
Price excludes VAT (USA)

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. B. F. Jones, H. Sthamer, and D.E. Eyres. Automatic structural testing using genetic algorithms. Software Engineering Journal, 11(5):299–306, 1996.

    CrossRef  Google Scholar 

  2. B. Korel. Automated software test data generation. IEEE Transactions on Software Engineering, 16(8):870–879, August 1990.

    CrossRef  Google Scholar 

  3. 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.

    Google Scholar 

  4. 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.

    Google Scholar 

  5. 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.

    CrossRef  Google Scholar 

  6. Judea Pearl. Probabilistic reasoning in intelligent systems. Morgan Kaufmann, 1988.

    Google Scholar 

  7. N. Tracey, J. Clark, and K. Mander. Automated program flaw finding using simulated annealing. Software Engineering Notes, 23(2):73–81, March 1998.

    CrossRef  Google Scholar 

  8. N. Tracey, J. Clark, K. Mander, and J. McDermid. Automated test data generation for exception conditions. Software — Practice and Experience, 30:61–79, 2000.

    CrossRef  Google Scholar 

  9. J Wegener, A. Baresel, and H. Sthamer. Evolutionary test environment for automatic structural testing. Information and Software Technology, 43:841–854, 2001.

    CrossRef  Google Scholar 

  10. 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.

    Google Scholar 

  11. L. A. Zadeh. Fuzzy logic and approximate reasoning. Synthese, 30:407–428, 1975.

    MATH  CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2003 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Bottaci, L. (2003). Predicate Expression Cost Functions to Guide Evolutionary Search for Test Data. In: , 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

Download citation

  • DOI: https://doi.org/10.1007/3-540-45110-2_149

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40603-7

  • Online ISBN: 978-3-540-45110-5

  • eBook Packages: Springer Book Archive