skip to main content
10.1145/1185448.1185480acmotherconferencesArticle/Chapter ViewAbstractPublication Pagesacm-seConference Proceedingsconference-collections
Article

Using genetic algorithms to generate test plans for functionality testing

Published:10 March 2006Publication History

ABSTRACT

Like in other fields, computer products (applications, hardware, etc.), before being marketed, require some level of testing to verify whether they meet their design and functional specifications -- called functionality test. The general process of performing functionality test consists in the production of a test plan that is then executed by humans or by automated software tools. The main difficulty in this entire process is the definition of such test plan. How can we know what a good sequence (test plan) is? The rule of thumb is to trust on people who understand the workings of the application being tested and who can decide what should be tested. The danger is that experts, due to their over-confidence on their knowledge, may become blind to issues that should otherwise be easy to see. This paper describes a technique based on genetic algorithms that is able to generate good test plans in an unbiased way and with minimum expert interference.

References

  1. A.-L. Barabási. Linked: The New Science of Networks. Perseus Publishing, 2002.]]Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. K. Beck. Extreme Programming Explained: Embrace Change. Addison-Wesley, 1999.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. D. Berndt, J. Fisher, L. Johnson, J. Pinglikar, and A. Watkins. Breeding software test cases with genetic algorithms. In HICSS '03: Proceedings of the 36th Annual Hawaii International Conference on System Sciences (HICSS'03), pages 338--347, Washington, DC, USA, 2003. IEEE Computer Society.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. D. J. Berndt. Investigating the performance of genetic algorithms-based software test case generation. In Eighth IEEE International Symposium on High Assurance Systems Engineering (HASE'04), 2004.]]Google ScholarGoogle ScholarCross RefCross Ref
  5. C. Darwin. On the Origin of Species: A facsimile of the first edition. Harvard University Press, July 1975.]]Google ScholarGoogle Scholar
  6. E. W. Dijkstra. Structured programming. In O.-J. Dahl, E. W. Dijkstra, and C. A. R. Hoare, editors, Notes on Structured Programming, pages 1--82. Academic Press, 1972.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. J. H. Holland. Adpatation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, MI, 1975.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. IVIA Ltda. Phonnia. http://www.phonnia.com.br/.]]Google ScholarGoogle Scholar
  9. B. Korel. Automated test data generation for programs with procedures. In ISSTA '96: Proceedings of the 1996 ACM SIGSOFT international symposium on Software testing and analysis, pages 209--215, New York, NY, USA, 1996. ACM Press.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. C. Lloyd. The alarm pheronones of social insects: A review. Technical report, Colorado State University, 2003.]]Google ScholarGoogle Scholar
  11. C. C. Michael, G. E. McGraw, M. A. Schatz, and C. C. Walton. Genetic algorithms for dynamic test data generation. In ASE '97: Proceedings of the 1997 International Conference on Automated Software Engineering (ASE '97) (formerly: KBSE), pages 307--308, Washington, DC, USA, 1997. IEEE Computer Society.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. C. E. Williams. Software testing and uml. In Proceedings of the 10th International Symposium on Software Reliability Engineering, Boca Raton, Florida, Nov. 1999. IEEE Press.]]Google ScholarGoogle Scholar

Index Terms

  1. Using genetic algorithms to generate test plans for functionality testing

                Recommendations

                Comments

                Login options

                Check if you have access through your login credentials or your institution to get full access on this article.

                Sign in
                • Published in

                  cover image ACM Other conferences
                  ACM-SE 44: Proceedings of the 44th annual Southeast regional conference
                  March 2006
                  823 pages
                  ISBN:1595933158
                  DOI:10.1145/1185448

                  Copyright © 2006 ACM

                  Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

                  Publisher

                  Association for Computing Machinery

                  New York, NY, United States

                  Publication History

                  • Published: 10 March 2006

                  Permissions

                  Request permissions about this article.

                  Request Permissions

                  Check for updates

                  Qualifiers

                  • Article

                  Acceptance Rates

                  Overall Acceptance Rate178of377submissions,47%

                PDF Format

                View or Download as a PDF file.

                PDF

                eReader

                View online with eReader.

                eReader