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.
- A.-L. Barabási. Linked: The New Science of Networks. Perseus Publishing, 2002.]]Google ScholarDigital Library
- K. Beck. Extreme Programming Explained: Embrace Change. Addison-Wesley, 1999.]] Google ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarCross Ref
- C. Darwin. On the Origin of Species: A facsimile of the first edition. Harvard University Press, July 1975.]]Google Scholar
- 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 ScholarDigital Library
- J. H. Holland. Adpatation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, MI, 1975.]] Google ScholarDigital Library
- IVIA Ltda. Phonnia. http://www.phonnia.com.br/.]]Google Scholar
- 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 ScholarDigital Library
- C. Lloyd. The alarm pheronones of social insects: A review. Technical report, Colorado State University, 2003.]]Google Scholar
- 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 ScholarDigital Library
- 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 Scholar
Index Terms
- Using genetic algorithms to generate test plans for functionality testing
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