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Abstract

In this paper we present the application of AI to automated test case generation. In particular we introduce a technique for deriving abstract test cases from qualitative models. In the case of reactive systems, the behavior of the device depends on its internal state and the perception of the environment via its sensors. Such systems are well suited for modeling with using Qualitative Reasoning methods. Qualitative Reasoning enables one to specify system behavior that also acquires changing environmental conditions and hence provides a good foundation to derive more realistic test cases. In the first part of this paper we give a short introduction to Qualitative Reasoning and Garp3, the tool we use for model creation and simulation. We also present a method for modeling differential equations within Garp3. In the second part we deal with abstract test case generation from QR models and present first results obtained.

Keywords

Model-based Reasoning Qualitative Reasoning Model-based Testing Embedded Systems 

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References

  1. 1.
    Bouwer, A., Liem, J., Bredeweg, B.: User Manual for Single-User Version of QR Workbench. Naturnet-Redime, STREP project co-funded by the European Commission within the Sixth Framework Programme (2002-2006), p. 1 (2005) (Project no. 004074. Project deliverable D4.2.1)Google Scholar
  2. 2.
    Bredeweg, B., Liem, J., Bouwer, A., Salles, P.: Curriculum for learning about QR modelling. Naturnet-Redime, STREP project co-funded by the European Commission within the Sixth Framework Programme (2002-2006) (2005) (Project no. 004074. Project deliverable D6.9.1)Google Scholar
  3. 3.
    Bredeweg, B., Bouwer, A., Jellema, J., Bertels, D., Linnebank, F.F., Liem, J.: Garp3 - a new workbench for qualitative reasoning and modelling. In: Proceedings of 20th International Workshop on Qualitative Reasoning (QR-2006), Hannover, New Hampshire, USA, pp. 21–28 (2006)Google Scholar
  4. 4.
    Forbus, K.D.: Qualitative process theory. Artif. Intell. 24(1-3), 85–168 (1984)CrossRefGoogle Scholar
  5. 5.
    Jard, C., Jeron, T.: TGV: theory, principles and algorithms. International Journal on Software Tools for Technology Transfer (STTT) 7(4), 297–315 (2004)Google Scholar
  6. 6.
    De Kleer, J., Brown, J.S.: A qualitative physics based on confluences. Artif. Intell. 24(1-3), 7–83 (1984)CrossRefGoogle Scholar
  7. 7.
    Kuipers, B.: Qualitative simulation. Artificial Intelligence 26, 289–338 (1986), Reprinted In: Weld, D., De Kleer, J. (eds.): Qualitative Reasoning about Physical Systems, pp. 236–260. Morgan Kaufmann, San Francisco (1990) Google Scholar
  8. 8.
    Kuipers, B.: Qualitative simulation: Then and now. Artificial Intelligence 59(1-2), 133–140 (1993)CrossRefGoogle Scholar
  9. 9.
    Morel, P., Jeron, T.: Test Generation Derived from Model-Checking. In: Halbwachs, N., Peled, D.A. (eds.) CAV 1999. LNCS, vol. 1633, pp. 108–121. Springer, Heidelberg (1999)Google Scholar
  10. 10.
    Tretmans, J.: Test generation with inputs, outputs, and quiescence. In: Margaria, T., Steffen, B. (eds.) TACAS 1996. LNCS, vol. 1055, pp. 127–146. Springer, Heidelberg (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Harald Brandl
    • 1
  • Franz Wotawa
    • 1
  1. 1.Institut für SoftwaretechnologieTechnische Universität GrazGrazAustria

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