Test Case Generation from QR Models

Authors:
Harald Brandl;Franz Wotawa
Affiliations:
Institut für Softwaretechnologie, Technische Universität Graz, Graz, Austria A-8010;Institut für Softwaretechnologie, Technische Universität Graz, Graz, Austria A-8010
Venue:
IEA/AIE '08 Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence
Year:
2008

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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.