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Test Case Generation as an AI Planning Problem

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Knowledge-Based Software Engineering

Abstract

While Artificial Intelligence techniques have been applied to a variety of software engineering applications, the area of automated software testing remains largely unexplored. Yet, test cases for certain types of systems (e.g., those with command language interfaces and transaction based systems) are similar to plans. We have exploited this similarity by constructing an automated test case generator with an AI planning system at its core. We compared the functionality and output of two systems, one based on Software Engineering techniques and the other on planning, for a real application: the StorageTek robot tape library command language. From this, we showed that AI planning is a viable technique for test case generation and that the two approaches are complementary in their capabilities.

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Author information

Authors and Affiliations

  1. Computer Science Department, Colorado State University, Fort Collins, CO, 80523

    Adele E. Howe & Anneliese von Mayrhauser

  2. HQ USAFA/DFCS, U.S. Air Force Academy, 2354 Fairchild Hall, Suite 6K41, CO, 80840

    Richard T. Mraz

Authors
  1. Adele E. Howe
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  2. Anneliese von Mayrhauser
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  3. Richard T. Mraz
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Editor information

Editors and Affiliations

  1. University of Pittsburgh, Pittsburgh

    Dorothy Setliff

  2. Concept Five Technologies, USA

    Howard Reubenstein

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© 1997 Kluwer Academic Publishers

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Howe, A.E., von Mayrhauser, A., Mraz, R.T. (1997). Test Case Generation as an AI Planning Problem. In: Setliff, D., Reubenstein, H. (eds) Knowledge-Based Software Engineering. Springer, Boston, MA. https://doi.org/10.1007/978-0-585-34714-1_5

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  • DOI: https://doi.org/10.1007/978-0-585-34714-1_5

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-7923-9789-2

  • Online ISBN: 978-0-585-34714-1

  • eBook Packages: Springer Book Archive

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