15th European Conference on Artificial Intelligence
  July 21-26 2002     Lyon     France  
   

ECAI-2002 Conference Paper

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Can AI help to improve debugging substantially? Debugging Experiences with Value-Based Models

Wolfgang Mayer, Markus Stumptner, Dominik Wieland, Franz Wotawa

Finding and fixing faults in programs is usually an expensive and tedious task. Consequently the development of intelligent debugging tools that aid the programmer in this task is a topic of major industrial interest. This work describes two representations for applying model-based diagnosis to Java programs, a technique that permits locating (and partly correcting) faults without requiring a formal specification of the desired program behavior, since interaction can be limited to test cases and observations of variable correctness. One of the models uses a special transformation to provide more accurate diagnoses on programs with loops and this is borne out by the experiments. The presented results on actual debugging performance show clearly superior accuracy to classical debugging techniques, and better discrimination than dependency-based programs models. We discuss the results in terms of the properties of the two models and the various example programs and present avenues for further improvement.

Keywords: Debugging, Diagnosis, Model-Based Reasoning

Citation: Wolfgang Mayer, Markus Stumptner, Dominik Wieland, Franz Wotawa: Can AI help to improve debugging substantially? Debugging Experiences with Value-Based Models. In F. van Harmelen (ed.): ECAI2002, Proceedings of the 15th European Conference on Artificial Intelligence, IOS Press, Amsterdam, 2002, pp.417-421.


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ECAI-2002 is organised by the European Coordinating Committee for Artificial Intelligence (ECCAI) and hosted by the UniversitÚ Claude Bernard and INSA, Lyon, on behalf of Association Franšaise pour l'Intelligence Artificielle.