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    University of British Columbia
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    Boosting Verification by Automatic Tuning of Decision Procedures

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    Parameterized heuristics abound in computer aided design and verification, and manual tuning of the respective parameters is difficult and time-consuming. Very recent results from the artificial intelligence (AI) community suggest that this tuning process can be automated, and that doing so can lead to significant performance improvements; furthermore, automated parameter optimization can provide valuable guidance during the development of heuristic algorithms. In this paper, we study how such an AI approach can improve a state-of-theart SAT solver for large, real-world bounded model-checking and software verification instances. The resulting, automaticallyderived parameter settings yielded runtimes on average 4.5 times faster on bounded model checking instances and 500 times faster on software verification problems than extensive handtuning of the decision procedure. Furthermore, the availability of automatic tuning influenced the design of the solver, and the automatically-derived parameter settings provided a deeper insight into the properties of problem instances.

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    Description

    Title : Boosting Verification by Automatic Tuning of Decision Procedures
    Author(s) : Frank Hutter, Domagoj Babic, Holger H. Hoos, Alan J. Hu
    Abstract : Parameterized heuristics abound in computer aided design and verification, and manual tuning of the respective parameters is difficult and time-consuming. Very recent results from the artificial intelligence (AI) community suggest that this tuning process can be automated, and that doing so can lead to significant performance improvements; furthermore, automated parameter optimization can provide valuable guidance during the development of heuristic algorithms. In this paper, we study how such an AI approach can improve a state-of-theart SAT solver for large, real-world bounded model-checking and software verification instances. The resulting, automaticallyderived parameter settings yielded runtimes on average 4.5 times faster on bounded model checking instances and 500 times faster on software verification problems than extensive handtuning of the decision procedure. Furthermore, the availability of automatic tuning influenced the design of the solver, and the automatically-derived parameter settings provided a deeper insight into the properties of problem instances.
    Keywords : automatic tuning, decision procedures, machine learning

    Subject : unspecified
    Area : Computer Science
    Language : English
    Year : 2007

    Affiliations University of British Columbia
    Conference_title : FMCAD'07: Proceedings of the 7th International Conference Formal Methods in Computer Aided Design
    Publisher : IEEE Computer Society
    Pages : 27--34
    Url : http://www.domagoj-babic.com/uploads/Pubs/FMCAD07/fmcad07.pdf
    Doi : 10.1.1.68.1795

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