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  • Learning assumptions for compositional verification of timed systems

    Author(s)
    Lin, SW
    Andre, E
    Liu, Y
    Sun, J
    Dong, JS
    Griffith University Author(s)
    Dong, Jin-Song
    Year published
    2014
    Metadata
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    Abstract
    Compositional techniques such as assume-guarantee reasoning (AGR) can help to alleviate the state space explosion problem associated with model checking. However, compositional verification is difficult to be automated, especially for timed systems, because constructing appropriate assumptions for AGR usually requires human creativity and experience. To automate compositional verification of timed systems, we propose a compositional verification framework using a learning algorithm for automatic construction of timed assumptions for AGR. We prove the correctness and termination of the proposed learning-based framework, and ...
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    Compositional techniques such as assume-guarantee reasoning (AGR) can help to alleviate the state space explosion problem associated with model checking. However, compositional verification is difficult to be automated, especially for timed systems, because constructing appropriate assumptions for AGR usually requires human creativity and experience. To automate compositional verification of timed systems, we propose a compositional verification framework using a learning algorithm for automatic construction of timed assumptions for AGR. We prove the correctness and termination of the proposed learning-based framework, and experimental results show that our method performs significantly better than traditional monolithic timed model checking.
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    Journal Title
    IEEE Transactions on Software Engineering
    Volume
    40
    Issue
    2
    DOI
    https://doi.org/10.1109/TSE.2013.57
    Subject
    Software engineering not elsewhere classified
    Information systems
    Publication URI
    http://hdl.handle.net/10072/172878
    Collection
    • Journal articles

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