CELL: A compositional verification framework
This paper presents CELL, a comprehensive and extensible framework for compositional verification of concurrent and real-time systems based on commonly used semantic models. For each semantic model, CELL offers three libraries, i.e., compositional verification paradigms, learning algorithms and mode...
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2013
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sg-smu-ink.sis_research-60002020-03-12T09:38:57Z CELL: A compositional verification framework JI, Kun LIU, Yang SUN, Jun SUN, Jun DONG, Jin Song NGUYEN, Truong Khanh This paper presents CELL, a comprehensive and extensible framework for compositional verification of concurrent and real-time systems based on commonly used semantic models. For each semantic model, CELL offers three libraries, i.e., compositional verification paradigms, learning algorithms and model checking methods to support various state-of-the-art compositional verification approaches. With well-defined APIs, the framework could be applied to build customized model checkers. In addition, each library could be used independently for verification and program analysis purposes. We have built three model checkers with CELL. The experimental results show that the performance of these model checkers can offer similar or often better performance compared to the state-of-the-art verification tools. 2013-10-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/4997 info:doi/10.1007/978-3-319-02444-8_38 https://ink.library.smu.edu.sg/context/sis_research/article/6000/viewcontent/cell.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Programming Languages and Compilers Software Engineering |
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Programming Languages and Compilers Software Engineering JI, Kun LIU, Yang SUN, Jun SUN, Jun DONG, Jin Song NGUYEN, Truong Khanh CELL: A compositional verification framework |
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This paper presents CELL, a comprehensive and extensible framework for compositional verification of concurrent and real-time systems based on commonly used semantic models. For each semantic model, CELL offers three libraries, i.e., compositional verification paradigms, learning algorithms and model checking methods to support various state-of-the-art compositional verification approaches. With well-defined APIs, the framework could be applied to build customized model checkers. In addition, each library could be used independently for verification and program analysis purposes. We have built three model checkers with CELL. The experimental results show that the performance of these model checkers can offer similar or often better performance compared to the state-of-the-art verification tools. |
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JI, Kun LIU, Yang SUN, Jun SUN, Jun DONG, Jin Song NGUYEN, Truong Khanh |
author_facet |
JI, Kun LIU, Yang SUN, Jun SUN, Jun DONG, Jin Song NGUYEN, Truong Khanh |
author_sort |
JI, Kun |
title |
CELL: A compositional verification framework |
title_short |
CELL: A compositional verification framework |
title_full |
CELL: A compositional verification framework |
title_fullStr |
CELL: A compositional verification framework |
title_full_unstemmed |
CELL: A compositional verification framework |
title_sort |
cell: a compositional verification framework |
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Institutional Knowledge at Singapore Management University |
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2013 |
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https://ink.library.smu.edu.sg/sis_research/4997 https://ink.library.smu.edu.sg/context/sis_research/article/6000/viewcontent/cell.pdf |
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