Please use this identifier to cite or link to this item: https://doi.org/10.1109/TSE.2013.57
DC FieldValue
dc.titleLearning assumptions for compositionalverification of timed systems
dc.contributor.authorLin, S.-W.
dc.contributor.authorAndre, E.
dc.contributor.authorLiu, Y.
dc.contributor.authorSun, J.
dc.contributor.authorDong, J.S.
dc.date.accessioned2014-07-04T03:09:51Z
dc.date.available2014-07-04T03:09:51Z
dc.date.issued2014
dc.identifier.citationLin, S.-W., Andre, E., Liu, Y., Sun, J., Dong, J.S. (2014). Learning assumptions for compositionalverification of timed systems. IEEE Transactions on Software Engineering 40 (2) : 137-153. ScholarBank@NUS Repository. https://doi.org/10.1109/TSE.2013.57
dc.identifier.issn00985589
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/77877
dc.description.abstractCompositional 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. © 2014 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/TSE.2013.57
dc.sourceScopus
dc.subjectAutomatic assume-guarantee reasoning
dc.subjectmodel checking
dc.subjecttimed systems
dc.typeArticle
dc.contributor.departmentCOMPUTER SCIENCE
dc.contributor.departmentTEMASEK LABORATORIES
dc.description.doi10.1109/TSE.2013.57
dc.description.sourcetitleIEEE Transactions on Software Engineering
dc.description.volume40
dc.description.issue2
dc.description.page137-153
dc.description.codenIESED
dc.identifier.isiut000334666000003
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