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• W2062241373 abstract "Despite many advances that enable the application of model checking techniques to the verification of large systems, the state-explosion problem remains the main challenge for scalability. Compositional verification addresses this challenge by decomposing the verification of a large system into the verification of its components. Recent techniques use learning-based approaches to automate compositional verification based on the assume-guarantee style reasoning. However, these techniques are only applicable to finite-state systems. In this work, we propose a new framework that interleaves abstraction and learning to perform automated compositional verification of infinite-state systems. We also discuss the role of learning and abstraction in the related context of interface generation for infinite-state components." @default.
• W2062241373 created "2016-06-24" @default.
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• W2062241373 date "2013-09-19" @default.
• W2062241373 modified "2023-10-04" @default.
• W2062241373 title "Abstraction and Learning for Infinite-State Compositional Verification" @default.
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• W2062241373 doi "https://doi.org/10.4204/eptcs.129.13" @default.
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