Abstraction and Learning for Infinite-State Compositional Verification

Dimitra Giannakopoulou
(NASA Ames)
Corina S. Păsăreanu
(Carnegie Mellon Silicon Valley)

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.

In Anindya Banerjee, Olivier Danvy, Kyung-Goo Doh and John Hatcliff: Semantics, Abstract Interpretation, and Reasoning about Programs: Essays Dedicated to David A. Schmidt on the Occasion of his Sixtieth Birthday (Festschrift for Dave Schmidt), Manhattan, Kansas, USA, 19-20th September 2013, Electronic Proceedings in Theoretical Computer Science 129, pp. 211–228.
Published: 19th September 2013.

ArXived at: http://dx.doi.org/10.4204/EPTCS.129.13 bibtex PDF
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