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Abstraction refinement guided by a learnt probabilistic model

Abstract:

The core challenge in designing an effective static program analysis is to find a good program abstraction -- one that retains only details relevant to a given query. In this paper, we present a new approach for automatically finding such an abstraction. Our approach uses a pessimistic strategy, which can optionally use guidance from a probabilistic model. Our approach applies to parametric static analyses implemented in Datalog, and is based on counterexample-guided abstraction refinement. F...

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Publication status:
Accepted
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Department:
Oxford, MPLS, Computer Science
Role:
Author
More by this author
Institution:
University of Oxford
Department:
Oxford, MPLS, Computer Science
Role:
Author
Publisher:
Association for Computing Machinery Publisher's website
Publication date:
2015-11-05
URN:
uuid:6f680b24-9a9d-4ef0-97da-af8bbf24a899
Source identifiers:
572427
Local pid:
pubs:572427

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