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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:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1145/2837614

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Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
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Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
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Name:
Government of Korea
Funding agency for:
Yang, H
Grant:
Development of Vulnerability Discovery Technologiesfor IoT Software Security
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Name:
Engineering and Physical Sciences Research Council
Grant:
EP/H008373/2
Publisher:
Association for Computing Machinery
Host title:
POPL '16: Proceedings of the 43rd Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages
Pages:
485–498
Publication date:
2016-01-11
Acceptance date:
2015-10-05
Event title:
POPL '16: The 43rd Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages
Event location:
FL, St. Petersburg, USA
Event website:
http://conf.researchr.org/home/POPL-2016
Event start date:
2016-01-20
Event end date:
2016-01-22
DOI:
ISBN:
9781450335492
Language:
English
Keywords:
Pubs id:
pubs:572427
UUID:
uuid:6f680b24-9a9d-4ef0-97da-af8bbf24a899
Local pid:
pubs:572427
Source identifiers:
572427
Deposit date:
2015-11-12

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