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Proceedings of the ACM on Programming Languages
Article . 2022 . Peer-reviewed
License: CC BY
Data sources: Crossref
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https://dx.doi.org/10.48550/ar...
Article . 2021
License: arXiv Non-Exclusive Distribution
Data sources: Datacite
DBLP
Article . 2022
Data sources: DBLP
DBLP
Article . 2021
Data sources: DBLP
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Synthesizing abstract transformers

Authors: Pankaj Kumar Kalita; Sujit Kumar Muduli; Loris D'Antoni; Thomas W. Reps; Subhajit Roy 0001;

Synthesizing abstract transformers

Abstract

This paper addresses the problem of creating abstract transformers automatically. The method we present automates the construction of static analyzers in a fashion similar to the way yacc automates the construction of parsers. Our method treats the problem as a program-synthesis problem. The user provides specifications of (i) the concrete semantics of a given operation op , (ii) the abstract domain A to be used by the analyzer, and (iii) the semantics of a domain-specific language L in which the abstract transformer is to be expressed. As output, our method creates an abstract transformer for op in abstract domain A , expressed in L (an “ L -transformer for op over A ”). Moreover, the abstract transformer obtained is a most-precise L -transformer for op over A ; that is, there is no other L -transformer for op over A that is strictly more precise. We implemented our method in a tool called AMURTH. We used AMURTH to create sets of replacement abstract transformers for those used in two existing analyzers, and obtained essentially identical performance. However, when we compared the existing transformers with the transformers obtained using AMURTH, we discovered that four of the existing transformers were unsound, which demonstrates the risk of using manually created transformers.

Keywords

FOS: Computer and information sciences, Computer Science - Programming Languages, Programming Languages (cs.PL)

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    influence
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    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
20
Top 10%
Top 10%
Top 10%
Green
Published in a Diamond OA journal