research-article

Learning minimal abstractions

Authors:

Mayur NaikAuthors Info & Claims

POPL '11: Proceedings of the 38th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages

Pages 31 - 42

https://doi.org/10.1145/1926385.1926391

Published: 26 January 2011 Publication History

Abstract

Static analyses are generally parametrized by an abstraction which is chosen from a family of abstractions. We are interested in flexible families of abstractions with many parameters, as these families can allow one to increase precision in ways tailored to the client without sacrificing scalability. For example, we consider k-limited points-to analyses where each call site and allocation site in a program can have a different k value. We then ask a natural question in this paper: What is the minimal (coarsest) abstraction in a given family which is able to prove a set of queries? In addressing this question, we make the following two contributions: (i) We introduce two machine learning algorithms for efficiently finding a minimal abstraction; and (ii) for a static race detector backed by a k-limited points-to analysis, we show empirically that minimal abstractions are actually quite coarse: It suffices to provide context/object sensitivity to a very small fraction (0.4-2.3%) of the sites to yield equally precise results as providing context/object sensitivity uniformly to all sites.

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Index Terms

Learning minimal abstractions

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cover image ACM Conferences

POPL '11: Proceedings of the 38th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages

January 2011

652 pages

ISBN:9781450304900

DOI:10.1145/1926385

General Chair:
Thomas Ball
Microsoft Research, USA
,
Program Chair:
Mooly Sagiv
Tel Aviv University, Israel

ACM SIGPLAN Notices Volume 46, Issue 1
POPL '11
January 2011
624 pages
ISSN:0362-1340
EISSN:1558-1160
DOI:10.1145/1925844
Issue’s Table of Contents

Copyright © 2011 ACM.

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Published: 26 January 2011

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POPL '11: The 38th Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages

January 26 - 28, 2011

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https://dl.acm.org/doi/10.5555/3692070.3694168
Zhang YShi YZhang X(2024)Learning Abstraction Selection for Bayesian Program AnalysisProceedings of the ACM on Programming Languages10.1145/36498458:OOPSLA1(954-982)Online publication date: 29-Apr-2024
https://dl.acm.org/doi/10.1145/3649845
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https://dl.acm.org/doi/10.1007/978-3-031-74776-2_2
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