research-article

Abstractions from tests

Authors:

Ghila Castelnuovo,

Mooly SagivAuthors Info & Claims

POPL '12: Proceedings of the 39th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages

Pages 373 - 386

https://doi.org/10.1145/2103656.2103701

Published: 25 January 2012 Publication History

Abstract

We present a framework for leveraging dynamic analysis to find good abstractions for static analysis. A static analysis in our framework is parametrised. Our main insight is to directly and efficiently compute from a concrete trace, a necessary condition on the parameter configurations to prove a given query, and thereby prune the space of parameter configurations that the static analysis must consider. We provide constructive algorithms for two instance analyses in our framework: a flow- and context-sensitive thread-escape analysis and a flow- and context-insensitive points-to analysis. We show the efficacy of these analyses, and our approach, on six Java programs comprising two million bytecodes: the thread-escape analysis resolves 80% of queries on average, disproving 28% and proving 52%; the points-to analysis resolves 99% of queries on average, disproving 29% and proving 70%.

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Cited By

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
Chen THeo KRaghothaman MSpinellis DGousios GChechik MDi Penta M(2021)Boosting static analysis accuracy with instrumented test executionsProceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3468264.3468626(1154-1165)Online publication date: 20-Aug-2021
https://dl.acm.org/doi/10.1145/3468264.3468626
Park JLee HRyu S(2021)A Survey of Parametric Static AnalysisACM Computing Surveys10.1145/346445754:7(1-37)Online publication date: 18-Jul-2021
https://dl.acm.org/doi/10.1145/3464457
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Abstractions from tests

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cover image ACM Conferences

POPL '12: Proceedings of the 39th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages

January 2012

602 pages

ISBN:9781450310833

DOI:10.1145/2103656

General Chair:
John Field
Google, USA
,
Program Chair:
Michael Hicks
University of Maryland, College Park, USA

ACM SIGPLAN Notices Volume 47, Issue 1
POPL '12
January 2012
569 pages
ISSN:0362-1340
EISSN:1558-1160
DOI:10.1145/2103621
Issue’s Table of Contents

Copyright © 2012 ACM.

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Published: 25 January 2012

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POPL '12: The 39th Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages

January 25 - 27, 2012

PA, Philadelphia, USA

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Overall Acceptance Rate 824 of 4,130 submissions, 20%

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Cited By

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
Chen THeo KRaghothaman MSpinellis DGousios GChechik MDi Penta M(2021)Boosting static analysis accuracy with instrumented test executionsProceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3468264.3468626(1154-1165)Online publication date: 20-Aug-2021
https://dl.acm.org/doi/10.1145/3468264.3468626
Park JLee HRyu S(2021)A Survey of Parametric Static AnalysisACM Computing Surveys10.1145/346445754:7(1-37)Online publication date: 18-Jul-2021
https://dl.acm.org/doi/10.1145/3464457
Heo KOh HYang HAtlee JBultan TWhittle J(2019)Resource-aware program analysis via online abstraction coarseningProceedings of the 41st International Conference on Software Engineering10.1109/ICSE.2019.00027(94-104)Online publication date: 25-May-2019
https://dl.acm.org/doi/10.1109/ICSE.2019.00027
Dimovski ABrabrand CWąsowski A(2019)Finding suitable variability abstractions for lifted analysisFormal Aspects of Computing10.1007/s00165-019-00479-y31:2(231-259)Online publication date: 1-Apr-2019
https://dl.acm.org/doi/10.1007/s00165-019-00479-y
Heo KOh HYang HYi K(2018)Adaptive Static Analysis via Learning with Bayesian OptimizationACM Transactions on Programming Languages and Systems10.1145/312113540:4(1-37)Online publication date: 16-Nov-2018
https://dl.acm.org/doi/10.1145/3121135
Bastani OSharma RAiken ALiang P(2017)Synthesizing program input grammarsACM SIGPLAN Notices10.1145/3140587.306234952:6(95-110)Online publication date: 14-Jun-2017
https://dl.acm.org/doi/10.1145/3140587.3062349
Chae KOh HHeo KYang H(2017)Automatically generating features for learning program analysis heuristics for C-like languagesProceedings of the ACM on Programming Languages10.1145/31339251:OOPSLA(1-25)Online publication date: 12-Oct-2017
https://dl.acm.org/doi/10.1145/3133925
Zhang XGrigore RSi XNaik M(2017)Effective interactive resolution of static analysis alarmsProceedings of the ACM on Programming Languages10.1145/31338811:OOPSLA(1-30)Online publication date: 12-Oct-2017
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Ferles KWüstholz VChristakis MDillig IBodden ESchäfer WDeursen AZisman A(2017)Failure-directed program trimmingProceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering10.1145/3106237.3106249(174-185)Online publication date: 21-Aug-2017
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