research-article

Finding optimum abstractions in parametric dataflow analysis

Authors:

Hongseok YangAuthors Info & Claims

PLDI '13: Proceedings of the 34th ACM SIGPLAN Conference on Programming Language Design and Implementation

Pages 365 - 376

https://doi.org/10.1145/2491956.2462185

Published: 16 June 2013 Publication History

Abstract

We propose a technique to efficiently search a large family of abstractions in order to prove a query using a parametric dataflow analysis. Our technique either finds the cheapest such abstraction or shows that none exists. It is based on counterexample-guided abstraction refinement but applies a novel meta-analysis on abstract counterexample traces to efficiently find abstractions that are incapable of proving the query. We formalize the technique in a generic framework and apply it to two analyses: a type-state analysis and a thread-escape analysis. We demonstrate the effectiveness of the technique on a suite of Java benchmark programs.

References

[1]

T. Ball and S. Rajamani. Bebop: a path-sensitive interprocedural dataflow engine. In Proceedings of the ACM Workshop on Program Analysis For Software Tools and Engineering (PASTE'01), 2001.

Digital Library

[2]

T. Ball and S. Rajamani. The SLAM project: Debugging system software via static analysis. In Proceedings of the 29th ACM Symposium on Principles of Programming Languages (POPL'02), 2002.

Digital Library

[3]

N. Beckman, A. Nori, S. Rajamani, R. Simmons, S. Tetali, and A. Thakur. Proofs from tests. IEEE Trans. Software Eng., 36(4):495--508, 2010.

Digital Library

[4]

P. Cousot and R. Cousot. Refining model checking by abstract interpretation. Autom. Softw. Eng., 6(1):69--95, 1999.

Digital Library

[5]

I. Dillig, T. Dillig, and A. Aiken. Sound, complete and scalable path-sensitive analysis. In Proceedings of the 29th ACM Conference on Programming Language Design and Implementation (PLDI'08),2008.

Digital Library

[6]

I. Dillig, T. Dillig, and A. Aiken. Fluid updates: beyond strong vs. weak updates. In Proceedings of the 19th European Symposium on Programming (ESOP'10), 2010.

Digital Library

[7]

S. Fink, E. Yahav, N. Dor, G. Ramalingam, and E. Geay. Effective typestate verification in the presence of aliasing. ACM Trans. Softw. Eng. Methodol., 17(2), 2008.

Digital Library

[8]

B. Gulavani, T. Henzinger, Y. Kannan, A. Nori, and S. Rajamani. Synergy: a new algorithm for property checking. In Proceedings of the 14th ACM SIGSOFT International Symposium on Foundations of Software Engineering (FSE'06), 2006.

Digital Library

[9]

B. Gulavani, S. Chakraborty, A. Nori, and S. Rajamani. Automatically refining abstract interpretations. In Proceedings of the 14th International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS'08), 2008.

Digital Library

[10]

S. Gulwani and A. Tiwari. Assertion checking unified. In Proceedings of the 8th International Conference on Verification, Model Checking, and Abstract Interpretation (VMCAI'07), 2007.

Digital Library

[11]

S. Gulwani, B. McCloskey, and A. Tiwari. Lifting abstract interpreters to quantified logical domains. In Proceedings of the 35th ACM Symposium on Principles of Programming Language (POPL'08), 2008.

Digital Library

[12]

S. Guyer and C. Lin. Client-driven pointer analysis. In Proceedings of the 10th International Symposium on Static Analysis (SAS'03), 2003.

Digital Library

[13]

T. Henzinger, R. Jhala, R. Majumdar, and K. McMillan. Abstractions from proofs. In Proceedings of the 31st ACM Symposium on Principles of Programming Languages (POPL'04), 2004.

Digital Library

[14]

P. Liang and M. Naik. Scaling abstraction refinement via pruning. In Proceedings of the 32nd ACM Conference on Programming Language Design and Implementation (PLDI'11), 2011.

Digital Library

[15]

P. Liang, O. Tripp, and M. Naik. Learning minimal abstractions. In Proceedings of the 38th ACM Symposium on Principles of Programming Languages (POPL'11), 2011.

Digital Library

[16]

M. Naik. Chord: A static and dynamic program analysis platform for Java. http://code.google.com/p/jchord/.

[17]

M. Naik, H. Yang, G. Castelnuovo, and M. Sagiv. Abstractions from tests. In Proceedings of the 39th ACM Symposium on Principles of Programming Languages (POPL'12), 2012.

Digital Library

[18]

J. Plevyak and A. Chien. Precise concrete type inference for objectoriented languages. In Proceedings of the 9th ACM Conference on Object-Oriented Programming Systems, Languages, and Applications (OOPSLA'94), 1994.

Digital Library

[19]

T. Reps, S. Horwitz, and M. Sagiv. Precise interprocedural dataflow analysis via graph reachability. In Proceedings of the 22nd ACM Symposium on Principles of Programming Languages (POPL'95), 1995.

Digital Library

[20]

T. Reps, S. Schwoon, S. Jha, and D. Melski. Weighted pushdown systems and their application to interprocedural dataflow analysis. Sci. Comput. Program., 58(1-2):206--263, 2005.

Digital Library

[21]

M. Sridharan and R. Bodík. Refinement-based context-sensitive points-to analysis for Java. In Proceedings of the 27th ACM Conference on Programming Language Design and Implementation (PLDI'06), 2006.

Digital Library

[22]

M. Sridharan, D. Gopan, L. Shan, and R. Bodík. Demand-driven points-to analysis for Java. In Proceedings of the 20th ACM Conference on Object-Oriented Programming, Systems, Languages, and Applications (OOPSLA'05), 2005.

Digital Library

[23]

X. Zhang, M. Naik, and H. Yang. Finding optimum abstractions in parametric dataflow analysis. Technical report, Georgia Institute of Technology, 2013. Available at http://pag.gatech.edu/pubs/pldi13.pdf.

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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
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Babu MLemerre MBardin SMarion J(2024)Trace Partitioning as an Optimization ProblemStatic Analysis10.1007/978-3-031-74776-2_2(26-60)Online publication date: 20-Oct-2024
https://dl.acm.org/doi/10.1007/978-3-031-74776-2_2
Park JLee HRyu S(2021)A Survey of Parametric Static AnalysisACM Computing Surveys10.1145/346445754:7(1-37)Online publication date: 18-Jul-2021
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Finding optimum abstractions in parametric dataflow analysis

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cover image ACM Conferences

PLDI '13: Proceedings of the 34th ACM SIGPLAN Conference on Programming Language Design and Implementation

June 2013

546 pages

ISBN:9781450320146

DOI:10.1145/2491956

General Chair:
Hans-J. Boehm
HP Labs
,
Program Chair:
Cormac Flanagan
University of California, Santa Cruz

ACM SIGPLAN Notices Volume 48, Issue 6
PLDI '13
June 2013
515 pages
ISSN:0362-1340
EISSN:1558-1160
DOI:10.1145/2499370
Issue’s Table of Contents

Copyright © 2013 ACM.

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Published: 16 June 2013

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PLDI '13

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PLDI '13: ACM SIGPLAN Conference on Programming Language Design and Implementation

June 16 - 19, 2013

Washington, Seattle, USA

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PLDI '13 Paper Acceptance Rate 46 of 267 submissions, 17%;

Overall Acceptance Rate 406 of 2,067 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
Babu MLemerre MBardin SMarion J(2024)Trace Partitioning as an Optimization ProblemStatic Analysis10.1007/978-3-031-74776-2_2(26-60)Online publication date: 20-Oct-2024
https://dl.acm.org/doi/10.1007/978-3-031-74776-2_2
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
Jeon MLee MOh H(2020)Learning graph-based heuristics for pointer analysis without handcrafting application-specific featuresProceedings of the ACM on Programming Languages10.1145/34282474:OOPSLA(1-30)Online publication date: 13-Nov-2020
https://dl.acm.org/doi/10.1145/3428247
Facchinetti LPalmer ZSmith S(2019)Higher-order Demand-driven Program AnalysisACM Transactions on Programming Languages and Systems10.1145/331034041:3(1-53)Online publication date: 2-Jul-2019
https://dl.acm.org/doi/10.1145/3310340
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
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
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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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Zhang XSi XNaik MShpeisman TGottschlich J(2017)Combining the logical and the probabilistic in program analysisProceedings of the 1st ACM SIGPLAN International Workshop on Machine Learning and Programming Languages10.1145/3088525.3088563(27-34)Online publication date: 18-Jun-2017
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