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Pipelining bottom-up data flow analysis

Authors:

Charles ZhangAuthors Info & Claims

ICSE '20: Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering

Pages 835 - 847

https://doi.org/10.1145/3377811.3380425

Published: 01 October 2020 Publication History

Abstract

Bottom-up program analysis has been traditionally easy to parallelize because functions without caller-callee relations can be analyzed independently. However, such function-level parallelism is significantly limited by the calling dependence - functions with caller-callee relations have to be analyzed sequentially because the analysis of a function depends on the analysis results, a.k.a., function summaries, of its callees. We observe that the calling dependence can be relaxed in many cases and, as a result, the parallelism can be improved. In this paper, we present Coyote, a framework of bottom-up data flow analysis, in which the analysis task of each function is elaborately partitioned into multiple sub-tasks to generate pipelineable function summaries. These sub-tasks are pipelined and run in parallel, even though the calling dependence exists. We formalize our idea under the IFDS/IDE framework and have implemented an application to checking null-dereference bugs and taint issues in C/C++ programs. We evaluate Coyote on a series of standard benchmark programs and open-source software systems, which demonstrates significant speedup over a conventional parallel design.

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Cited By

Xu RPeng HYang KMao X(2024)Exploring Scalability of Value-Flow Graph ConstructionProceedings of the 2024 4th International Conference on Artificial Intelligence, Automation and High Performance Computing10.1145/3690931.3690951(112-117)Online publication date: 19-Jul-2024
https://dl.acm.org/doi/10.1145/3690931.3690951
Cheng XRen JSui Y(2024)Fast Graph Simplification for Path-Sensitive Typestate Analysis through Tempo-Spatial Multi-Point SlicingProceedings of the ACM on Software Engineering10.1145/36437491:FSE(494-516)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3643749
Tang WDong DLi SWang CYao PZhou JZhang C(2024) Octopus: Scaling Value-Flow Analysis via Parallel Collection of Realizable Path ConditionsACM Transactions on Software Engineering and Methodology10.1145/363274333:3(1-33)Online publication date: 24-Jan-2024
https://dl.acm.org/doi/10.1145/3632743
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Index Terms

Pipelining bottom-up data flow analysis
1. Software and its engineering
  1. Software creation and management
    1. Software verification and validation

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cover image ACM Conferences

ICSE '20: Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering

June 2020

1640 pages

ISBN:9781450371216

DOI:10.1145/3377811

General Chairs:
Gregg Rothermel
North Carolina State University
,
Doo-Hwan Bae
KAIST, South Korea

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Published: 01 October 2020

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ICSE '20: 42nd International Conference on Software Engineering

June 27 - July 19, 2020

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Cited By

Xu RPeng HYang KMao X(2024)Exploring Scalability of Value-Flow Graph ConstructionProceedings of the 2024 4th International Conference on Artificial Intelligence, Automation and High Performance Computing10.1145/3690931.3690951(112-117)Online publication date: 19-Jul-2024
https://dl.acm.org/doi/10.1145/3690931.3690951
Cheng XRen JSui Y(2024)Fast Graph Simplification for Path-Sensitive Typestate Analysis through Tempo-Spatial Multi-Point SlicingProceedings of the ACM on Software Engineering10.1145/36437491:FSE(494-516)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3643749
Tang WDong DLi SWang CYao PZhou JZhang C(2024) Octopus: Scaling Value-Flow Analysis via Parallel Collection of Realizable Path ConditionsACM Transactions on Software Engineering and Methodology10.1145/363274333:3(1-33)Online publication date: 24-Jan-2024
https://dl.acm.org/doi/10.1145/3632743
Wu RHe YHuang JWang CTang WShi QXiao XZhang CRoychoudhury APaiva AAbreu RStorey M(2024)LibAlchemy: A Two-Layer Persistent Summary Design for Taming Third-Party Libraries in Static Bug-Finding SystemsProceedings of the IEEE/ACM 46th International Conference on Software Engineering10.1145/3597503.3639132(1-13)Online publication date: 20-May-2024
https://dl.acm.org/doi/10.1145/3597503.3639132
Liu JLiu JDi PWu DZheng HLiu AXue JJust RFraser G(2023)Hybrid Inlining: A Framework for Compositional and Context-Sensitive Static AnalysisProceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3597926.3598042(114-126)Online publication date: 12-Jul-2023
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Wang XZuo ZBu LZhao JGrundy JPollock LPenta M(2023)DStream: A Streaming-Based Highly Parallel IFDS FrameworkProceedings of the 45th International Conference on Software Engineering10.1109/ICSE48619.2023.00208(2488-2500)Online publication date: 14-May-2023
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Utture APalsberg JDwyer MDamian DZeller A(2022)Fast and precise application code analysis using a partial libraryProceedings of the 44th International Conference on Software Engineering10.1145/3510003.3510046(934-945)Online publication date: 21-May-2022
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Shi QYao PWu RZhang CFreund SYahav E(2021)Path-sensitive sparse analysis without path conditionsProceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation10.1145/3453483.3454086(930-943)Online publication date: 19-Jun-2021
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Huihui CHongwei Z(2021)Optimizing demand‐driven null dereference verification via merging branchesExpert Systems10.1111/exsy.1270739:6Online publication date: 27-May-2021
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Zhang YChen LNie XZhang ZWei HShi G(2021)Accelerating Data-Flow Analysis with Full-Partitioning2021 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)10.1109/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00184(1345-1352)Online publication date: Sep-2021
https://doi.org/10.1109/ISPA-BDCloud-SocialCom-SustainCom52081.2021.00184
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