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Data-driven context-sensitivity for points-to analysis

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Hakjoo OhAuthors Info & Claims

Proceedings of the ACM on Programming Languages, Volume 1, Issue OOPSLA

Article No.: 100, Pages 1 - 28

https://doi.org/10.1145/3133924

Published: 12 October 2017 Publication History

Abstract

We present a new data-driven approach to achieve highly cost-effective context-sensitive points-to analysis for Java. While context-sensitivity has greater impact on the analysis precision and performance than any other precision-improving techniques, it is difficult to accurately identify the methods that would benefit the most from context-sensitivity and decide how much context-sensitivity should be used for them. Manually designing such rules is a nontrivial and laborious task that often delivers suboptimal results in practice. To overcome these challenges, we propose an automated and data-driven approach that learns to effectively apply context-sensitivity from codebases. In our approach, points-to analysis is equipped with a parameterized and heuristic rules, in disjunctive form of properties on program elements, that decide when and how much to apply context-sensitivity. We present a greedy algorithm that efficiently learns the parameter of the heuristic rules. We implemented our approach in the Doop framework and evaluated using three types of context-sensitive analyses: conventional object-sensitivity, selective hybrid object-sensitivity, and type-sensitivity. In all cases, experimental results show that our approach significantly outperforms existing techniques.

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Cited By

Oh WOh HFilkov VRay BZhou M(2024)Towards Effective Static Type-Error Detection for PythonProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695545(1808-1820)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695545
Yan ZZhang XDi P(2024)Scaling Abstraction Refinement for Program Analyses in Datalog using Graph Neural NetworksProceedings of the ACM on Programming Languages10.1145/36897658:OOPSLA2(1532-1560)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3689765
Erhard JSchinabeck JSchwarz MSeidl HMonat RRubio-González C(2024)When to Stop Going Down the Rabbit Hole: Taming Context-Sensitivity on the FlyProceedings of the 13th ACM SIGPLAN International Workshop on the State Of the Art in Program Analysis10.1145/3652588.3663321(35-44)Online publication date: 20-Jun-2024
https://dl.acm.org/doi/10.1145/3652588.3663321
Show More Cited By

Index Terms

Data-driven context-sensitivity for points-to analysis
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
2. Theory of computation
  1. Semantics and reasoning
    1. Program reasoning
      1. Program analysis

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Published In

cover image Proceedings of the ACM on Programming Languages

Proceedings of the ACM on Programming Languages Volume 1, Issue OOPSLA

October 2017

1786 pages

EISSN:2475-1421

DOI:10.1145/3152284

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Copyright © 2017 ACM.

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Publication History

Published: 12 October 2017

Published in PACMPL Volume 1, Issue OOPSLA

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Cited By

Oh WOh HFilkov VRay BZhou M(2024)Towards Effective Static Type-Error Detection for PythonProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695545(1808-1820)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695545
Yan ZZhang XDi P(2024)Scaling Abstraction Refinement for Program Analyses in Datalog using Graph Neural NetworksProceedings of the ACM on Programming Languages10.1145/36897658:OOPSLA2(1532-1560)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3689765
Erhard JSchinabeck JSchwarz MSeidl HMonat RRubio-González C(2024)When to Stop Going Down the Rabbit Hole: Taming Context-Sensitivity on the FlyProceedings of the 13th ACM SIGPLAN International Workshop on the State Of the Art in Program Analysis10.1145/3652588.3663321(35-44)Online publication date: 20-Jun-2024
https://dl.acm.org/doi/10.1145/3652588.3663321
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
Li HTan TLi YLu JMeng HCao LHuang YLi LGao LDi PLin LCui C(2024)Generic Sensitivity: Generics-Guided Context Sensitivity for Pointer AnalysisIEEE Transactions on Software Engineering10.1109/TSE.2024.337764550:5(1144-1162)Online publication date: May-2024
https://doi.org/10.1109/TSE.2024.3377645
Cai YZhang C(2023)A Cocktail Approach to Practical Call Graph ConstructionProceedings of the ACM on Programming Languages10.1145/36228337:OOPSLA2(1001-1033)Online publication date: 16-Oct-2023
https://dl.acm.org/doi/10.1145/3622833
He DGui YLi WTao YZou CSui YXue J(2023)A Container-Usage-Pattern-Based Context Debloating Approach for Object-Sensitive Pointer AnalysisProceedings of the ACM on Programming Languages10.1145/36228327:OOPSLA2(971-1000)Online publication date: 16-Oct-2023
https://dl.acm.org/doi/10.1145/3622832
Hueckelheim JDoerfert J(2023)ORAQL — Optimistic Responses to Alias Queries in LLVMProceedings of the 52nd International Conference on Parallel Processing10.1145/3605573.3605644(655-664)Online publication date: 7-Aug-2023
https://dl.acm.org/doi/10.1145/3605573.3605644
Chow YSchäfer MPradel MJust RFraser G(2023)Beware of the Unexpected: Bimodal Taint AnalysisProceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3597926.3598050(211-222)Online publication date: 12-Jul-2023
https://dl.acm.org/doi/10.1145/3597926.3598050
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
https://dl.acm.org/doi/10.1145/3597926.3598042
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