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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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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.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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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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