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Return of CFA: call-site sensitivity can be superior to object sensitivity even for object-oriented programs
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Fast Numerical Program Analysis with Reinforcement Learning
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Correlation Tracking for Points-To Analysis of JavaScript
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Liveness-Based Pointer Analysis
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Making k-Object-Sensitive Pointer Analysis More Precise with Still k-Limiting
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Machine-Learning-Guided Selectively Unsound Static Analysis
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Resource-Aware Program Analysis Via Online Abstraction Coarsening
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Parameterized object sensitivity for points-to analysis for Java
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Demand-driven points-to analysis for Java
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Evaluating the impact of context-sensitivity on Andersen's algorithm for Java programs
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Effective static race detection for Java
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Refinement-based context-sensitive points-to analysis for Java
inferredcites →https://doi.org/10.1145/1348250.1348255openalex-snapshot
Effective typestate verification in the presence of aliasing
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Merging equivalent contexts for scalable heap-cloning-based context-sensitive points-to analysis
inferredcites →https://doi.org/10.1145/1391984.1391987openalex-snapshot
Evaluating the benefits of context-sensitive points-to analysis using a BDD-based implementation
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Strictly declarative specification of sophisticated points-to analyses
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Resolving and exploiting the k -CFA paradox
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Learning minimal abstractions
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Scaling abstraction refinement via pruning
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Hybrid context-sensitivity for points-to analysis
inferredcites →https://doi.org/10.1145/2594291.2594318openalex-snapshot
Selective context-sensitivity guided by impact pre-analysis
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On abstraction refinement for program analyses in Datalog
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Context transformations for pointer analysis
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Data-driven context-sensitivity for points-to analysis
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Scalability-first pointer analysis with self-tuning context-sensitivity
inferredcites →https://doi.org/10.1145/3276510openalex-snapshot
Precise and scalable points-to analysis via data-driven context tunneling
inferredcites →https://doi.org/10.1145/3276511openalex-snapshot
Precision-guided context sensitivity for pointer analysis
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A Machine-Learning Algorithm with Disjunctive Model for Data-Driven Program Analysis
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A Principled Approach to Selective Context Sensitivity for Pointer Analysis
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Learning graph-based heuristics for pointer analysis without handcrafting application-specific features
inferredcites →https://doi.org/10.1145/566172.566174openalex-snapshot
Parameterized object sensitivity for points-to and side-effect analyses for Java
inferredcites →https://doi.org/10.1145/996841.996859openalex-snapshot
Cloning-based context-sensitive pointer alias analysis using binary decision diagrams
inferredcites →https://doi.org/10.4230/lipics.ecoop.2015.712openalex-snapshot
Adaptive Context-sensitive Analysis for JavaScript
inferredcites →https://doi.org/10.4230/lipics.ecoop.2016.22openalex-snapshot
Boomerang: Demand-Driven Flow- and Context-Sensitive Pointer Analysis for Java
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Scikit-learn: Machine Learning in Python
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Automatic Generation and Reuse of Precise Library Summaries for Object-Sensitive Pointer Analysis
inferred← citeshttps://doi.org/10.1109/icse55347.2025.00227openalex-snapshot
Module-Aware Context Sensitive Pointer Analysis
inferred← citeshttps://doi.org/10.1109/tse.2024.3377645openalex-snapshot
Generic Sensitivity: Generics-Guided Context Sensitivity for Pointer Analysis
inferred← citeshttps://doi.org/10.1145/3540250.3549122openalex-snapshot
Generic sensitivity: customizing context-sensitive pointer analysis for generics
inferred← citeshttps://doi.org/10.1145/3591242openalex-snapshot
Context Sensitivity without Contexts: A Cut-Shortcut Approach to Fast and Precise Pointer Analysis
inferred← citeshttps://doi.org/10.1145/3597926.3598120openalex-snapshot
Tai-e: A Developer-Friendly Static Analysis Framework for Java by Harnessing the Good Designs of Classics
inferred← citeshttps://doi.org/10.1145/3622832openalex-snapshot
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On the Effectiveness of Machine Learning-based Call Graph Pruning: An Empirical Study
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Learning Abstraction Selection for Bayesian Program Analysis
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TIPS: Tracking Integer-Pointer Value Flows for C++ Member Function Pointers
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Fast Constraint Synthesis for C++ Function Templates
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The Incredible Shrinking Context... in a Decompiler Near You
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Universal Scalability in Declarative Program Analysis (with Choice-Based Combination Pruning)
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Cut-Shortcut Whole-Program Pointer Analysis: Re-Imagining Context-Sensitivity without Contexts
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