Qilin: A New Framework for Supporting Fine-Grained Context-Sensitivity in Java Pointer Analysis (Artifact)

Authors Dongjie He, Jingbo Lu, Jingling Xue

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Dongjie He
  • The University of New South Wales, Sydney, Australia
Jingbo Lu
  • The University of New South Wales, Sydney, Australia
Jingling Xue
  • The University of New South Wales, Sydney, Australia


We thank all the reviewers for their constructive comments.

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Dongjie He, Jingbo Lu, and Jingling Xue. Qilin: A New Framework for Supporting Fine-Grained Context-Sensitivity in Java Pointer Analysis (Artifact). In Special Issue of the 36th European Conference on Object-Oriented Programming (ECOOP 2022). Dagstuhl Artifacts Series (DARTS), Volume 8, Issue 2, pp. 6:1-6:3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)


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The artifact has been evaluated as described in the ECOOP 2022 Call for Artifacts and the ACM Artifact Review and Badging Policy


Existing whole-program context-sensitive pointer analysis frameworks for Java, which were open-sourced over one decade ago, were designed and implemented to support only method-level context-sensitivity (where all the variables/objects in a method are qualified by a common context abstraction representing a context under which the method is analyzed). We introduce Qilin as a generalized (modern) alternative, which will be open-sourced soon on GitHub, to support the current research trend on exploring fine-grained context-sensitivity (including variable-level context-sensitivity where different variables/objects in a method can be analyzed under different context abstractions at the variable level), precisely, efficiently, and modularly. To meet these four design goals, Qilin is developed as an imperative framework (implemented in Java) consisting of a fine-grained pointer analysis kernel with parameterized context-sensitivity that supports on-the-fly call graph construction and exception analysis, solved iteratively based on a new carefully-crafted incremental worklist-based constraint solver, on top of its handlers for complex Java features. We have evaluated Qilin extensively using a set of 12 representative Java programs (popularly used in the literature). For method-level context-sensitive analyses, we compare Qilin with Doop (a declarative framework that defines the state-of-the-art), Qilin yields logically the same precision but more efficiently (e.g., 2.4x faster for four typical baselines considered, on average). For fine-grained context-sensitive analyses (which are not currently supported by open-source Java pointer analysis frameworks such as Doop), we show that Qilin allows seven recent approaches to be instantiated effectively in our parameterized framework, requiring additionally only an average of 50 LOC each.

Subject Classification

ACM Subject Classification
  • Theory of computation → Program analysis
  • Pointer Analysis
  • Fine-Grained Context Sensitivity


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