Accelerating Object-Sensitive Pointer Analysis by Exploiting Object Containment and Reachability (Artifact)

Authors Dongjie He, Jingbo Lu, Yaoqing Gao, Jingling Xue

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Author Details

Dongjie He
  • University of New South Wales, Sydney, Australia
Jingbo Lu
  • University of New South Wales, Sydney, Australia
Yaoqing Gao
  • Huawei, Toronto, Canada
Jingling Xue
  • University of New South Wales, Sydney, Australia


We thank the reviewers for their constructive comments. This work is supported by an ARC DP grant DP180104069 and a UNSW-Huawei research grant (YBN2019105002).

Cite AsGet BibTex

Dongjie He, Jingbo Lu, Yaoqing Gao, and Jingling Xue. Accelerating Object-Sensitive Pointer Analysis by Exploiting Object Containment and Reachability (Artifact). In Special Issue of the 35th European Conference on Object-Oriented Programming (ECOOP 2021). Dagstuhl Artifacts Series (DARTS), Volume 7, Issue 2, pp. 12:1-12:3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)



Object-sensitive pointer analysis for an object-oriented program can be accelerated if context-sensitivity can be selectively applied to some precision-critical variables/objects in the program. Existing pre-analyses, which are performed to make such selections, either preserve precision but achieve limited speedups by reasoning about all the possible value flows in the program conservatively or achieve greater speedups but sacrifice precision (often unduly) by examining only some but not all the value flows in the program heuristically. In this paper, we introduce a new approach, named Turner, that represents a sweet spot between the two existing ones, as it is designed to enable object-sensitive pointer analysis to run significantly faster than the former approach and achieve significantly better precision than the latter approach. Turner is simple, lightweight yet effective due to two novel aspects in its design. First, we exploit a key observation that some precision-uncritical objects can be approximated based on the object-containment relationship pre-established (by applying Andersen’s analysis). This approximation introduces a small degree yet the only source of imprecision into Turner. Second, leveraging this initial approximation, we introduce a simple DFA to reason about object reachability for a method intra-procedurally from its entry to its exit along all the possible value flows established by its statements to finalize its precision-critical variables/objects identified. We have validated Turner with an implementation in Soot against the state of the art using a set of 12 popular Java benchmarks and applications.

Subject Classification

ACM Subject Classification
  • Theory of computation → Program analysis
  • Object-Sensitive Pointer Analysis
  • CFL Reachability
  • Object Containment


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  2. Ondřej Lhoták and Laurie Hendren. Scaling Java points-to analysis using spark. In International Conference on Compiler Construction, pages 153-169, Berlin, Heidelberg, 2003. Springer Berlin Heidelberg. URL:
  3. Yue Li, Tian Tan, Anders Møller, and Yannis Smaragdakis. Precision-guided context sensitivity for pointer analysis. Proceedings of the ACM on Programming Languages, 2(OOPSLA):1-29, 2018. URL:
  4. Jingbo Lu and Jingling Xue. Precision-preserving yet fast object-sensitive pointer analysis with partial context sensitivity. Proceedings of the ACM on Programming Languages, 3(OOPSLA):1-29, 2019. URL:
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