6 Search Results for "Lu, Jingbo"

Document
DOI: 10.4230/LIPIcs.ECOOP.2024.18
A CFL-Reachability Formulation of Callsite-Sensitive Pointer Analysis with Built-In On-The-Fly Call Graph Construction

Authors: Dongjie He, Jingbo Lu, and Jingling Xue

Published in: LIPIcs, Volume 313, 38th European Conference on Object-Oriented Programming (ECOOP 2024)

Abstract
In object-oriented languages, the traditional CFL-reachability formulation for k-callsite-sensitive pointer analysis (kCFA) focuses on modeling field accesses and calling contexts, but it relies on a separate algorithm for call graph construction. This division can result in a loss of precision in kCFA, a problem that persists even when using the most precise call graphs, whether pre-constructed or generated on the fly. Moreover, pre-analyses based on this framework aiming to improve the efficiency of kCFA may inadvertently reduce its precision, due to the framework’s lack of native call graph construction, essential for precise analysis. Addressing this gap, this paper introduces a novel CFL-reachability formulation of kCFA for Java, uniquely integrating on-the-fly call graph construction. This advancement not only addresses the precision loss inherent in the traditional CFL-reachability-based approach but also enhances its overall applicability. In a significant secondary contribution, we present the first precision-preserving pre-analysis to accelerate kCFA. This pre-analysis leverages selective context sensitivity to improve the efficiency of kCFA without sacrificing its precision. Collectively, these contributions represent a substantial step forward in pointer analysis, offering both theoretical and practical advancements that could benefit future developments in the field.
Cite as

Dongjie He, Jingbo Lu, and Jingling Xue. A CFL-Reachability Formulation of Callsite-Sensitive Pointer Analysis with Built-In On-The-Fly Call Graph Construction. In 38th European Conference on Object-Oriented Programming (ECOOP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 313, pp. 18:1-18:29, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

Copy BibTex To Clipboard

@InProceedings{he_et_al:LIPIcs.ECOOP.2024.18,
  author =	{He, Dongjie and Lu, Jingbo and Xue, Jingling},
  title =	{{A CFL-Reachability Formulation of Callsite-Sensitive Pointer Analysis with Built-In On-The-Fly Call Graph Construction}},
  booktitle =	{38th European Conference on Object-Oriented Programming (ECOOP 2024)},
  pages =	{18:1--18:29},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-341-6},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{313},
  editor =	{Aldrich, Jonathan and Salvaneschi, Guido},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2024.18},
  URN =		{urn:nbn:de:0030-drops-208674},
  doi =		{10.4230/LIPIcs.ECOOP.2024.18},
  annote =	{Keywords: Pointer Analysis, CFL Reachability, Call Graph Construction}
}
Document
Short Paper
DOI: 10.4230/LIPIcs.CP.2024.38
Frugal Algorithm Selection (Short Paper)

Authors: Erdem Kuş, Özgür Akgün, Nguyen Dang, and Ian Miguel

Published in: LIPIcs, Volume 307, 30th International Conference on Principles and Practice of Constraint Programming (CP 2024)

Abstract
When solving decision and optimisation problems, many competing algorithms (model and solver choices) have complementary strengths. Typically, there is no single algorithm that works well for all instances of a problem. Automated algorithm selection has been shown to work very well for choosing a suitable algorithm for a given instance. However, the cost of training can be prohibitively large due to running candidate algorithms on a representative set of training instances. In this work, we explore reducing this cost by choosing a subset of the training instances on which to train. We approach this problem in three ways: using active learning to decide based on prediction uncertainty, augmenting the algorithm predictors with a timeout predictor, and collecting training data using a progressively increasing timeout. We evaluate combinations of these approaches on six datasets from ASLib and present the reduction in labelling cost achieved by each option.
Cite as

Erdem Kuş, Özgür Akgün, Nguyen Dang, and Ian Miguel. Frugal Algorithm Selection (Short Paper). In 30th International Conference on Principles and Practice of Constraint Programming (CP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 307, pp. 38:1-38:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

Copy BibTex To Clipboard

@InProceedings{kus_et_al:LIPIcs.CP.2024.38,
  author =	{Ku\c{s}, Erdem and Akg\"{u}n, \"{O}zg\"{u}r and Dang, Nguyen and Miguel, Ian},
  title =	{{Frugal Algorithm Selection}},
  booktitle =	{30th International Conference on Principles and Practice of Constraint Programming (CP 2024)},
  pages =	{38:1--38:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-336-2},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{307},
  editor =	{Shaw, Paul},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2024.38},
  URN =		{urn:nbn:de:0030-drops-207239},
  doi =		{10.4230/LIPIcs.CP.2024.38},
  annote =	{Keywords: Algorithm Selection, Active Learning}
}
Document
DOI: 10.4230/LIPIcs.ECOOP.2022.30
Qilin: A New Framework For Supporting Fine-Grained Context-Sensitivity in Java Pointer Analysis

Authors: Dongjie He, Jingbo Lu, and Jingling Xue

Published in: LIPIcs, Volume 222, 36th European Conference on Object-Oriented Programming (ECOOP 2022)

Abstract
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 has been open-sourced 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.
Cite as

Dongjie He, Jingbo Lu, and Jingling Xue. Qilin: A New Framework For Supporting Fine-Grained Context-Sensitivity in Java Pointer Analysis. In 36th European Conference on Object-Oriented Programming (ECOOP 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 222, pp. 30:1-30:29, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

Copy BibTex To Clipboard

@InProceedings{he_et_al:LIPIcs.ECOOP.2022.30,
  author =	{He, Dongjie and Lu, Jingbo and Xue, Jingling},
  title =	{{Qilin: A New Framework For Supporting Fine-Grained Context-Sensitivity in Java Pointer Analysis}},
  booktitle =	{36th European Conference on Object-Oriented Programming (ECOOP 2022)},
  pages =	{30:1--30:29},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-225-9},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{222},
  editor =	{Ali, Karim and Vitek, Jan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2022.30},
  URN =		{urn:nbn:de:0030-drops-162581},
  doi =		{10.4230/LIPIcs.ECOOP.2022.30},
  annote =	{Keywords: Pointer Analysis, Fine-Grained Context Sensitivity}
}
Document
Artifact
DOI: 10.4230/DARTS.8.2.6
Qilin: A New Framework for Supporting Fine-Grained Context-Sensitivity in Java Pointer Analysis (Artifact)

Authors: Dongjie He, Jingbo Lu, and Jingling Xue

Published in: DARTS, Volume 8, Issue 2, Special Issue of the 36th European Conference on Object-Oriented Programming (ECOOP 2022)

Abstract
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.
Cite as

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)

Copy BibTex To Clipboard

@Article{he_et_al:DARTS.8.2.6,
  author =	{He, Dongjie and Lu, Jingbo and Xue, Jingling},
  title =	{{Qilin: A New Framework for Supporting Fine-Grained Context-Sensitivity in Java Pointer Analysis (Artifact)}},
  pages =	{6:1--6:3},
  journal =	{Dagstuhl Artifacts Series},
  ISSN =	{2509-8195},
  year =	{2022},
  volume =	{8},
  number =	{2},
  editor =	{He, Dongjie and Lu, Jingbo and Xue, Jingling},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DARTS.8.2.6},
  URN =		{urn:nbn:de:0030-drops-162040},
  doi =		{10.4230/DARTS.8.2.6},
  annote =	{Keywords: Pointer Analysis, Fine-Grained Context Sensitivity}
}
Document
DOI: 10.4230/LIPIcs.ECOOP.2021.16
Accelerating Object-Sensitive Pointer Analysis by Exploiting Object Containment and Reachability

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

Published in: LIPIcs, Volume 194, 35th European Conference on Object-Oriented Programming (ECOOP 2021)

Abstract
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.
Cite as

Dongjie He, Jingbo Lu, Yaoqing Gao, and Jingling Xue. Accelerating Object-Sensitive Pointer Analysis by Exploiting Object Containment and Reachability. In 35th European Conference on Object-Oriented Programming (ECOOP 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 194, pp. 16:1-16:31, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

Copy BibTex To Clipboard

@InProceedings{he_et_al:LIPIcs.ECOOP.2021.16,
  author =	{He, Dongjie and Lu, Jingbo and Gao, Yaoqing and Xue, Jingling},
  title =	{{Accelerating Object-Sensitive Pointer Analysis by Exploiting Object Containment and Reachability}},
  booktitle =	{35th European Conference on Object-Oriented Programming (ECOOP 2021)},
  pages =	{16:1--16:31},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-190-0},
  ISSN =	{1868-8969},
  year =	{2021},
  volume =	{194},
  editor =	{M{\o}ller, Anders and Sridharan, Manu},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2021.16},
  URN =		{urn:nbn:de:0030-drops-140592},
  doi =		{10.4230/LIPIcs.ECOOP.2021.16},
  annote =	{Keywords: Object-Sensitive Pointer Analysis, CFL Reachability, Object Containment}
}
Document
Artifact
DOI: 10.4230/DARTS.7.2.12
Accelerating Object-Sensitive Pointer Analysis by Exploiting Object Containment and Reachability (Artifact)

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

Published in: DARTS, Volume 7, Issue 2, Special Issue of the 35th European Conference on Object-Oriented Programming (ECOOP 2021)

Abstract
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.
Cite as

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)

Copy BibTex To Clipboard

@Article{he_et_al:DARTS.7.2.12,
  author =	{He, Dongjie and Lu, Jingbo and Gao, Yaoqing and Xue, Jingling},
  title =	{{Accelerating Object-Sensitive Pointer Analysis by Exploiting Object Containment and Reachability (Artifact)}},
  pages =	{12:1--12:3},
  journal =	{Dagstuhl Artifacts Series},
  ISSN =	{2509-8195},
  year =	{2021},
  volume =	{7},
  number =	{2},
  editor =	{He, Dongjie and Lu, Jingbo and Gao, Yaoqing and Xue, Jingling},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DARTS.7.2.12},
  URN =		{urn:nbn:de:0030-drops-140363},
  doi =		{10.4230/DARTS.7.2.12},
  annote =	{Keywords: Object-Sensitive Pointer Analysis, CFL Reachability, Object Containment}
}
  • Refine by Author
  • 5 He, Dongjie
  • 5 Lu, Jingbo
  • 5 Xue, Jingling
  • 2 Gao, Yaoqing
  • 1 Akgün, Özgür
  • Show More...

  • Refine by Classification
  • 5 Theory of computation → Program analysis
  • 1 Theory of computation → Active learning
  • 1 Theory of computation → Constraint and logic programming

  • Refine by Keyword
  • 3 CFL Reachability
  • 3 Pointer Analysis
  • 2 Fine-Grained Context Sensitivity
  • 2 Object Containment
  • 2 Object-Sensitive Pointer Analysis
  • Show More...

  • Refine by Type
  • 6 document

  • Refine by Publication Year
  • 2 2021
  • 2 2022
  • 2 2024

Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail
© 2023-2024 Schloss Dagstuhl – LZI GmbH Imprint Privacy Contact