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Documents authored by Hao, Dan

Document
DOI: 10.4230/LIPIcs.ECOOP.2024.39
Formalizing, Mechanizing, and Verifying Class-Based Refinement Types

Authors: Ke Sun, Di Wang, Sheng Chen, Meng Wang, and Dan Hao

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

Abstract
Refinement types have been extensively used in class-based languages to specify and verify fine-grained logical specifications. Despite the advances in practical aspects such as applicability and usability, two fundamental issues persist. First, the soundness of existing class-based refinement type systems is inadequately explored, casting doubts on their reliability. Second, the expressiveness of existing systems is limited, restricting the depiction of semantic properties related to object-oriented constructs. This work tackles these issues through a systematic framework. We formalize a declarative class-based refinement type calculus (named RFJ), that is expressive and concise. We rigorously develop the soundness meta-theory of this calculus, followed by its mechanization in Coq. Finally, to ensure the calculus’s verifiability, we propose an algorithmic verification approach based on a fragment of first-order logic (named LFJ), and implement this approach as a type checker.
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Ke Sun, Di Wang, Sheng Chen, Meng Wang, and Dan Hao. Formalizing, Mechanizing, and Verifying Class-Based Refinement Types. In 38th European Conference on Object-Oriented Programming (ECOOP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 313, pp. 39:1-39:30, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{sun_et_al:LIPIcs.ECOOP.2024.39,
  author =	{Sun, Ke and Wang, Di and Chen, Sheng and Wang, Meng and Hao, Dan},
  title =	{{Formalizing, Mechanizing, and Verifying Class-Based Refinement Types}},
  booktitle =	{38th European Conference on Object-Oriented Programming (ECOOP 2024)},
  pages =	{39:1--39:30},
  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.2024.39},
  URN =		{urn:nbn:de:0030-drops-208881},
  doi =		{10.4230/LIPIcs.ECOOP.2024.39},
  annote =	{Keywords: Refinement Types, Program Verification, Object-oriented Programming}
}
Document
Artifact
DOI: 10.4230/DARTS.10.2.22
Formalizing, Mechanizing, and Verifying Class-Based Refinement Types (Artifact)

Authors: Ke Sun, Di Wang, Sheng Chen, Meng Wang, and Dan Hao

Published in: DARTS, Volume 10, Issue 2, Special Issue of the 38th European Conference on Object-Oriented Programming (ECOOP 2024)

Abstract
This is the artifact description of an ECOOP paper. A new expressive formalization of class-based refinement types is proposed in the paper. We enrich the formalization by analyzing its meta-theory and algorithmic verification. The meta-theory and algorithmic verification have been mechanized and implemented. We discuss details of the mechanization and implementation in this document.
Cite as

Ke Sun, Di Wang, Sheng Chen, Meng Wang, and Dan Hao. Formalizing, Mechanizing, and Verifying Class-Based Refinement Types (Artifact). In Special Issue of the 38th European Conference on Object-Oriented Programming (ECOOP 2024). Dagstuhl Artifacts Series (DARTS), Volume 10, Issue 2, pp. 22:1-22:3, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@Article{sun_et_al:DARTS.10.2.22,
  author =	{Sun, Ke and Wang, Di and Chen, Sheng and Wang, Meng and Hao, Dan},
  title =	{{Formalizing, Mechanizing, and Verifying Class-Based Refinement Types (Artifact)}},
  pages =	{22:1--22:3},
  journal =	{Dagstuhl Artifacts Series},
  ISBN =	{978-3-95977-342-3},
  ISSN =	{2509-8195},
  year =	{2024},
  volume =	{10},
  number =	{2},
  editor =	{Sun, Ke and Wang, Di and Chen, Sheng and Wang, Meng and Hao, Dan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DARTS.10.2.22},
  URN =		{urn:nbn:de:0030-drops-209209},
  doi =		{10.4230/DARTS.10.2.22},
  annote =	{Keywords: Refinement Types, Program Verification, Object-oriented Programming}
}
Document
DOI: 10.4230/LIPIcs.ECOOP.2018.6
Learning to Accelerate Symbolic Execution via Code Transformation

Authors: Junjie Chen, Wenxiang Hu, Lingming Zhang, Dan Hao, Sarfraz Khurshid, and Lu Zhang

Published in: LIPIcs, Volume 109, 32nd European Conference on Object-Oriented Programming (ECOOP 2018)

Abstract
Symbolic execution is an effective but expensive technique for automated test generation. Over the years, a large number of refined symbolic execution techniques have been proposed to improve its efficiency. However, the symbolic execution efficiency problem remains, and largely limits the application of symbolic execution in practice. Orthogonal to refined symbolic execution, in this paper we propose to accelerate symbolic execution through semantic-preserving code transformation on the target programs. During the initial stage of this direction, we adopt a particular code transformation, compiler optimization, which is initially proposed to accelerate program concrete execution by transforming the source program into another semantic-preserving target program with increased efficiency (e.g., faster or smaller). However, compiler optimizations are mostly designed to accelerate program concrete execution rather than symbolic execution. Recent work also reported that unified settings on compiler optimizations that can accelerate symbolic execution for any program do not exist at all. Therefore, in this work we propose a machine-learning based approach to tuning compiler optimizations to accelerate symbolic execution, whose results may also aid further design of specific code transformations for symbolic execution. In particular, the proposed approach LEO separates source-code functions and libraries through our program-splitter, and predicts individual compiler optimization (i.e., whether a type of code transformation is chosen) separately through analyzing the performance of existing symbolic execution. Finally, LEO applies symbolic execution on the code transformed by compiler optimization (through our local-optimizer). We conduct an empirical study on GNU Coreutils programs using the KLEE symbolic execution engine. The results show that LEO significantly accelerates symbolic execution, outperforming the default KLEE configurations (i.e., turning on/off all compiler optimizations) in various settings, e.g., with the default training/testing time, LEO achieves the highest line coverage in 50/68 programs, and its average improvement rate on all programs is 46.48%/88.92% in terms of line coverage compared with turning on/off all compiler optimizations.
Cite as

Junjie Chen, Wenxiang Hu, Lingming Zhang, Dan Hao, Sarfraz Khurshid, and Lu Zhang. Learning to Accelerate Symbolic Execution via Code Transformation. In 32nd European Conference on Object-Oriented Programming (ECOOP 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 109, pp. 6:1-6:27, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{chen_et_al:LIPIcs.ECOOP.2018.6,
  author =	{Chen, Junjie and Hu, Wenxiang and Zhang, Lingming and Hao, Dan and Khurshid, Sarfraz and Zhang, Lu},
  title =	{{Learning to Accelerate Symbolic Execution via Code Transformation}},
  booktitle =	{32nd European Conference on Object-Oriented Programming (ECOOP 2018)},
  pages =	{6:1--6:27},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-079-8},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{109},
  editor =	{Millstein, Todd},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ECOOP.2018.6},
  URN =		{urn:nbn:de:0030-drops-92115},
  doi =		{10.4230/LIPIcs.ECOOP.2018.6},
  annote =	{Keywords: Symbolic Execution, Code Transformation, Machine Learning}
}
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