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Formalizing, Mechanizing, and Verifying Class-Based Refinement Types

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

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ACM Subject Classification
  • Theory of computation → Type structures
  • Software and its engineering → Formal software verification
Keywords
  • Refinement Types
  • Program Verification
  • Object-oriented Programming

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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.

Cite As Get BibTex

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) https://doi.org/10.4230/LIPIcs.ECOOP.2024.39

Author Details

Ke Sun
  • Key Lab of HCST (PKU), MOE, School of Computer Science, Peking University, Beijing, China
Di Wang
  • Key Lab of HCST (PKU), MOE, School of Computer Science, Peking University, Beijing, China
Sheng Chen
  • The Center for Advanced Computer Studies, University of Louisiana, Lafayette, LA, USA
Meng Wang
  • University of Bristol, UK
Dan Hao
  • Key Lab of HCST (PKU), MOE, School of Computer Science, Peking University, Beijing, China

Funding

This work is sponsored by National Natural Science Foundation of China Grant No. 62232001, NSF Grant 1750886, and EPSRC Grant EP/T008911/1.

Acknowledgements

We thank the anonymous reviewers for their helpful comments.

Supplementary Materials

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