Nothing Special   »   [go: up one dir, main page]

skip to main content
research-article
Open access

Gradual liquid type inference

Published: 24 October 2018 Publication History

Abstract

Refinement types allow for lightweight program verification by enriching types with logical predicates. Liquid typing provides a decidable refinement inference mechanism that is convenient but subject to two major issues: (1) inference is global and requires top-level annotations, making it unsuitable for inference of modular code components and prohibiting its applicability to library code, and (2) inference failure results in obscure error messages. These difficulties seriously hamper the migration of existing code to use refinements.
This paper shows that gradual liquid type inference–a novel combination of liquid inference and gradual refinement types–addresses both issues. Gradual refinement types, which support imprecise predicates that are optimistically interpreted, can be used in argument positions to constrain liquid inference so that the global inference process effectively infers modular specifications usable for library components. Dually, when gradual refinements appear as the result of inference, they signal an inconsistency in the use of static refinements. Because liquid refinements are drawn from a finite set of predicates, in gradual liquid type inference we can enumerate the textitsafe concretizations of each imprecise refinement, i.e., the static refinements that justify why a program is gradually well-typed. This enumeration is useful for static liquid type error explanation, since the safe concretizations exhibit all the potential inconsistencies that lead to static type errors.
We develop the theory of gradual liquid type inference and explore its pragmatics in the setting of Liquid Haskell. To demonstrate the utility of our approach, we develop an interactive tool, GuiLT, for gradual liquid type inference in Liquid Haskell that both infers modular types and explores safe concretizations of gradual refinements. We report on the use of GuiLT for error reporting and discuss a case study on the migration of three commonly-used Haskell list manipulation libraries into Liquid Haskell.

Supplementary Material

WEBM File (a132-vazou.webm)

References

[1]
Yves Bertot and Pierre Castéran. 2004. Coq’Art: The Calculus of Inductive Constructions. Springer Verlag.
[2]
John Peter Campora, Sheng Chen, Martin Erwig, and Eric Walkingshaw. 2018. Migrating Gradual Types. PACMPL (POPL).
[3]
Sheng Chen and Martin Erwig. 2018. Systematic identification and communication of type errors. JFP 28.
[4]
Sheng Chen, Martin Erwig, and Eric Walkingshaw. 2014. Extending Type Inference to Variational Programs. In TOPLAS.
[5]
Bruno. Courcelle and Joost Engelfriet. 2012. Graph Structure and Monadic Second-Order Logic: A Language-Theoretic Approach (1st ed.). Cambridge University Press.
[6]
Tim Freeman and Frank Pfenning. 1991. Refinement Types for ML. In PLDI.
[7]
Ronald Garcia and Matteo Cimini. 2015. Principal Type Schemes for Gradual Programs. In POPL.
[8]
Ronald Garcia, Alison M. Clark, and Éric Tanter. 2016. Abstracting Gradual Typing. In POPL.
[9]
Michael Greenberg, Benjamin C. Pierce, and Stephanie Weirich. 2010. Contracts Made Manifest. In POPL.
[10]
Christian Haack and Joe Wells. 2003. Type Error Slicing in Implicitly-Typed Higher-Order Languages. In ESOP.
[11]
K.W. Knowles and C. Flanagan. 2010. Hybrid type checking. In TOPLAS.
[12]
Nico Lehmann and Éric Tanter. 2017. Gradual Refinement Types. In POPL.
[13]
Benjamin Lerner, Matthew Flower, Dan Grossman, and Craig Chambers. 2007. Searching for Type-Error Messages. In PLDI.
[14]
Phuc C. Nguyen and David Van Horn. 2015. Relatively complete counterexamples for higher-order programs. In PLDI.
[15]
Phuc C. Nguyen, Sam Tobin-Hochstadt, and David Van Horn. 2014. Soft Contract Verification. In ICFP.
[16]
Phuc C. Nguyen, Sam Tobin-Hochstadt, and David Van Horn. 2018. Soft Contract Verification for Higher-order Stateful Programs. In POPL.
[17]
Xinming Ou, Gang Tan, Yitzhak Mandelbaum, and David Walker. 2004. Dynamic Typing with Dependent Types, Jean-Jacques Levy, Ernst W. Mayr, and John C. Mitchell (Eds.). IFIP.
[18]
Zvonimir Pavlinovic, Tim King, and Thomas Wies. 2014. Finding minimum type error sources. In OOPSLA.
[19]
Aseem Rastogi, Avik Chaudhuri, and Basil Hosmer. 2012. The ins and outs of gradual type inference. In POPL.
[20]
P. Rondon, M. Kawaguchi, and R. Jhala. 2008. Liquid Types. In PLDI.
[21]
Eric Seidel, Ranjit Jhala, and Westley Weimer. 2016. Dynamic witnesses for static type errors (or, ill-typed programs usually go wrong). In ICFP.
[22]
Eric L. Seidel, Huma Sibghat, Kamalika Chaudhuri, Westley Weimer, and Ranjit Jhala. 2017. Learning to blame: localizing novice type errors with data-driven diagnosis. OOPSLA (2017).
[23]
Jeremy Siek and Walid Taha. 2006. Gradual Typing for Functional Languages. In Scheme and Functional Programming Workshop.
[24]
Jeremy G. Siek and Manish Vachharajani. 2008. Gradual Typing with Unification-based Inference. In Dynamic Languages Symposium.
[25]
Jeremy G. Siek, Michael M. Vitousek, Matteo Cimini, and John Tang Boyland. 2015. Refined Criteria for Gradual Typing. In SNAPL.
[26]
N. Swamy, C. Hriţcu, C. Keller, A. Rastogi, A. Delignat-Lavaud, S. Forest, K. Bhargavan, C. Fournet, P. Y. Strub, M. Kohlweiss, J. K. Zinzindohoue, and S. Zanella-Béguelin. 2016. Dependent Types and Multi-Monadic Effects in F*. In POPL.
[27]
Frank Tip and T. B. Dinesh. 2001. A slicing-based approach for locating type error. In TOSEM.
[28]
Sam Tobin-Hochstadt and Matthias Felleisen. 2006. Interlanguage migration: From scripts to programs. In DLS.
[29]
Sam Tobin-Hochstadt and David Van Horn. 2012. Higher-order Symbolic Execution via Contracts. In OOPSLA.
[30]
Niki Vazou, Eric L. Seidel, and Ranjit Jhala. 2014a. LiquidHaskell: Experience with Refinement Types in the Real World. In Haskell.
[31]
Niki Vazou, Eric L. Seidel, Ranjit Jhala, Dimitrios Vytiniotis, and Simon Peyton-Jones. 2014b. Refinement Types for Haskell. In ICFP.
[32]
Niki Vazou, Éric Tanter, and David Van Horn. 2018a. Gradual Liquid Types. ArXiv e-prints (2018). arXiv: 1807.02132
[33]
Niki Vazou, Anish Tondwalkar, Vikraman Choudhury, Ryan Scott, Ryan Newton, Philip Wadler, and Ranjit Jhala. 2018b. Refinement Reflection: Complete Verification with SMT. In POPL.
[34]
Mitchell Wand. 1986. Finding the Source of Type Errors. In POPL.
[35]
Makarius Wenzel. 2016. The Isabelle System Manual. (2016). https://www.cl.cam.ac.uk/research/hvg/Isabelle/dist/ Isabelle2016- 1/doc/system.pdf
[36]
Danfeng Zhang, Andrew C. Myers, Dimitrios Vytiniotis, and Simon Peyton-Jones. 2015. Diagnosing Type Errors with Class. In PLDI.

Cited By

View all
  • (2024)Total Type Error Localization and Recovery with HolesProceedings of the ACM on Programming Languages10.1145/36329108:POPL(2041-2068)Online publication date: 5-Jan-2024
  • (2023)Types, Modes and so Much More – The Prolog WayProlog: The Next 50 Years10.1007/978-3-031-35254-6_2(23-37)Online publication date: 17-Jun-2023
  • (2023)Gradual Tensor Shape CheckingProgramming Languages and Systems10.1007/978-3-031-30044-8_8(197-224)Online publication date: 17-Apr-2023
  • Show More Cited By

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Proceedings of the ACM on Programming Languages
Proceedings of the ACM on Programming Languages  Volume 2, Issue OOPSLA
November 2018
1656 pages
EISSN:2475-1421
DOI:10.1145/3288538
Issue’s Table of Contents
This work is licensed under a Creative Commons Attribution International 4.0 License.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 October 2018
Published in PACMPL Volume 2, Issue OOPSLA

Permissions

Request permissions for this article.

Check for updates

Badges

Author Tags

  1. error explanation
  2. gradual typing
  3. liquid types
  4. refinement types

Qualifiers

  • Research-article

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)85
  • Downloads (Last 6 weeks)19
Reflects downloads up to 17 Nov 2024

Other Metrics

Citations

Cited By

View all
  • (2024)Total Type Error Localization and Recovery with HolesProceedings of the ACM on Programming Languages10.1145/36329108:POPL(2041-2068)Online publication date: 5-Jan-2024
  • (2023)Types, Modes and so Much More – The Prolog WayProlog: The Next 50 Years10.1007/978-3-031-35254-6_2(23-37)Online publication date: 17-Jun-2023
  • (2023)Gradual Tensor Shape CheckingProgramming Languages and Systems10.1007/978-3-031-30044-8_8(197-224)Online publication date: 17-Apr-2023
  • (2022)Dependently-typed data plane programmingProceedings of the ACM on Programming Languages10.1145/34987016:POPL(1-28)Online publication date: 12-Jan-2022
  • (2021)Gradually structured dataProceedings of the ACM on Programming Languages10.1145/34855035:OOPSLA(1-29)Online publication date: 15-Oct-2021
  • (2021)Data flow refinement type inferenceProceedings of the ACM on Programming Languages10.1145/34343005:POPL(1-31)Online publication date: 4-Jan-2021

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Login options

Full Access

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media