research-article

Learning refinement types

Authors:

Aditya V. Nori,

Suresh JagannathanAuthors Info & Claims

ACM SIGPLAN Notices, Volume 50, Issue 9

Pages 400 - 411

https://doi.org/10.1145/2858949.2784766

Published: 29 August 2015 Publication History

Abstract

We propose the integration of a random test generation system (capable of discovering program bugs) and a refinement type system (capable of expressing and verifying program invariants), for higher-order functional programs, using a novel lightweight learning algorithm as an effective intermediary between the two. Our approach is based on the well-understood intuition that useful, but difficult to infer, program properties can often be observed from concrete program states generated by tests; these properties act as likely invariants, which if used to refine simple types, can have their validity checked by a refinement type checker. We describe an implementation of our technique for a variety of benchmarks written in ML, and demonstrate its effectiveness in inferring and proving useful invariants for programs that express complex higher-order control and dataflow.

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Cited By

Wu GCao WYao YWei HChen TMa XFilkov VRay BZhou M(2024)LLM Meets Bounded Model Checking: Neuro-symbolic Loop Invariant InferenceProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695014(406-417)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695014
Xu RHe FWang BDevanbu PCohen MZimmermann T(2020)Interval counterexamples for loop invariant learningProceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3368089.3409752(111-122)Online publication date: 8-Nov-2020
https://dl.acm.org/doi/10.1145/3368089.3409752
Champion AChiba TKobayashi NSato R(2018)ICE-Based Refinement Type Discovery for Higher-Order Functional ProgramsTools and Algorithms for the Construction and Analysis of Systems10.1007/978-3-319-89960-2_20(365-384)Online publication date: 12-Apr-2018
https://doi.org/10.1007/978-3-319-89960-2_20
Show More Cited By

Index Terms

Learning refinement types

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Refinement types are an effective language-based verification technique. However, as any expressive typing discipline, its strength is its weakness, imposing sometimes undesired rigidity. Guided by abstract interpretation, we extend the gradual typing ...
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Information

Published In

cover image ACM SIGPLAN Notices

ACM SIGPLAN Notices Volume 50, Issue 9

ICFP '15

September 2015

436 pages

ISSN:0362-1340

EISSN:1558-1160

DOI:10.1145/2858949

Editor:
Andy Gill
University of Kansas, Lawrence, KS

Issue’s Table of Contents

ICFP 2015: Proceedings of the 20th ACM SIGPLAN International Conference on Functional Programming
August 2015
436 pages
ISBN:9781450336697
DOI:10.1145/2784731
General Chair:
Kathleen Fisher
Tufts University, USA
,
Program Chair:
John Reppy
University of Chicago, USA

Copyright © 2015 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 29 August 2015

Published in SIGPLAN Volume 50, Issue 9

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Cited By

Wu GCao WYao YWei HChen TMa XFilkov VRay BZhou M(2024)LLM Meets Bounded Model Checking: Neuro-symbolic Loop Invariant InferenceProceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering10.1145/3691620.3695014(406-417)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3691620.3695014
Xu RHe FWang BDevanbu PCohen MZimmermann T(2020)Interval counterexamples for loop invariant learningProceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3368089.3409752(111-122)Online publication date: 8-Nov-2020
https://dl.acm.org/doi/10.1145/3368089.3409752
Champion AChiba TKobayashi NSato R(2018)ICE-Based Refinement Type Discovery for Higher-Order Functional ProgramsTools and Algorithms for the Construction and Analysis of Systems10.1007/978-3-319-89960-2_20(365-384)Online publication date: 12-Apr-2018
https://doi.org/10.1007/978-3-319-89960-2_20
Neider DGarg PMadhusudan PSaha SPark D(2018)Invariant Synthesis for Incomplete Verification EnginesTools and Algorithms for the Construction and Analysis of Systems10.1007/978-3-319-89960-2_13(232-250)Online publication date: 12-Apr-2018
https://doi.org/10.1007/978-3-319-89960-2_13
Kobayashi NTsukada TWatanabe K(2018)Higher-Order Program Verification via HFL Model CheckingProgramming Languages and Systems10.1007/978-3-319-89884-1_25(711-738)Online publication date: 14-Apr-2018
https://doi.org/10.1007/978-3-319-89884-1_25
Katsura HKobayashi NSato R(2023)Higher-Order Property-Directed ReachabilityProceedings of the ACM on Programming Languages10.1145/36078317:ICFP(48-77)Online publication date: 31-Aug-2023
https://dl.acm.org/doi/10.1145/3607831
Yu SWang TWang JJust RFraser G(2023)Loop Invariant Inference through SMT Solving Enhanced Reinforcement LearningProceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis10.1145/3597926.3598047(175-187)Online publication date: 12-Jul-2023
https://dl.acm.org/doi/10.1145/3597926.3598047
Kobayashi NTanahashi KSato RTsukada T(2023)HFL(Z) Validity Checking for Automated Program VerificationProceedings of the ACM on Programming Languages10.1145/35711997:POPL(154-184)Online publication date: 11-Jan-2023
https://dl.acm.org/doi/10.1145/3571199
Mukai RKobayashi NSato R(2022)Parameterized Recursive Refinement Types for Automated Program VerificationStatic Analysis10.1007/978-3-031-22308-2_18(397-421)Online publication date: 2-Dec-2022
https://doi.org/10.1007/978-3-031-22308-2_18
Pavlinovic ZSu YWies T(2021)Data flow refinement type inferenceProceedings of the ACM on Programming Languages10.1145/34343005:POPL(1-31)Online publication date: 4-Jan-2021
https://dl.acm.org/doi/10.1145/3434300
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