ICE-based refinement type discovery for higher-order functional programs

Adrien Champion, Tomoya Chiba, Naoki Kobayashi, Ryosuke Sato

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Citations (Scopus)

Abstract

We propose a method for automatically finding refinement types of higher-order function programs. Our method is an extension of the Ice framework of Garg et al. for finding invariants. In addition to the usual positive and negative samples in machine learning, their Ice framework uses implication constraints, which consist of pairs (x, y) such that if x satisfies an invariant, so does y. From these constraints, Ice infers inductive invariants effectively. We observe that the implication constraints in the original Ice framework are not suitable for finding invariants of recursive functions with multiple function calls. We thus generalize the implication constraints to those of the form ((x1, ⋯, xk), y), which means that if all of x1, ⋯, xk satisfy an invariant, so does y. We extend their algorithms for inferring likely invariants from samples, verifying the inferred invariants, and generating new samples. We have implemented our method and confirmed its effectiveness through experiments.

Original languageEnglish
Title of host publicationTools and Algorithms for the Construction and Analysis of Systems - 24th International Conference, TACAS 2018, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2018, Proceedings
EditorsDirk Beyer, Marieke Huisman
PublisherSpringer Verlag
Pages365-384
Number of pages20
ISBN (Print)9783319899596
DOIs
Publication statusPublished - Jan 1 2018
Event24th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2018 Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2018 - Thessaloniki, Greece
Duration: Apr 14 2018Apr 20 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10805 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other24th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2018 Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2018
CountryGreece
CityThessaloniki
Period4/14/184/20/18

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Champion, A., Chiba, T., Kobayashi, N., & Sato, R. (2018). ICE-based refinement type discovery for higher-order functional programs. In D. Beyer, & M. Huisman (Eds.), Tools and Algorithms for the Construction and Analysis of Systems - 24th International Conference, TACAS 2018, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2018, Proceedings (pp. 365-384). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10805 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-89960-2_20