HoIce: An ICE-Based Non-linear Horn Clause Solver

The ICE framework is a machine-learning-based technique originally introduced for inductive invariant inference over transition systems, and building on the supervised learning paradigm. Recently, we adapted the approach to non-linear Horn clause solving in the context of higher-order program verifi...

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Bibliographic Details
Published inProgramming Languages and Systems pp. 146 - 156
Main Authors Champion, Adrien, Kobayashi, Naoki, Sato, Ryosuke
Format Book Chapter
LanguageEnglish
Published Cham Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
Clause Implication
Horn Clause Solver
Predicate Application
Program Verification
Program Verification Tools
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Summary:The ICE framework is a machine-learning-based technique originally introduced for inductive invariant inference over transition systems, and building on the supervised learning paradigm. Recently, we adapted the approach to non-linear Horn clause solving in the context of higher-order program verification. We showed that we could solve more of our benchmarks (extracted from higher-order program verification problems) than other state-of-the-art Horn clause solvers. This paper discusses some of the many improvements we recently implemented in HoIce, our implementation of this generalized ICE framework.
ISBN:9783030027674
3030027678
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-030-02768-1_8