Computer Science > Programming Languages
[Submitted on 12 May 2015 (v1), last revised 16 May 2015 (this version, v2)]
Title:Refinement Type Inference via Horn Constraint Optimization
View PDFAbstract:We propose a novel method for inferring refinement types of higher-order functional programs. The main advantage of the proposed method is that it can infer maximally preferred (i.e., Pareto optimal) refinement types with respect to a user-specified preference order. The flexible optimization of refinement types enabled by the proposed method paves the way for interesting applications, such as inferring most-general characterization of inputs for which a given program satisfies (or violates) a given safety (or termination) property. Our method reduces such a type optimization problem to a Horn constraint optimization problem by using a new refinement type system that can flexibly reason about non-determinism in programs. Our method then solves the constraint optimization problem by repeatedly improving a current solution until convergence via template-based invariant generation. We have implemented a prototype inference system based on our method, and obtained promising results in preliminary experiments.
Submission history
From: Hiroshi Unno [view email][v1] Tue, 12 May 2015 05:38:53 UTC (22 KB)
[v2] Sat, 16 May 2015 13:04:03 UTC (22 KB)
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