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- W2218263673 abstract "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." @default.
- W2218263673 created "2016-06-24" @default.
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- W2218263673 date "2015-01-01" @default.
- W2218263673 modified "2023-09-30" @default.
- W2218263673 title "Refinement Type Inference via Horn Constraint Optimization" @default.
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- W2218263673 doi "https://doi.org/10.1007/978-3-662-48288-9_12" @default.
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