3,522 research outputs found
Practical Datatype Specializations with Phantom Types and Recursion Schemes
Datatype specialization is a form of subtyping that captures program
invariants on data structures that are expressed using the convenient and
intuitive datatype notation. Of particular interest are structural invariants
such as well-formedness. We investigate the use of phantom types for describing
datatype specializations. We show that it is possible to express
statically-checked specializations within the type system of Standard ML. We
also show that this can be done in a way that does not lose useful programming
facilities such as pattern matching in case expressions.Comment: 25 pages. Appeared in the Proc. of the 2005 ACM SIGPLAN Workshop on
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Using Inhabitation in Bounded Combinatory Logic with Intersection Types for Composition Synthesis
We describe ongoing work on a framework for automatic composition synthesis
from a repository of software components. This work is based on combinatory
logic with intersection types. The idea is that components are modeled as typed
combinators, and an algorithm for inhabitation {\textemdash} is there a
combinatory term e with type tau relative to an environment Gamma?
{\textemdash} can be used to synthesize compositions. Here, Gamma represents
the repository in the form of typed combinators, tau specifies the synthesis
goal, and e is the synthesized program. We illustrate our approach by examples,
including an application to synthesis from GUI-components.Comment: In Proceedings ITRS 2012, arXiv:1307.784
Gradual Liquid Type Inference
Liquid typing provides a decidable refinement inference mechanism that is
convenient but subject to two major issues: (1) inference is global and
requires top-level annotations, making it unsuitable for inference of modular
code components and prohibiting its applicability to library code, and (2)
inference failure results in obscure error messages. These difficulties
seriously hamper the migration of existing code to use refinements. This paper
shows that gradual liquid type inference---a novel combination of liquid
inference and gradual refinement types---addresses both issues. Gradual
refinement types, which support imprecise predicates that are optimistically
interpreted, can be used in argument positions to constrain liquid inference so
that the global inference process e effectively infers modular specifications
usable for library components. Dually, when gradual refinements appear as the
result of inference, they signal an inconsistency in the use of static
refinements. Because liquid refinements are drawn from a nite set of
predicates, in gradual liquid type inference we can enumerate the safe
concretizations of each imprecise refinement, i.e. the static refinements that
justify why a program is gradually well-typed. This enumeration is useful for
static liquid type error explanation, since the safe concretizations exhibit
all the potential inconsistencies that lead to static type errors. We develop
the theory of gradual liquid type inference and explore its pragmatics in the
setting of Liquid Haskell.Comment: To appear at OOPSLA 201
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