Simplifying subtyping constraints

PottierFrançois,

doi:10.1145/232629.232642

Cited by 9 publications

(6 citation statements)

References 8 publications

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“…Unlike other investigations of type inference for HM with subtyping, e.g., [19,23,25], our goal is algorithmic dependent type inference, which requires incorporating path-sensitivity and decision procedures for EUFA. We also draw inspiration from type qualifiers [13] that refine types with a lattice of built-in and programmer-specified annotations.…”

Section: Related Workmentioning

confidence: 99%

Liquid types

Rondon

Kawaguci

Jhala

2008

Proceedings of the 29th ACM SIGPLAN Conference on Programming Language Design and Implementation

302

209

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We present Logically Qualified Data Types, abbreviated to Liquid Types, a system that combines Hindley-Milner type inference with Predicate Abstraction to automatically infer dependent types precise enough to prove a variety of safety properties. Liquid types allow programmers to reap many of the benefits of dependent types, namely static verification of critical properties and the elimination of expensive run-time checks, without the heavy price of manual annotation. We have implemented liquid type inference in DSOLVE, which takes as input an OCAML program and a set of logical qualifiers and infers dependent types for the expressions in the OCAML program. To demonstrate the utility of our approach, we describe experiments using DSOLVE to statically verify the safety of array accesses on a set of OCAML benchmarks that were previously annotated with dependent types as part of the DML project. We show that when used in conjunction with a fixed set of array bounds checking qualifiers, DSOLVE reduces the amount of manual annotation required for proving safety from 31% of program text to under 1%.

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Section: Related Workmentioning

confidence: 99%

Liquid types

Rondon

Kawaguci

Jhala

2008

Proceedings of the 29th ACM SIGPLAN Conference on Programming Language Design and Implementation

302

209

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“…Their fastest approach yields a factor of 3 saving in both time and space, but is slow in absolute times compared to other program analyses. Pottier [1996] studies an ML-style language with subtyping. Performing type inference on this language produces subtype constraints that are similar to our constraints.…”

Section: Related Workmentioning

confidence: 99%

Componential set-based analysis

Flanagan¹,

Felleisen

1999

ACM Trans. Program. Lang. Syst.

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Set-based analysis (SBA) produces good predictions about the behavior of functional and objectoriented programs. The analysis proceeds by inferring constraints that characterize the data flow relationships of the analyzed program. Experiences with MrSpidey, a static debugger based on SBA, indicate that SBA can adequately deal with programs of up to a couple of thousand lines of code. SBA fails, however, to cope with larger programs because it generates systems of constraints that are at least linear, and possibly quadratic, in the size of the analyzed program. This article presents theoretical and practical results concerning methods for reducing the size of constraint systems. The theoretical results include a proof-theoretic characterization of the observable behavior of constraint systems for program components, and a complete algorithm for deciding the observable equivalence of constraint systems. In the course of this development we establish a close connection between the observable equivalence of constraint systems and the equivalence of regular-tree grammars. We then exploit this connection to adapt a variety of algorithms for simplifying grammars to the problem of simplifying constraint systems. Based on the resulting algorithms, we have developed componential set-based analysis, a modular and polymorphic variant of SBA. Experimental results verify the effectiveness of the simplification algorithms and the componential analysis. The simplified constraint systems are typically an order of magnitude smaller than the original systems. These reductions in size produce significant gains in the speed of the analysis. Aiken [1994], and Cousot and Cousot [1995], previously exploited the relationship between RTGs and the least solution of a constraint system. We present a different result, namely a connection between RTGs and the observable behavior (i.e., the entire solution space) of constraint systems.

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“…Pottier [17] studies an ML-style language with a subtype system based on constraints, and and presents an incomplete algorithm for deciding entailment on constraint systems. He proposes some ad hoc algorithms for simplifying constraints, but does not present results on the cost or effectiveness of these algorithms.…”

Section: Competitive Workmentioning

confidence: 99%

Componential set-based analysis

FlanaganCormac¹,

FelleisenMatthias²

1997

SIGPLAN Not.

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analysis a whole analysis is to and oriented languages. *This work was partially supported by NSF grants CCR-0633748 and CCR-0610758, and a Lodieska Stockbridge Vaughan Fellowship. Permission to make digital/hard copy of part or all this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage, the copyright notice. the title of the publication and its date appear, and notice is given that copying is by permission of ACM, Inc. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. PLDI '97 Las Vegas, NV, USA 0 1997 ACM 0-89791-907-6197/0006...$3.50 'A number of researchers, including Reynolds [la], Jones and Muchnick [14], Heintze (111, Aiken [2], and Cousot and Coueot [3]previously exploited the relationship between RTGa and the leasst solution of a constraint system. We present an additional result, namely a connection between RTGs and the observable behavior (i.e., the entire solution space) of constraint systems.

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Simplifying subtyping constraints

Cited by 9 publications

References 8 publications

Liquid types

Liquid types

Componential set-based analysis

Componential set-based analysis

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