Space-Efficient Gradual Typing in Coercion-Passing Style

Herman et al. pointed out that the insertion of run-time checks into a gradually typed program could hamper tail-call optimization and, as a result, worsen the space complexity of the program. To address the problem, they proposed a space-efficient coercion calculus, which was subsequently improved by Siek et al. The semantics of these calculi involves eager composition of run-time checks expressed by coercions to prevent the size of a term from growing. However, it relies also on a nonstandard reduction rule, which does not seem easy to implement. In fact, no compiler implementation of gradually typed languages fully supports the space-efficient semantics faithfully. In this paper, we study coercion-passing style, which Herman et al. have already mentioned, as a technique for straightforward space-efficient implementation of gradually typed languages. A program in coercion-passing style passes "the rest of the run-time checks" around - just like continuation-passing style (CPS), in which "the rest of the computation" is passed around - and (unlike CPS) composes coercions eagerly. We give a formal coercion-passing translation from ?S by Siek et al. to ?S?, which is a new calculus of first-class coercions tailored for coercion-passing style, and prove correctness of the translation. We also implement our coercion-passing style transformation for the Grift compiler developed by Kuhlenschmidt et al. An experimental result shows stack overflow can be prevented properly at the cost of up to 3 times slower execution for most partially typed practical programs

Transitioning from structural to nominal code with efficient gradual typing

Gradual typing is a principled means for mixing typed and untyped code. But typed and untyped code often exhibit different programming patterns. There is already substantial research investigating gradually giving types to code exhibiting typical untyped patterns, and some research investigating gradually removing types from code exhibiting typical typed patterns. This paper investigates how to extend these established gradual-typing concepts to give formal guarantees not only about how to change types as code evolves but also about how to change such programming patterns as well. In particular, we explore mixing untyped "structural" code with typed "nominal" code in an object-oriented language. But whereas previous work only allowed "nominal" objects to be treated as "structural" objects, we also allow "structural" objects to dynamically acquire certain nominal types, namely interfaces. We present a calculus that supports such "cross-paradigm" code migration and interoperation in a manner satisfying both the static and dynamic gradual guarantees, and demonstrate that the calculus can be implemented efficiently

TOWARDS EFFICIENT GRADUAL TYPING VIA MONOTONIC REFERENCES AND COERCIONS

Thesis (Ph.D.) - Indiana University, Luddy School of Informatics, Computing, and Engineering/University Graduate School, 2020Integrating static and dynamic typing into a single programming language enables programmers to choose which discipline to use in each code region. Different approaches for this integration have been studied and put into use at large scale, e.g. TypeScript for JavaScript and adding the dynamic type to C#. Gradual typing is one approach to this integration that preserves type soundness by performing type-checking at run-time using casts. For higher order values such as functions and mutable references, a cast typically wraps the value in a proxy that performs type-checking when the value is used. This approach suffers from two problems: (1) chains of proxies can grow and consume unbounded space, and (2) statically typed code regions need to check whether values are proxied. Monotonic references solve both problems for mutable references by directly casting the heap cell instead of wrapping the reference in a proxy. In this dissertation, an integration is proposed of monotonic references with the coercion-based solution to the problem of chains of proxies for other values such as functions. Furthermore, the prior semantics for monotonic references involved storing and evaluating cast expressions (not yet values) in the heap and it is not obvious how to implement this behavior efficiently in a compiler and run-time system. This dissertation proposes novel dynamic semantics where only values are written to the heap, making the semantics straightforward to implement. The approach is implemented in Grift, a compiler for a gradually typed programming language, and a few key optimizations are proposed. Finally, the proposed performance evaluation methodology shows that the proposed approach eliminates all overheads associated with gradually typed references in statically typed code regions without introducing significant average-case overhead

Gradual Certified Programming in Coq

Expressive static typing disciplines are a powerful way to achieve high-quality software. However, the adoption cost of such techniques should not be under-estimated. Just like gradual typing allows for a smooth transition from dynamically-typed to statically-typed programs, it seems desirable to support a gradual path to certified programming. We explore gradual certified programming in Coq, providing the possibility to postpone the proofs of selected properties, and to check "at runtime" whether the properties actually hold. Casts can be integrated with the implicit coercion mechanism of Coq to support implicit cast insertion a la gradual typing. Additionally, when extracting Coq functions to mainstream languages, our encoding of casts supports lifting assumed properties into runtime checks. Much to our surprise, it is not necessary to extend Coq in any way to support gradual certified programming. A simple mix of type classes and axioms makes it possible to bring gradual certified programming to Coq in a straightforward manner.Comment: DLS'15 final version, Proceedings of the ACM Dynamic Languages Symposium (DLS 2015

A Type System for Julia

The Julia programming language was designed to fill the needs of scientific computing by combining the benefits of productivity and performance languages. Julia allows users to write untyped scripts easily without needing to worry about many implementation details, as do other productivity languages. If one just wants to get the work done-regardless of how efficient or general the program might be, such a paradigm is ideal. Simultaneously, Julia also allows library developers to write efficient generic code that can run as fast as implementations in performance languages such as C or Fortran. This combination of user-facing ease and library developer-facing performance has proven quite attractive, and the language has increasing adoption. With adoption comes combinatorial challenges to correctness. Multiple dispatch -- Julia's key mechanism for abstraction -- allows many libraries to compose "out of the box." However, it creates bugs where one library's requirements do not match what another provides. Typing could address this at the cost of Julia's flexibility for scripting. I developed a "best of both worlds" solution: gradual typing for Julia. My system forms the core of a gradual type system for Julia, laying the foundation for improving the correctness of Julia programs while not getting in the way of script writers. My framework allows methods to be individually typed or untyped, allowing users to write untyped code that interacts with typed library code and vice versa. Typed methods then get a soundness guarantee that is robust in the presence of both dynamically typed code and dynamically generated definitions. I additionally describe protocols, a mechanism for typing abstraction over concrete implementation that accommodates one common pattern in Julia libraries, and describe its implementation into my typed Julia framework.Comment: PhD thesi

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