Strategic polymorphism requires just two combinators!

In previous work, we introduced the notion of functional strategies: first-class generic functions that can traverse terms of any type while mixing uniform and type-specific behaviour. Functional strategies transpose the notion of term rewriting strategies (with coverage of traversal) to the functional programming paradigm. Meanwhile, a number of Haskell-based models and combinator suites were proposed to support generic programming with functional strategies. In the present paper, we provide a compact and matured reconstruction of functional strategies. We capture strategic polymorphism by just two primitive combinators. This is done without commitment to a specific functional language. We analyse the design space for implementational models of functional strategies. For completeness, we also provide an operational reference model for implementing functional strategies (in Haskell). We demonstrate the generality of our approach by reconstructing representative fragments of the Strafunski library for functional strategies.Comment: A preliminary version of this paper was presented at IFL 2002, and included in the informal preproceedings of the worksho

Acute: high-level programming language design for distributed computation

Existing languages provide good support for typeful programming of standalone programs. In a distributed system, however, there may be interaction between multiple instances of many distinct programs, sharing some (but not necessarily all) of their module structure, and with some instances rebuilt with new versions of certain modules as time goes on. In this paper we discuss programming language support for such systems, focussing on their typing and naming issues. We describe an experimental language, Acute, which extends an ML core to support distributed development, deployment, and execution, allowing type-safe interaction between separately-built programs. The main features are: (1) type-safe marshalling of arbitrary values; (2) type names that are generated (freshly and by hashing) to ensure that type equality tests suffice to protect the invariants of abstract types, across the entire distributed system; (3) expression-level names generated to ensure that name equality tests suffice for type-safety of associated values, e.g. values carried on named channels; (4) controlled dynamic rebinding of marshalled values to local resources; and (5) thunkification of threads and mutexes to support computation mobility. These features are a large part of what is needed for typeful distributed programming. They are a relatively lightweight extension of ML, should be efficiently implementable, and are expressive enough to enable a wide variety of distributed infrastructure layers to be written as simple library code above the byte-string network and persistent store APIs. This disentangles the language runtime from communication intricacies. This paper highlights the main design choices in Acute. It is supported by a full language definition (of typing, compilation, and operational semantics), by a prototype implementation, and by example distribution libraries

A Typed Slicing Compilation of the Polymorphic RPC Calculus

The polymorphic RPC calculus allows programmers to write succinct multitier programs using polymorphic location constructs. However, until now it lacked an implementation. We develop an experimental programming language based on the polymorphic RPC calculus. We introduce a polymorphic Client-Server (CS) calculus with the client and server parts separated. In contrast to existing untyped CS calculi, our calculus is not only able to resolve polymorphic locations statically, but it is also able to do so dynamically. We design a type-based slicing compilation of the polymorphic RPC calculus into this CS calculus, proving type and semantic correctness. We propose a method to erase types unnecessary for execution but retaining locations at runtime by translating the polymorphic CS calculus into an untyped CS calculus, proving semantic correctness.Comment: A long version of PPDP 2021 (23rd International Symposium on Principles and Practice of Declarative Programming

A Polymorphic RPC Calculus

The RPC calculus is a simple semantic foundation for multi-tier programming languages such as Links in which located functions can be written for the client-server model. Subsequently, the typed RPC calculus is designed to capture the location information of functions by types and to drive location type-directed slicing compilations. However, the use of locations is currently limited to monomorphic ones, which is one of the gaps to overcome to put into practice the theory of RPC calculi for client-server model. This paper proposes a polymorphic RPC calculus to allow programmers to write succinct multi-tier programs using polymorphic location constructs. Then the polymorphic multi-tier programs can be automatically translated into programs only containing location constants amenable to the existing slicing compilation methods. We formulate a type system for the polymorphic RPC calculus, and prove its type soundness. Also, we design a monomorphization translation together with proofs on its type and semantic correctness for the translation.Comment: SBMF-Brazilian Symposium on Formal Methods 201

Particpants' Proceedings on the Workshop: Types for Program Analysis

As a satellite meeting of the TAPSOFT'95 conference we organized a small workshop on program analysis. The title of the workshop, ``Types for Program Analysis´´, was motivated by the recent trend of letting the presentation and development of program analyses be influenced by annotated type systems, effect systems, and more general logical systems. The contents of the workshop was intended to be somewhat broader; consequently the call for participation listed the following areas of interest:- specification of specific analyses for programming languages,- the role of effects, polymorphism, conjunction/disjunction types, dependent types etc.in specification of analyses,- algorithmic tools and methods for solving general classes of type-based analyses,- the role of unification, semi-unification etc. in implementations of analyses,- proof techniques for establishing the safety of analyses,- relationship to other approaches to program analysis, including abstract interpretation and constraint-based methods,- exploitation of analysis results in program optimization and implementation.The submissions were not formally refereed; however each submission was read by several members of the program committee and received detailed comments and suggestions for improvement. We expect that several of the papers, in slightly revised forms, will show up at future conferences. The workshop took place at Aarhus University on May 26 and May 27 and lasted two half days

A Calculus with Partially Dynamic Records for Typeful Manipulation of JSON Objects

This paper investigates language constructs for high-level and type-safe manipulation of JSON objects in a typed functional language. A major obstacle in representing JSON in a static type system is their heterogeneous nature: in most practical JSON APIs, a JSON array is a heterogeneous list consisting of, for example, objects having common fields and possibly some optional fields. This paper presents a typed calculus that reconciles static typing constraints and heterogeneous JSON arrays based on the idea of partially dynamic records originally proposed and sketched by Buneman and Ohori for complex database object manipulation. Partially dynamic records are dynamically typed records, but some parts of their structures are statically known. This feature enables us to represent JSON objects as typed data structures. The proposed calculus smoothly extends with ML-style pattern matching and record polymorphism. These results yield a typed functional language where the programmer can directly import JSON data as terms having static types, and can manipulate them with the full benefits of static polymorphic type-checking. The proposed calculus has been embodied in SML#, an extension of Standard ML with record polymorphism and other practically useful features. This paper also reports on the details of the implementation and demonstrates its feasibility through examples using actual Web APIs. The SML# version 3.1.0 compiler includes JSON support presented in this paper, and is available from Tohoku University as open-source software under a BSD-style license