Qub: A Resource Aware Functional Programming Language

Managing resources---file handles, database connections, etc.---is a hard problem. Debugging resource leaks and runtime errors due to resource mismanagement are difficult in evolving production code. Programming languages with static type systems are great tools to ensure erroneous code is detected at compile time. However, modern static type systems do little in the aspect of resource management as resources are treated as normal values. We propose a type system, Qub, based on the logic of bunched implications (BI) which models resources as first class citizens. We distinguish two kinds of program objects---restricted and unrestricted---and two kinds of functions---sharing and separating. Our approach guarantees resource correctness without compromising existing functional abstractions

Enumerating Counter-Factual Type Error Messages with an Existing Type Checker

The type error message of a standard type checker for a functional language gives only a single location as potential cause of the type error. If that location is not the cause, which often is the case, then the type error message hardly helps in locating the real cause. Here we present a method that uses a standard type checker to enumerate locations that potentially cause the type error, each with an actual and a counter-factual type for the given location. Adding our method to existing compilers requires only limited effort but improves type error debugging substantially

A Common Framework Using Expected Types for Several Type Debugging Approaches

Many different approaches to type error debugging were developed independently. In this paper, we describe a new common framework for several type error debugging approaches. For this purpose, we introduce expected types from the outer context and propose a method for obtaining them. Using expected types, we develop three type error debugging approaches: enumeration of type error messages, type error slicing and (improved) interactive type error debugging. Based on our idea we implemented prototypes and confirm that the framework works well for type debugging

GPT-3-powered type error debugging: investigating the use of large language models for code repair

Type systems are responsible for assigning types to terms in programs. That way, they enforce the actions that can be taken and can, consequently, detect type errors during compilation. However, while they are able to ag the existence of an error, they often fail to pinpoint its cause or provide a helpful error message. Thus, without adequate support, debugging this kind of errors can take a considerable amount of effort. Recently, neural network models have been developed that are able to understand programming languages and perform several downstream tasks. We argue that type error debugging can be enhanced by taking advantage of this deeper understanding of the language’s structure. In this paper, we present a technique that leverages GPT-3’s capabilities to automatically fix type errors in OCaml programs. We perform multiple source code analysis tasks to produce useful prompts that are then provided to GPT-3 to generate potential patches. Our publicly available tool, Mentat, supports multiple modes and was validated on an existing public dataset with thousands of OCaml programs. We automatically validate successful repairs by using Quickcheck to verify which generated patches produce the same output as the user-intended fixed version, achieving a 39% repair rate. In a comparative study, Mentat outperformed two other techniques in automatically fixing ill-typed OCaml programs.This work is financed by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia, within project UIDP/50014/2020. Francisco Ribeiro and José Nuno Macedo acknowledge FCT PhD grants SFRH/BD/144938/2019 and 2021.08184.BD, respectively. Additional funding: JSPS KAKENHI-JP19K20248

Boxy Types: Inference for Higher-Rank Types and Impredicativity

Languages with rich type systems are beginning to employ a blend of type inference and type checking, so that the type inference engine is guided by programmer-supplied type annotations. In this paper we show, for the first time, how to combine the virtues of two well-established ideas: unification-based inference, and bidirectional propagation of type annotations. The result is a type system that conservatively extends Hindley-Milner, and yet supports both higher-rank types and impredicativity

Introduction to the Literature On Programming Language Design

This is an introduction to the literature on programming language design and related topics. It is intended to cite the most important work, and to provide a place for students to start a literature search