One of the pleasant new features in GHC 6.10 is the long-awaited addition of view patterns. This feature is usually advertised as making it possible to pattern match against the values of an abstract type. An essential aspect of modular software design is that we don't want to expose the implementation of complex code. Someone will surely come along and start making decisions based on bits of our code that they can see, thus limiting our future room to manoeuvre. This is all amply explained on the view pattern wiki page and in the GHC User's Guide. how do they diff from f# active pats
This article is part three in a series on introductory Haskell programming. In the first article, we wrote a small program to recursively scan file-system directories and print their contents as ASCII-art trees. In the second article, we refactored the program to make its logic more reusable by separating the directory-scanning logic from the tree-printing logic. In this article, we will address a shortcoming of the refactored version: It must scan directory hierarchies completely before printing their trees, i.e., it must scan and then print, when doing both simultaneously is both more efficient and more user friendly. Recall from the previous article that our directory-printing program is factored into three pieces of logic:
August 6, 2007 I am curious about the possibility of developing Haskell programs spontaneously with proofs about their properties and have the type checker verify the proofs for us, in a way one would do in a dependently typed language. In the exercise below, I tried to redo part of the merge-sort example in Altenkirch, McBride, and McKinna’s introduction to Epigram: deal the input list into a binary tree, and fold the tree by the function merging two sorted lists into one. The property I am going to show is merely that the length of the input list is preserved. To begin with, we define the usual type-level representation of natural numbers and lists indexed by their lengths.
1. What is a dependent type 1. ADT -- the simplest dependent-type 2. Singleton types 3. Branding: type proxies 2. Lightweight static capabilities 1. Abstract and Introduction 2. Formalization and proofs 3. Accompanying source code 3. The question of verification 4. Genuine Dependent-type systems This is a joint work with Chung-chieh Shan. We describe several approaches to lightweight dependent-type programming, letting us gain experience with dependent types on existing programming language systems All these lightweight approaches rely on type-level proxies for values, so we can statically express properties (e.g., equality, inequality) of the values that are not generally known until the run time. ``This much is clear: many programmers are already finding practical uses for the approximants to dependent types which mainstream functional languages (especially Haskell) admit, by hook or by crook.''
Idris is an experimental language with full dependent types. Dependent types allow types to be predicated on values, meaning that some aspects of a program's behaviour can be specified precisely in the type. The language is closely related to Epigram and Agda. The aims of the project are: * To provide a platform for realistic programming with dependent types. By realistic, we mean the ability to interact with the outside world and use primitive types and operations. This includes networking, file handling, concurrency, etc. * To show that full dependent types do not mean we have to abandon the functional style we have come to know and love with languages like Haskell and OCaml. lightweight dependently typed programming means allowing the programmer full access to values in types, and letting the type checker do the hard work so you don't have to! Future plans include some more useful syntax (local definitions/where clauses being the most important), a compiler,
au:chlipala Ur introduces richer type system features into FP. Ur is functional, pure, statically-typed, and strict. Ur supports metaprogramming based on row types. Ur/Web is standard library and associated rules for parsing and optimization. Ur/Web supports construction of dynamic web applications backed by SQL databases. The signature of the standard library is such that well-typed Ur/Web programs "don't go wrong" in a very broad sense. They also may not: * Suffer from any kinds of code-injection attacks * Return invalid HTML * Contain dead intra-application links * Have mismatches between HTML forms and the fields expected by their handlers It is also possible to use metaprogramming to build significant application pieces by analysis of type structure - demo includes an ML-style functor for building an admin interface for an arbitrary SQL table. The Ur/Web compiler also produces very efficient object code that does not use gc
Efficiently translating Haskell to JVM Bytecode using GHC's intermediate language, STG. LambdaVM is the proof that complete and efficient translation is possible. LambdaVM is a set of patches to GHC's which extend it to fully support generating useable JVM bytecode. It modifies the three primary components of GHC: * The compiler itself: The compiler has been modified to transform STG, one of GHC's many intermediate languages, to JVM bytecode. * The runtime system (RTS): GHC's RTS implemented as a mix of C and C-- has been reimplemented in Java. * The base libraries: GHC's base libraries have been modified to run on top of Java's standard libraries rather than ANSI C/POSIX libraries. October, 2008 Update LambdaVM is coming back! I've fixed all the GHC 6.8.x build problems and the instructions below should once again work. LambdaVM itself is still based on a circa November, 2007 GHC HEAD but moving all my changes to the current HEAD is next
A Tutorial Implementation of a Dependently Typed Lambda Calculus Andres Löh, Conor McBride and Wouter Swierstra We present the type rules for a dependently-typed core calculus together with a straightforward implementation in Haskell. We explicitly highlight the changes necessary to shift from a simply-typed lambda calculus to the dependently-typed lambda calculus. We also describe how to extend our core language with data types and write several small example programs. The paper is accompanied by an executable interpreter and example code that allows immediate experimentation with the system we describe. Download Draft Paper (submitted to FI) Haskell source code (executable Haskell file containing all the code from the paper plus the interpreter; automatically generated from the paper sources) prelude.lp (prelude for the LambdaPi interpreter, containing several example programs) Instructions (how to get started with the LambdaPi interpreter)
In the previous post, we talked about some of the basics of functional programming unit testing. That post mostly focused around HUnit, which is a traditional xUnit testing framework. This time, let's focus on type-based property testing, which is to create specs which assert logical properties about a function, and then to generate data to test in an attempt to falsify these assertions, through the use of a tool called QuickCheck. Much like the traditional xUnit frameworks, this tool helps us flush out the specifications of our software through the use of tests. Unlike the xUnit frameworks, however, this framework allows us to create generators to help flush out our behaviors and capture our edge cases as we look for ways to falsify our tests. These generators could use either random data or well structured data that you can craft. Let's dive a little deeper into what that means.
There are two schools of thought when it comes to documenting programs and libraries: some embed fragments of code within the documentation (so called “literate programming”) and others embed fragments of documentation in their code (i.e. comments). The “comments” approach makes it easy to generate API documentation and the like (a feature built-in to Haskell’s Hackage system) but help me write blog posts and other documents containing code, which is where literate programming shines. Happily, Haskell supports both of these approaches and has a few rather useful tools available to make both easier. In this post, I’ll describe how to take literate Haskell with Markdown formatted text and produce syntax highlighted documents in HTML and PDF.