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:
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.