![]() For instance, it gives you full control over side effects. This is technically true, in the sense that Haskell is able to reduce all imperative programming to pure functions, but in practice Haskell is the best imperative language that there is. This is not true, and you'll find no high math in this series of tutorials.Īnother myth is that you can't do imperative programming in Haskell. One such myth is that you have to know advanced mathematics (category theory in particular) to be a good Haskell programmer. There are many myths about functional programming, and Haskell in particular, that have no basis in reality. Since I came from an imperative background - I can write some mean C++ code - I'm familiar with the potential obstacles in embracing Haskell. If you have some familiarity with functional programming, that's even better. The prerequisite for this series of tutorials is some knowledge of imperative programming, whether C++, Java, Pascal, you name it. We use a refinement type on the index argument to state its validity.A gentle introduction to Haskell for beginners. Specification of the original function expressed with predicate logic. ![]() ![]() With Liquid Haskell, instead, we could get our safety guarantees by writing a List indexing, which then would make this change less local than we To avoid conversions between the standard types and the new Vec andįin types, we could be tempted to use these types in code that needs List type is no longer the standard list type for which many functionsĪre offered in the standard libraries, and the efficient Int type hasīeen replaced by a representation that counts sticks. In the transformation, though, we lost the original simplicity. Moreover, the new types force all invocations to provide a valid index. Being a total function, it can no longer fail at runtime. The types of theĪrguments have been changed to exclude from the domain the invalid The original function has been rewritten to be total. Presentation of these and the following examples with dependent types We include the types of Vec and Fin to illustrate some of theĬomplexity that needs to be introduced to work with dependent types,īut we are not overly concerned with their details. Let us consider an indexing function for lists.ĮlemAt1 :: Vec a n -> Fin n -> a elemAt1 xs i = case ( i, xs ) of ( FZ, x :> _ ) -> x ( FS i, _ :> xs ) -> elemAt1 xs i - Vec a n = lists of length n data Vec :: Type -> Nat -> Type where VNil :: Vec a Zero ( :> ) :: a -> Vec a n -> Vec a ( Succ n ) - Fin n = natural numbers smaller than n data Fin :: Nat -> Type where FZ :: Fin ( Succ n ) FS :: Fin n -> Fin ( Succ n ) data Nat = Zero | Succ Nat It leverages the power of tools specialized to reason about logic when The point is not so much that proofsĪre easier with Liquid Haskell, but rather that we are in front of anĪpproach that integrates well with a programming language as is, and yet That would usually require dependent types to ensure at compile time, canīe described in Liquid Haskell specifications. The conjecture that I pose is that many of the properties In an attempt to save the programmer the trouble of writing a proof. Identified, they are given to a theorem prover (an SMT solver specifically) The compiler ignores thisĬomment, but Liquid Haskell can find it. The specification is included in the programĪs a special comment inserted by the programmer. Meets some specification (not unlike Dafny, Wh圓, or F*). Liquid Haskell is a tool that can analyseĪ program and calculate proof obligations that would ensure that the program In this post, I’m arguing that Liquid Haskell offers an alternativeĪngle to approach this question. That doesn’t make them hard to read or change? But can we prove the correctness of our programs with a type-system Software has largely exceeded our capacity in the last severalĭecades. ![]() Our human brainsĬan only deal with a limited amount of complexity, and industrial-scale On the other hand, we want our programs to do what we mean, and we wantĪssistance from the computer to help us achieve this goal. Others, and the meaning of a piece of code should remain reasonably Instance, changes to one part of the code shouldn’t propagate to many On the one hand, we want our programs to be easy to maintain. There seems to be two design goals at conflict in current discussions. Simple Haskell to supporting full motion towards Which eventually sprang feelings that ranged from sticking to Then we had to deal with the programs resulting from all this innovation, Template Haskell, generalized algebraic data types (GADTs), data kinds, etc. To mentionĪ few, we had the IO monad, all flavors of type classes and constraints, Steadily innovated with language extensions and abstractions. Since the inception of the Haskell language, the community around it has
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