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

A Tutorial on the Universality and Expressiveness of Fold

In functional programming, fold is a standard operator that encapsulates a simple pattern of recursion for processing lists. This article is a tutorial on two key aspects of the fold operator for lists. First of all, we emphasize the use of the universal property of fold both as a proof principle that avoids the need for inductive proofs, and as a definition principle that guides the transformation of recursive functions into definitions using fold. Secondly, we show that even though the pattern of recursion encapsulated by fold is simple, in a language with tuples and functions as first-class values the fold operator has greater expressive power than might first be expected

Down with variables

Techn. Report DI-PURe-05.06.01The subject of this paper is point-free functional programming in Haskell. By this we mean writing programs using categorically-inspired combinators, algebraic data types defined as fixed points of functors, and impicit recursion through the use of type-parameterized recursion patterns. This style of programming is appropriate for program calculation (reasoning about programs equationally), but difficult to actually use in practice - most programmers use a mixture of the above elements with explicit recursion and manipulation of arguments. In this paper we present a mechanism that allows programmers to convert classic point-wise code into point-free style, and a Haskell library that enables the direct execution of the resulting code. Together, they make possible the use of point-free either as a direct programming style or as a domain into which programs can be transformed before being subject to further manipulation

Automatic visualization of recursion trees: a case study on generic programming

Although the principles behind generic programming are already well understood, this style of programming is not widespread and examples of applications are rarely found in the literature. This paper addresses this shortage by presenting a new method, based on generic programming, to automatically visualize recursion trees of functions written in Haskell. Crucial to our solution is the fact that almost any function definition can be automatically factorized into the composition of a fold after an unfold of some intermediate data structure that models its recursion tree. By combining this technique with an existing tool for graphical debugging, and by extensively using Generic Haskell, we achieve a rather concise and elegant solution to this problem.Fundação para a Ciência e a Tecnologia - POSI/CHS/44304/2002

A principled approach to programming with nested types in Haskell

Initial algebra semantics is one of the cornerstones of the theory of modern functional programming languages. For each inductive data type, it provides a Church encoding for that type, a build combinator which constructs data of that type, a fold combinator which encapsulates structured recursion over data of that type, and a fold/build rule which optimises modular programs by eliminating from them data constructed using the buildcombinator, and immediately consumed using the foldcombinator, for that type. It has long been thought that initial algebra semantics is not expressive enough to provide a similar foundation for programming with nested types in Haskell. Specifically, the standard folds derived from initial algebra semantics have been considered too weak to capture commonly occurring patterns of recursion over data of nested types in Haskell, and no build combinators or fold/build rules have until now been defined for nested types. This paper shows that standard folds are, in fact, sufficiently expressive for programming with nested types in Haskell. It also defines buildcombinators and fold/build fusion rules for nested types. It thus shows how initial algebra semantics provides a principled, expressive, and elegant foundation for programming with nested types in Haskell

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

The generic HASKELL user's guide : version 0.99 - Amber release

Software development often consists of designing datatypes around which functionality is added. Some functionality is datatype specific, whereas other functionality is defined on almost all datatypes in such a way that it depends only on the structure of the datatype. A function that works on many datatypes in this way is called a generic (or polytypic) function. Examples of generic functionality include storing a value in a database, editing a value, comparing two values for equality, and pretty-printing a value. Since datatypes often change and new datatypes are introduced, we have developed Generic HASKELL which supports generic definitions to save the programmer from (re)writing instances of generic functions. Generic HASKELL extends the functional programming language Haskell [5] with, among other things, a construct for defining type-indexed values with kind-indexed types, based on recent work by Hinze [2]. These values can be specialised to all Haskell datatypes, facilitating wider application of generic programming than provided by earlier systems such as PolyP [4]

Polytipic recursion patterns

Comunicação apresentada em SBLP'00, Proc. 4th Brazilian Symposium on Programming Languages, Recife, May, 2000Recursive schemes over inductive data structures have been recognized as category-theoretic universals, yielding a handful of equational laws for program construction and transformation. This paper introduces the implementation of such recursion patterns as type parametric, or polytypic, functionals in the CAMILA prototyping language. Several examples are discussed

Type Generic Observing

Observing intermediate values helps to understand what is going on when your program runs. Gill presented an observation method for lazy functional languages that preserves the program's semantics. However, users need to define for each type how its values are observed: a laborious task and strictness of the program can easily be affected. Here we define how any value can be observed based on the structure of its type by applying generic programming frameworks. Furthermore we present an extension to specify per observation point how much to observe of a value. We discuss especially functional values and behaviour based on class membership in generic programming frameworks