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This paper describes a new approach to generic functional programming, which allows us to define functions generically for all datatypes expressible in Haskell. A generic function is one that is defined by induction on the structure of types. Typical examples include pretty printers, parsers, and comparison functions. The advanced type system of Haskell presents a real challenge: datatypes may be parameterized not only by types but also by type constructors, type definitions may involve mutual recursion, and recursive calls of type constructors can be arbitrarily nested. We show that--- despite this complexity---a generic function is uniquely defined by giving cases for primitive types and type constructors (such as disjoint unions and cartesian products). Given this information a generic function can be specialized to arbitrary Haskell datatypes. The key idea of the approach is to model types by terms of the simply typed -calculus augmented by a family of recursion operators. While co...

Generic Haskell is an extension of Haskell that supports the construction of generic programs. During the development of several applications, such as an XML editor and compressor, we encountered a number of limitations with the existing (Classic) Generic Haskell language, as implemented by the current Generic Haskell compiler. Specifically,

Abstract. Generic Haskell is an extension of Haskell that supports the construction of generic programs. These lecture notes describe the basic constructs of Generic Haskell and highlight the underlying theory. Generic programming aims at making programming more effective by making it more general. Generic programs often embody non-traditional kinds of polymorphism. Generic Haskell is an extension of Haskell [38] that supports the construction of generic programs. Generic Haskell adds to Haskell the notion of structural polymorphism, the ability to define a function (or a type) by induction on the structure of types. Such a function is generic in the sense that it works not only for a specific type but for a whole class of types. Typical examples include equality, parsing and pretty printing, serialising, ordering, hashing, and so on. The lecture notes on Generic Haskell are organized into two parts. This first part motivates the need for genericity, describes the basic constructs of Generic Haskell, puts Generic Haskell into perspective, and highlights the underlying theory. The second part entitled “Generic Haskell: applications ” delves deeper into the language discussing three non-trivial applications of Generic Haskell: generic dictionaries, compressing XML documents, and a generic version of the zipper data type. The first part is organized as follows. Section 1 provides some background discussing type systems in general and the type system of Haskell in particular. Furthermore, it motivates the basic constructs of Generic Haskell. Section 2 takes a closer look at generic definitions and shows how to define some popular generic functions. Section 3 highlights the theory underlying Generic Haskell and discusses its implementation. Section 4 concludes. 1

Abstract. Generic programming enables the programmer to define functions by induction on the structure of types. Defined once, such a generic function can be used to generate a specialized function for any user defined data type. Several ways to support generic programming in functional languages have been proposed, each with its own pros and cons. In this paper we describe a combination of two existing approaches, which has the advantages of both of them. In our approach overloaded functions with class variables of an arbitrary kind can be defined generically. A single generic definition defines a kind-indexed family of overloaded functions, one for each kind. For instance, the generic mapping function generates an overloaded mapping function for each kind. Additionally, we propose a separate extension that allows to specify a customized instance of a generic function for a type in terms of the generated instance for that type. 1

Writing boilerplate code is a royal pain. Generic programming promises to alleviate this pain by allowing the programmer to write a generic "recipe" for boilerplate code, and use that recipe in many places. In earlier work we introduced the "Scrap your boilerplate " approach to generic programming, which cunningly exploits Haskell's existing type-class mechanism to support generic transformations and queries.

Lacking support for generic traversal, functional programming languages suffer from a scalability problem when applied to largescale program transformation problems. As a solution, we introduce functional strategies: typeful generic functions that not only can be applied to terms of any type, but which also allow generic traversal into subterms.

Abstract. Many problems call for a mixture of generic and speci c programming techniques. We propose a polytypic programming approach based on generalised (monadic) folds where a separation is made between basic fold algebras that model generic behaviour and updates on these algebras that model speci c behaviour. We identify particular basic algebras as well as some algebra combinators, and we show how these facilitate structured programming with updatable fold algebras. This blend of genericity and speci city allows programming with folds to scale up to applications involving large systems of mutually recursive datatypes. Finally, we address the possibility of providing generic de nitions for the functions, algebras, and combinators that we propose. 1

syntax representation in which variables are not leaves and extraction of variable names from expressions. strategies free-vars2(getvars, boundvars) = rec x(split(getvars <+ ![], split(collect-kids(x), boundvars <+ ![]); diff); union) Figure 15: Algorithm for collecting free variables that takes variables in subterms of variables into account. A variant of this algorithm taking into account binding positions can be created analogously to Figure 13 11 Section: Case Studies RENAMING BOUND VARIABLES Renaming of bound variables depends on the shape of variables and the shape of binding constructs. For binding constructs, in addition to determining what variables are bound and in which arguments they are binding, it is necessary to declare where new variables should be pasted. In order to keep track of renamings it is also required to distribute an environment along with the renaming traversal. Renaming of bound variables is used to prevent name clashes between variables, for exa...

Several generic programs for converting values from regular datatypes to some other format, together with their corresponding inverses, are constructed. Among the formats considered are shape plus contents, compact bit streams and pretty printed strings. The different data conversion programs are constructed using John Hughes' arrow combinators along with a proof that printing (from a regular datatype to another format) followed by parsing (from that format back to the regular datatype) is the identity. The printers and parsers are described in PolyP, a polytypic extension of the functional language Haskell.