Datatype-generic programming is defining functions that depend on the structure, or “shape”, of datatypes. It has been around for more than 10 years, and a lot of progress has been made, in particular in the lazy functional programming language Haskell. There are more than 10 proposals for generic programming libraries or language extensions for Haskell. To compare and characterize the many generic programming libraries in a typed functional language, we introduce a set of criteria and develop a generic programming benchmark: a set of characteristic examples testing various facets of datatype-generic programming. We have implemented the benchmark for nine existing Haskell generic programming libraries and present the evaluation of the libraries. The comparison is useful for reaching a common standard for generic programming, but also for a programmer who has to choose a particular approach for datatype-generic programming.

Datatypes which di#er inessentially in their names and structure are said to be isomorphic; for example, a ternary product is isomorphic to a nested pair of binary products. In some canonical cases, the conversion function is uniquely determined solely by the two types involved.

Abstract. The last decade has seen a number of approaches to datatype-generic programming: PolyP, Functorial ML, ‘Scrap Your Boilerplate’, Generic Haskell, ‘Generics for the Masses’, etc. The approaches vary in sophistication and target audience: some propose full-blown programming languages, some suggest libraries, some can be seen as categorical programming methods. In these lecture notes we compare the various approaches to datatype-generic programming in Haskell. We introduce each approach by means of example, and we evaluate it along different dimensions (expressivity, ease of use, etc). 1

We exhibit the rationale behind the design of Epigram, a dependently typed programming language and interactive program development system, using refinements of a well known program—merge sort—as a running example. We discuss its relationship with other proposals to introduce aspects of dependent types into functional programming languages and sketch some topics for further work in this area. 1.

Abstract. Strategic programming is generic programming with the use of strategies. A strategy is a generic data-processing action which can traverse into heterogeneous data structures while mixing uniform and type-specific behaviour. With strategic programming, one gains full control over the application of basic actions, most notably full traversal control. Using a combinator style, traversal schemes can be defined, and actual traversals are obtained by passing the problem-specific ingredients as parameters to suitable schemes. The prime application domain for strategic programming is program transformation and analysis. In this paper, we provide a language-independent definition that generalises over existing incarnations of this idiom in term rewriting, functional programming, and object-oriented programming.

Contents SKELL? 5 1.1 Generic programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 SKELL overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2 Installation 6 2.1 System requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.2 Installing the binary distribution (Linux, Mac OS X) . . . . . . . . . . . . . . 6 2.3 Installing the binary distribution (Windows) . . . . . . . . . . . . . . . . . . . 7 2.4 Building from source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.5 Running gh . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.6 Command line flags . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.7 General overview of compilation . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.8 Compiling and running the generated code . . . . . . . . . . . . . . . . . . . 9 SKELL: The Language 10 3.1 Special Parentheses . . . . . . . . . . . . . . . . . . . .

We present a closed dependent type theory whose inductive types are given not by a scheme for generative declarations, but by encoding in a universe. Each inductive datatype arises by interpreting its description—a first-class value in a datatype of descriptions. Moreover, the latter itself has a description. Datatype-generic programming thus becomes ordinary programming. We show some of the resulting generic operations and deploy them in particular, useful ways on the datatype of datatype descriptions itself. Surprisingly this apparently self-supporting setup is achievable without paradox or infinite regress. 1.

Abstract. We present four constructions for standard equipment which can be generated for every inductive datatype: case analysis, structural recursion, no confusion, acyclicity. Our constructions follow a two-level approach—they require less work than the standard techniques which inspired them [11, 8]. Moreover, given a suitably heterogeneous notion of equality, they extend without difficulty to inductive families of datatypes. These constructions are vital components of the translation from dependently typed programs in pattern matching style [7] to the equivalent programs expressed in terms of induction principles [21] and as such play a crucial behind-the-scenes rôle in Epigram [25]. 1

Tony Hoare has always been a leader in writing down and proving properties of programs. To prove properties of programs automatically, the most widely used technology today is by far the ubiquitous type checker. Alas, static type systems inevitably exclude some good programs and allow some bad ones. Thus motivated, we describe some fun we have been having with Haskell, by making the type system more expressive without losing the benefits of automatic proof and compact expression. Specifically, we offer a programmer’s tour of so-called type families, a recent extension to Haskell that allows functions on types to be expressed as straightforwardly as functions on values. This facility makes it easier for programmers to effectively extend the compiler by writing functional programs that execute during type-checking. This paper gives a programmer’s tour of type families as they are supported in GHC. Source code for all the examples is available at

Self-representation – the ability to represent programs in their own language – has important applications in reflective languages and many other domains of programming language design. Although approaches to designing typed program representations for sublanguages of some base language have become quite popular recently, the question whether a fully metacircular typed selfrepresentation is possible is still open. This paper makes a big step towards this aim by defining the F ∗ ω calculus, an extension of the higher-order polymorphic lambda calculus Fω that allows typed self-representations. While the usability of these representations for metaprogramming is still limited, we believe that our approach makes a significant step towards a new generation of reflective languages that are both safe and efficient.