Many large software systems originate from untyped scripting language code. While good for initial development, the lack of static type annotations can impact code-quality and performance in the long run. We present an approach for integrating untyped code and typed code in the same system to allow an initial prototype to smoothly evolve into an efficient and robust program. We introduce like types , a novel intermediate point between dynamic and static typing. Occurrences of like types variables are checked statically within their scope but, as they may be bound to dynamic values, their usage is checked dynamically. Thus like types provide some of the benefits of static typing without decreasing the expressiveness of the language. We provide a formal account of like types in a core object calculus and evaluate their applicability in the context of a new scripting language.

Integrating typed and untyped code in a scripting language

Compile-time metaprogramming has been proven immensely useful enabling programming techniques such as language virtualization, embedding of external domain-specific languages, self-optimization, and boilerplate generation among many others.In the recent production release of Scala 2.10 we have introduced macros, an experimental facility which gives its users compile-time metaprogramming powers. Alongside of the mainline release of Scala Macros, we have also introduced other macro flavors, which provide their users with different interfaces and capabilities for interacting with the Scala compiler.In this paper, we show how the rich syntax and static types of Scala synergize with macros, through a number of real case studies using our macros (some of which are production systems) such as language virtualization, type providers, materialization of type class instances, type-level programming, and embedding of external DSLs. We explore how macros enable new and unique ways to use pre-existing language features such as implicits, dynamics, annotations, string interpolation and others, showing along the way how these synergies open up new ways of dealing with software development challenges.

Scala macros: let our powers combine!: on how rich syntax and static types work with metaprogramming

ENTAILMENT: The Logic of Relevance and Necessity (Volume I)

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. Simulations in existing systems suggest that this apparently self-supporting setup is achievable without paradox or infinite regress.

The gentle art of levitation

This thesis aims at making Datatype Generic Programming more useful in practice. We extend the fixed-point view as introduced by PolyP in order to support systems of mutually recursive datatypes. The new improved view allows us to define generic functions that were previously usable only on regular datatypes. Such examples of generic functions include the zipper, generic rewriting and fold. This thesis explores the problem of generic rewriting and that of extending a datatype with a meta-variable case without modifying its definition. We also compare various approaches to generic programming in Haskell. Finally, we apply generic programming to the problem of generating well-typed terms.

/pdf/towards-getting-generic-programming-ready-for-prime-time-86ck4ljhsh.pdf

Towards Getting Generic Programming Ready for Prime Time

Many datatype-generic functions need access to the recursive positions in the structure of the datatype, and therefore adopt a fixed point view on datatypes. Examples include variants of fold that traverse the data following the recursive structure, or the Zipper data structure that enables navigation along the recursive positions. However, Hindley-Milner-inspired type systems with algebraic datatypes make it difficult to express fixed points for anything but regular datatypes. Many real-life examples such as abstract syntax trees are in fact systems of mutually recursive datatypes and therefore excluded. Using Haskell's GADTs and type families, we describe a technique that allows a fixed-point view for systems of mutually recursive datatypes. We demonstrate that our approach is widely applicable by giving several examples of generic functions for this view, most prominently the Zipper.

/pdf/generic-programming-with-fixed-points-for-mutually-recursive-4x35on2jvp.pdf

Generic programming with fixed points for mutually recursive datatypes

Previous implementations of generic rewriting libraries have a number of limitations: they require the user to either adapt the datatype on which rewriting is applied, or the rewriting rules are specified as functions, which makes it hard or impossible to document, test, and analyse them. We describe a library that demonstrates how to overcome these limitations by defining rules in terms of datatypes, and show how to use a type-indexed datatype to automatically extend a datatype for syntax trees with a case for metavariables. We then show how rewrite rules can be implemented without any knowledge of how the datatype is extended with metavariables. We use Haskell, extended with associated type synonyms, to implement both type-indexed datatypes and generic functions. We analyse the performance of our library and compare it with other approaches to generic rewriting.

/pdf/a-lightweight-approach-to-datatype-generic-rewriting-55nlwnn6dh.pdf

A lightweight approach to datatype-generic rewriting

A generic function is defined by induction on the structure of types. The structure of a data type can be defined in several ways. For example, in PolyP a pattern functor gives the structure of a data type viewed as a fixed point, and in Generic Haskell a structural representation type gives an isomorphic type view of a data type in terms of sums of products. Depending on this generic view on the structure of data types, some generic functions are easier, more difficult, or even impossible to define. Furthermore, the efficiency of some generic functions can be improved by choosing a different view. This paper introduces generic views on data types and shows why they are useful. Furthermore, it shows how generic views have been added to Generic Haskell, an extension of the functional programming language Haskell that supports the construction of generic functions. The separation between inductive definitions on type structure and generic views allows us to combine many approaches to generic programming in a single framework.

Generic views on data types

/pdf/generic-views-on-data-types-565z25fbln.pdf

Datatype-generic programming increases program reliability by reducing code duplication and enhancing reusability and modularity. Several generic programming libraries for Haskell have been developed in the past few years. These libraries have been compared in detail with respect to expressiveness, extensibility, typing issues, etc., but performance comparisons have been brief, limited, and preliminary. It is widely believed that generic programs run slower than hand-written code. In this paper we present an extensive benchmark suite for generic functions and analyze the potential for automatic code optimization at compilation time. Our benchmark confirms that generic programs, when compiled with the standard optimization flags of the Glasgow Haskell Compiler (GHC), are substantially slower than their hand-written counterparts. However, we also find that more advanced optimization capabilities of GHC can be used to further optimize generic functions, sometimes achieving the same efficiency as hand-written code.

/pdf/optimizing-generics-is-easy-zetl63g9bw.pdf

Stefan Holdermans

Papers

Generic programming with fixed points for mutually recursive datatypes

A lightweight approach to datatype-generic rewriting

Generic views on data types

Generic views on data types

Optimizing generics is easy