In the end, research on functional languages does little good unless they are used to write something other than compilers for functional languages. However, if one scans a typical functional programming conference or journal, one mainly sees papers on twists in language design, speed-ups in compiled code, clever new analyses, or refinements to semantic models. It much less common to see a paper that considers a functional language as a tool to some other practical end. We would like to see this change.

The Journal of Functional Programming carries, and will continue to carry, articles on all aspects of functional programming from lambda calculus theory to language design to implementation. But we have specially sought, and will continue to seek, papers on functional programming practice and experience.

Research and papers on practice and experience sometimes receive less attention because they are perceived as possessing less academic content. So we want to remind potential authors that we have published a number of papers on this topic in the past, and to spell out the criteria we apply to such papers.

Many, perhaps even most, algorithms that involve data structures are traditionally expressed by incremental updates of the data structures. In functional languages, however, incremental updates are usually both clumsy and inefficient, especially when the data structure is an array.

In functional languages, we instead prefer to express such array algorithms using monolithic arrays – wholesale creation of the final answer – both for succinctness of expression, efficiency (only one array created) and (sometimes) implicit parallelism. The ease with which the solution can be reformulated of course depends on the problem, and varies from trivial (e.g. matrix multiplication), to challenging (e.g. solving linear equation systems using Gauss elimination, which in fact can be done by creating only two arrays, recursively defined, of which one is the answer). Other problems have been notoriously resistant to attack; these usually involve some unpredictable processing order of the elements. One such problem is graph marking, i.e. marking the nodes reachable from a set of roots. Hitherto, no functional method has been known except emulating the traditional imperative solution (King & Launchbury, 1995; Launchbury & Peyton Jones, 1995).

The contribution of this paper is to show how this problem, and some related ones, can be solved using a novel array creation primitive, lazier than previous ones.

In this paper we give a big-step Structured Operational Semantics (SOS), in the style of Plotkin, Kahn and Milner, of a significant fragment of the functional programming language Scheme, including quote, eval, quasiquote and unquote. The SOS formalism allows us to discuss incrementally the various features of the language and to keep a low mathematical overhead, thus producing a rigorous account of the semantics of a ‘real’ programming language, which nonetheless has a pedagogical value. More specifically, we formalize four strictly increasing fragments of Scheme, using a number of formal systems which express the evaluation of expressions, the display of output results, and the handling of errors.

We describe a system that supports source-level integration of ML-like functional language code with ANSI C or Ada83 code. The system works by translating the functional code into type-correct, ‘vanilla’ C or Ada; it offers simple, efficient, type-safe inter-operation between new functional code components and ‘legacy’ third-generation-language components. Our translator represents a novel synthesis of techniques including user-parameterized specification of primitive types and operators; removal of polymorphism by code specialization; removal of higher-order functions using closure datatypes and interpretation; and aggressive optimization of the resulting first-order code, which can be viewed as encoding the result of a closure analysis. Programs remain fully typed at every stage of the translation process, using only simple, standard type systems. Target code runs at speeds comparable to the output of current optimizing ML compilers, even though handicapped by a conservative garbage collector.

In type theory a proposition is represented by a type, the type of its proofs. As a consequence, the equality relation on a certain type is represented by a binary family of types. Equality on a type may be conventional or inductive. Conventional equality means that one particular equivalence relation is singled out as the equality, while inductive equality – which we also call identity – is inductively defined as the ‘smallest reflexive relation’. It is sometimes convenient to know that the type representing a proposition is collapsed, in the sense that all its inhabitants are identical. Although uniqueness of identity proofs for an arbitrary type is not derivable inside type theory, there is a large class of types for which it may be proved. Our main result is a proof that any type with decidable identity has unique identity proofs. This result is convenient for proving that the class of types with decidable identities is closed under indexed sum. Our proof of the main result is completely formalized within a kernel fragment of Martin-Löf's type theory and mechanized using ALF. Proofs of auxiliary lemmas are explained in terms of the category theoretical properties of identity. These suggest two coherence theorems as the result of rephrasing the main result in a context of conventional equality, where the inductive equality has been replaced by, in the former, an initial category structure and, in the latter, a smallest reflexive relation.

This paper is a tutorial on defining recursive descent parsers in Haskell. In the spirit of one-stop shopping, the paper combines material from three areas into a single source. The three areas are functional parsers (Burge, 1975; Wadler, 1985; Hutton, 1992; Fokker, 1995), the use of monads to structure functional programs (Wadler, 1990, 1992a, 1992b), and the use of special syntax for monadic programs in Haskell (Jones, 1995; Peterson et al., 1996). More specifically, the paper shows how to define monadic parsers using do notation in Haskell.

Of course, recursive descent parsers defined by hand lack the efficiency of bottom-up parsers generated by machine (Aho et al., 1986; Mogensen, 1993; Gill and Marlow, 1995). However, for many research applications, a simple recursive descent parser is perfectly sufficient. Moreover, while parser generators typically offer a fixed set of combinators for describing grammars, the method described here is completely extensible: parsers are first-class values, and we have the full power of Haskell available to define new combinators for special applications. The method is also an excellent illustration of the elegance of functional programming.