Some 10 years ago, Harper illustrated the powerful method of proof-directed debugging for developing programs with an article in this journal. Unfortunately, his example uses both higher-order functions and continuation-passing style, which is too difficult for students in an introductory programming course. In this pearl, we present a first-order version of Harper's example and demonstrate that it is easy to transform the final version into an efficient state machine. Our new version convinces students that the approach is useful, even essential, in developing both correct and efficient programs.

Functional languages provide an excellent framework for formulating biological algorithms in a naive form and then transforming them into an efficient form. This helps biologists understand what matters about programming and brings functional programming into the realm of the practical. In this column, we present an example from our MSc course on bioinformatics and report on our experiences teaching functional programming in this context.

The Ninth ACM SIGPLAN International Conference on Functional Programming (ICFP) took place on September 19–21 2004 in Snowbird, Utah. The scope of ICFP includes all languages that encourage programming with functions, with topics ranging from principles to practice, foundations to features, and abstractions to applications. The program committee, which I chaired, selected 21 papers from 80 submissions for presentation at the conference. Afterwards, the program committee invited the authors of nine papers to submit extended versions for this special issue of JFP. The seven papers that appear in this volume were reviewed, revised, and accepted following standard Journal of Functional Programming procedures. Reflecting the scope of the conference, the papers cover the theoretical foundations, implementation, design, programming techniques, and applications of functional languages.

A bag (literally and mathematically!) of marbles is deemed to be mingled if all the colours of the marbles are different. Given a positive integer $k$ and a collection of $m$ marbles, our objective is to try and extract as many mingled bags as possible from the collection, where each bag contains exactly $k$ marbles.

Lisp programmers have long used macros to extend their language. Indeed, their success has inspired macro notations for a variety of other languages, such as C and Java. There is, however, a paucity of effective pedagogic examples of macro use. This paper presents a short, non-trivial example that implements a construct not already found in mainstream languages. Furthermore, it motivates the need for tail-calls, as opposed to mere tail-recursion, and illustrates how support for tail-call optimization is crucial to support a natural style of macro-based language extension.

Even in statically typed languages it is useful to have certain invariants checked dynamically. Findler and Felleisen gave an algorithm for dynamically checking expressive higher-order types called contracts. They did not, however, give a semantics of contracts. The lack of a semantics makes it impossible to define and prove soundness and completeness of the checking algorithm. (Given a semantics, a sound checker never reports violations that do not exist under that semantics; a complete checker is – in principle – able to find violations when violations exist.) Ideally, a semantics should capture what programmers intuitively feel is the meaning of a contract or otherwise clearly point out where intuition does not match reality. In this paper we give an interpretation of contracts for which we prove the Findler-Felleisen algorithm sound and (under reasonable assumptions) complete. While our semantics mostly matches intuition, it also exposes a problem with predicate contracts where an arguably more intuitive interpretation than ours would render the checking algorithm unsound. In our semantics we have to make use of a notion of safety (which we define in the paper) to avoid unsoundness. We are able to eliminate the “leakage” of safety into the semantics by changing the language, replacing the original version of unrestricted predicate contracts with a restricted form. The corresponding loss in expressive power can be recovered by making safety explicit as a contract. This can be done either in ad-hoc fashion or by including general recursive contracts. The addition of recursive contracts has far-reaching implications, deeply affecting the formulation of our model and requiring different techniques for proving soundness.

The setting is a class on functional programming. There are four students, Anne, Jack, Mary and Theo.

Pure Type Systems (PTS) come in two flavours: domain-free systems with untyped $\lambda$-abstractions (i.e. of the form $\lambda{x}.{M}$); and domain-free systems with typed $\lambda$-abstractions (i.e. of the form $\lambda{x}{A}{M}$). Both flavours of systems are related by an erasure function $\er{.}$ that removes types from $\lambda$-abstractions. Preservation of Equational Theory, which states the equational theories of both systems coincide through the erasure function, is a property of functional and normalizing PTSs. In this paper we establish that Preservation of Equational Theory fails for some non-normalizing PTSs, including the PTS with $\ast:\ast$. The gist of our argument is to exhibit a typable expression $Y_H$ whose erasure $\er{Y}$ is a fixpoint combinator, but which is not a fixpoint combinator itself.

We formalize and prove the folklore theorem that the static delimited-control operators shift and reset can be simulated in terms of the dynamic delimited-control operators control and prompt. The proof is based on small-step operational semantics.

There's no maths involved. You solve the puzzle with reasoning and logic. Advice on how to play Sudoku, The Independent Newspaper

Polytypic programming is a way of defining type-indexed operations, such as map, fold and zip, based on type information. Run-time polytypic programming allows that type information to be dynamically computed – this support is essential in modern programming languages that support separate compilation, first-class type abstraction, or polymorphic recursion. However, in previous work we defined run-time polytypic programming with a type-passing semantics. Although it is natural to define polytypic programs as operating over first-class types, such a semantics suffers from a number of drawbacks. This paper describes how to recast that work in a type-erasure semantics, where terms represent type information in a safe manner. The resulting language is simple and easy to implement – we present a prototype implementation of the necessary machinery as a small Haskell library.

This paper introduces reFLect, a functional programming language with reflection features intended for applications in hardware design and verification. The reFLect language is strongly typed and similar to ML, but has quotation and antiquotation constructs. These may be used to construct and decompose expressions in the reFLect language itself. The paper motivates and presents the syntax and type system of this language, which brings together a new combination of pattern-matching and reflection features targeted specifically at our application domain. It also gives an operational semantics based on a novel use of contexts as expression constructors, and it presents a scheme for compiling reFLect programs using the same context mechanism.

At the heart of functional programming rests the principle of referential transparency, which in particular means that a function f applied to a value x always yields one and the same value y=f(x). This principle seems to be violated when contemplating the use of functions to describe probabilistic events, such as rolling a die: It is not clear at all what exactly the outcome will be, and neither is it guaranteed that the same value will be produced repeatedly. However, these two seemingly incompatible notions can be reconciled if probabilistic values are encapsulated in a data type.

Higher-order languages that encourage currying are typically implemented using one of two basic evaluation models: push/enter or eval/apply. Implementors use their intuition and qualitative judgements to choose one model or the other. Our goal in this paper is to provide, for the first time, a more substantial basis for this choice, based on our qualitative and quantitative experience of implementing both models in a state-of-the-art compiler for Haskell. Our conclusion is simple, and contradicts our initial intuition: compiled implementations should use eval/apply.

Every lazy functional programmer knows about the following approach to enumerating the positive rationals: generate a two-dimensional matrix (an infinite list of infinite lists), then traverse its finite diagonals (an infinite list of finite lists).

We introduce 2-3 finger trees, a functional representation of persistent sequences supporting access to the ends in amortized constant time, and concatenation and splitting in time logarithmic in the size of the smaller piece. Representations achieving these bounds have appeared previously, but 2-3 finger trees are much simpler, as are the operations on them. Further, by defining the split operation in a general form, we obtain a general purpose data structure that can serve as a sequence, priority queue, search tree, priority search queue and more.

XML data are described by types involving regular expressions. This raises the question of what language feature is convenient for manipulating such data. Previously, we have given an answer to this question by proposing regular expression pattern matching. However, since this construct is derived from ML pattern matching, it does not have an iteration functionality in itself, which makes it cumbersome to process data typed by Kleene stars. In this paper, we propose a novel programming feature regular expression filters. This construct extends the previous proposal by permitting pattern clauses to be closed under arbitrary regular expression operators. This yields many convenient programming idioms such as non-uniform processing of sequences and almost-copying of trees. We further develop a type inference mechanism that obtains (1) types for pattern variables that are locally precise with respect to the type of input values and (2) a type for the result of the whole filter expression that is also locally precise with respect to the types of the body expressions. We discuss how our construct is useful in the practice of XML processing and, in particular, how our type inference is crucial for avoiding changes of programs when types of data to be processed evolve frequently.

A generic function is a function that can be instantiated on many data types to obtain data type specific functionality. Examples of generic functions are the functions that can be derived in Haskell, such as show, read, and ‘==’. The recent years have seen a number of proposals that support the definition of generic functions. Some of the proposals define new languages, some define extensions to existing languages. As a common characteristic none of the proposals can be made to work within Haskell 98: they all require something extra, either a more sophisticated type system or an additional language construct. The purpose of this paper is to show that one can, in fact, program generically within Haskell 98 obviating to some extent the need for fancy type systems or separate tools. Haskell's type classes are at the heart of this approach: they ensure that generic functions can be defined succinctly and, in particular, that they can be used painlessly. We detail three different implementations of generics both from a practical and from a theoretical perspective.

A huge discrepancy between theory and practice exists in one popular application area of functional programming – spreadsheets. Although spreadsheets are the most frequently used (functional) programs, they fall short of the quality level that is expected of functional programs, which is evidenced by the fact that existing spreadsheets contain many errors, some of which have serious impacts. We have developed a template specification language that allows the definition of spreadsheet templates that describe possible spreadsheet evolutions. This language is based on a table calculus that formally captures the process of creating and modifying spreadsheets. We have developed a type system for this calculus that can prevent type, reference, and omission errors from occurring in spreadsheets. On the basis of the table calculus we have developed Gencel, a system for generating reliable spreadsheets. We have implemented a prototype version of Gencel as an extension of Excel.

Pattern matching is an important operation in functional programs. So far, pattern matching has been investigated in the context of structured terms. This article presents an approach to extend pattern matching to terms without (much of a) structure such as binaries which is the kind of data format that network applications typically manipulate. After introducing the binary datatype and a notation for matching binary data against patterns, we present an algorithm that constructs a decision tree automaton from a set of binary patterns. We then show how the pattern matching using this tree automaton can be made adaptive, how redundant tests can be avoided, and how we can further reduce the size of the resulting automaton by taking interferences between patterns into account. Since the size of the tree automaton is exponential in the worst case, we also present an alternative new approach to compiling binary pattern matching which is conservative in space and analyze its complexity properties. The effectiveness of our techniques is evaluated using standard packet filter benchmarks and on implementations of network protocols taken from actual telecom applications.