The worker/wrapper transformation is a general way of changing the type of a recursive definition, usually applied with an eye to increasing algorithmic efficiency. This note identifies an infelicity in the program transformations presented by Gill & Hutton (The worker/wrapper transformation, J. Funct. Program., vol. 19, 2009, pp. 227–251) and proposes a new totally correct worker/wrapper fusion rule.

We present a simple and easy-to-understand explanation of ML type inference and parametric polymorphism within the framework of type monomorphism, as in the first order typed lambda calculus. We prove the equivalence of this system with the standard interpretation using type polymorphism, and extend the equivalence to include polymorphic fixpoints. The monomorphic interpretation gives a purely combinatorial understanding of the type inference problem, and is a classic instance of quantifier elimination, as well as an example of Gentzen-style cut elimination in the framework of the Curry-Howard propositions-as-types analogy.

The aim of our work is to be able to do fully formal, machine-verified proofs over Generic Haskell-style polytypic programs. In order to achieve this goal, we embed polytypic programming in the proof assistant Coq and provide an infrastructure for polytypic proofs. Polytypic functions are reified within Coq as a datatype and they can then be specialized by applying a dependently typed term specialization function. Polytypic functions are thus first-class citizens and can be passed as arguments or returned as results. Likewise, we reify polytypic proofs as a datatype and provide a lemma that a polytypic proof can be specialized to any datatype in the universe. The correspondence between polytypic functions and their polytypic proofs is very clear: programmers need to give proofs for, and only for, the same cases that they need to give instances for when they define the polytypic function itself. Finally, we discuss how to write (co)recursive functions and do (co)recursive proofs in a similar way that recursion is handled in Generic Haskell.

We describe the design, implementation and use of a new kind of profiling tool that yields valuable information about the memory use of lazy functional programs. The tool has two parts: a modified functional language implementation which generates profiling information during the execution of programs, and a separate program which converts this information to graphical form. With the aid of profile graphs, one can make alterations to a functional program which dramatically reduce its space consumption. We demonstrate this in the case of a genuine example - the first to which the tool was applied - for which the results are strikingly successful.

In the static analysis of functional programs, pushdown flow analysis and abstract garbage collection push the boundaries of what we can learn about programs statically. This work illuminates and poses solutions to theoretical and practical challenges that stand in the way of combining the power of these techniques. Pushdown flow analysis grants unbounded yet computable polyvariance to the analysis of return-flow in higher-order programs. Abstract garbage collection grants unbounded polyvariance to abstract addresses which become unreachable between invocations of the abstract contexts in which they were created. Pushdown analysis solves the problem of precisely analyzing recursion in higher-order languages; abstract garbage collection is essential in solving the “stickiness” problem. Alone, our benchmarks demonstrate that each method can reduce analysis times and boost precision by orders of magnitude. We combine these methods. The challenge in marrying these techniques is not subtle: computing the reachable control states of a pushdown system relies on limiting access during transition to the top of the stack; abstract garbage collection, on the other hand, needs full access to the entire stack to compute a root set, just as concrete collection does. Conditional pushdown systems were developed for just such a conundrum, but existing methods are ill-suited for the dynamic nature of garbage collection. We show fully precise and approximate solutions to the feasible paths problem for pushdown garbage-collecting control-flow analysis. Experiments reveal synergistic interplay between garbage collection and pushdown techniques, and the fusion demonstrates “better-than-both-worlds” precision.

Since Findler and Felleisen (Findler, R. B. & Felleisen, M. 2002) introduced higher-order contracts, many variants have been proposed. Broadly, these fall into two groups: some follow Findler and Felleisen (2002) in using latent contracts, purely dynamic checks that are transparent to the type system; others use manifest contracts, where refinement types record the most recent check that has been applied to each value. These two approaches are commonly assumed to be equivalent—different ways of implementing the same idea, one retaining a simple type system, and the other providing more static information. Our goal is to formalize and clarify this folklore understanding. Our work extends that of Gronski and Flanagan (Gronski, J. & Flanagan, C. 2007), who defined a latent calculus λC and a manifest calculus λH, gave a translation φ from λC to λH, and proved that if a λC term reduces to a constant, so does its φ-image. We enrich their account with a translation ψ from λH to λC and prove an analogous theorem. We then generalize the whole framework to dependent contracts, whose predicates can mention free variables. This extension is both pragmatically crucial, supporting a much more interesting range of contracts, and theoretically challenging. We define dependent versions of λH and two dialects (“lax” and “picky”) of λC, establish type soundness—a substantial result in itself, for λH — and extend φ and ψ accordingly. Surprisingly, the intuition that the latent and manifest systems are equivalent now breaks down: the extended translations preserve behavior in one direction, but in the other, sometimes yield terms that blame more.

This paper addresses the issue of analysing the run-time behaviour of lazy, higher-order functional programs. We examine the difference between the way that functional programmers and functional language implementors view program behaviour. Existing profiling techniques are discussed and a new technique is proposed which produces results that are straightforward for programmers to assimilate. The new technique, which we call lexical profiling, collects information about the run-time behaviour of functional programs, and reports the results with respect to the original source code rather than simply listing the actions performed at run-time. Lexical profiling complements implementation-specific profiling and is important because it provides a view of program activity which is largely independent of the underlying evaluation mechanism. Using the lexical profiler, programmers may easily relate results back to the source program. We give a full implementation of the lexical profiling technique for a sequential, interpretive graph reduction engine, and extensions for compiled and parallel graph reduction are discussed.

The worker/wrapper transformation is a technique for changing the type of a computation, usually with the aim of improving its performance. It has been used by compiler writers for many years, but the technique is little known in the wider functional programming community, and has never been described precisely. In this article we explain, formalise and explore the generality of the worker/wrapper transformation. We also provide a systematic recipe for its use as an equational reasoning technique for improving the performance of programs, and illustrate the power of this recipe using a range of examples.

This paper presents a library for programming with polymorphic dynamic types in the dependently typed programming language Agda. The resulting library allows dynamically typed values with a polymorphic type to be instantiated to a less general (possibly monomorphic) type without compromising type soundness.

A closed λ-term E is called an enumerator if

Here ⋀0 is the set of closed λ-terms,. is the set of natural numbers and the ⌜n⌝ are the Church's numerals λfx.fnx. Such an E is called reducing if, moreover

An ingenious recursion theoretic proof by Statman will be presented, showing that every enumerator is reducing. I do not know any direct proof.

This paper introduces a new recursion principle for inductively defined data modulo α-equivalence of bound names that makes use of Odersky-style local names when recursing over bound names. It is formulated in simply typed λ-calculus extended with names that can be restricted to a lexical scope, tested for equality, explicitly swapped and abstracted. The new recursion principle is motivated by the nominal sets notion of ‘α-structural recursion’, whose use of names and associated freshness side-conditions in recursive definitions formalizes common practice with binders. The new calculus has a simple interpretation in nominal sets equipped with name-restriction operations. It is shown to adequately represent α-structural recursion while avoiding the need to verify freshness side-conditions in definitions and computations. The paper is a revised and expanded version of Pitts (Nominal System T. In Proceedings of the 37th ACM SIGACT-SIGPLAN Symposium on Principles of Programming Languages, POPL 2010 (Madrid, Spain). ACM Press, pp. 159–170, 2010).

At the recent TC2 working conference on constructing programs from specifications (Moeller, 1991), I presented the derivation of a functional program for solving a problem posed by Knuth (1990). Slightly simplified, the problem was to construct a shortest decimal fraction representing a given integer multiple of 1/216. Later in the conference – and in a different context – Robert Dewar described a second problem that he had recently set as an examination question. In brief, the problem was to replace sequences of blanks in a file by tab characters wherever possible. Although Knuth's and Dewar's problems appear to have little in common, I suspected that both had the same ‘deep structure’ and were instances of a single general result about greedy algorithms. The purpose of this note is to bring the genral result to light and to unify the treatment of the two problems. We begin by describing the problems more precisely.

In this article, we extend the Barendregt Cube with ∏-conversion (which is the analogue of β-conversion, on product type level) and study its properties. We use this extension to separate the problem of whether a term is typable from the problem of what is the type of a term.

We present a new static system which reconstructs the types, regions and effects of expressions in an implicitly typed functional language that supports imperative operations on reference values. Just as types structurally abstract collections of concrete values, regions represent sets of possibly aliased reference values and effects represent approximations of the imperative behaviour on regions.

We introduce a static semantics for inferring types, regions and effects, and prove that it is consistent with respect to the dynamic semantics of the language. We present a reconstruction algorithm that computes the types and effects of expressions, and assigns regions to reference values. We prove the correctness of the reconstruction algorithm with respect to the static semantics. Finally, we discuss potential applications of our system to automatic stack allocation and parallel code generation.

Subtyping appears in a variety of programming languages, in the form of the ‘automatic coercion’ of integers to reals, Pascal subranges, and subtypes arising from class hierarchies in languages with inheritance. A general framework based on untyped lambda calculus provides a simple semantic model of subtyping and is used to demonstrate that an extension of Curry's type inference rules are semantically complete. An algorithm G for computing the most general typing associated with any given expression, and a restricted, optimized algorithm GA using only atomic subtyping hypotheses are developed. Both algorithms may be extended to insert type conversion functions at compile time or allow polymorphic function declarations as in ML.

List terminators are discussed as a new form of qualifier in list comprehensions for early termination of a list. The semantics of list terminators is expressed in terms of an optimal translation of list comprehensions (cf. Wadler, 1987) because it makes direct use of a continuation list.

This paper presents a semantics of self-adjusting computation and proves that the semantics is correct and consistent. The semantics introduces memoizing change propagation, which enhances change propagation with the classic idea of memoization to enable reuse of computations even when memory is mutated via side effects. During evaluation, computation reuse via memoization triggers a change-propagation algorithm that adapts the reused computation to the memory mutations (side effects) that took place since the creation of the computation. Since the semantics includes both memoization and change propagation, it involves both non-determinism (due to memoization) and mutation (due to change propagation). Our consistency theorem states that the non-determinism is not harmful: any two evaluations of the same program starting at the same state yield the same result. Our correctness theorem states that mutation is not harmful: Self-adjusting programs are compatible with purely functional programming. We formalize the semantics and its meta-theory in the LF logical framework and machine check our proofs using Twelf.

A common technique for improving the speed of program specialization is to specialize the program specializer itself on the program to be specialized, creating a custom program generator. Much research has been devoted to the problem of generating efficient program generators, which do not perform reductions at program generation time which could instead have been performed when the program generator was constructed. The conventional wisdom holds that only program specializers using binding-time approximations can be specialized into such efficient program generators. This paper argues that this is not the case, and demonstrates that the specialization of a nontrivial online program specializer similar to the original ‘naive MIX’ can indeed yield an efficient program generator. The key to our argument is that, while the use of binding-time information at program generator generation time is necessary for the construction of an efficient custom specializer, the use of explicit binding-time approximation techniques is not. This allows us to distinguish the problem at hand (i.e. the use of binding-time information during program generator generation) from particular solutions to that problem (i.e. offline specialization). We show that, given a careful choice of specializer data structures, and sufficiently powerful specialization techniques, binding-time information can be inferred and utilized without explicit approximations. This allows the construction of efficient, optimizing program generators from online program specializers.

The HM(X) system is a generalization of the Hindley/Milner system parameterized in the constraint domain X. Type inference is performed by generating constraints out of the program text, which are then solved by the domain-specific constraint solver X. The solver has to be invoked at the latest when type inference reaches a let node so that we can build a polymorphic type. A typical example of such an inference approach is Milner's algorithm W. We formalize an inference approach where the HM(X) type inference problem is first mapped to a CLP(X) program. The actual type inference is achieved by executing the CLP(X) program. Such an inference approach supports the uniform construction of type inference algorithms and has important practical consequences when it comes to reporting type errors. The CLP(X) style inference system, where X is defined by Constraint Handling Rules, is implemented as part of the Chameleon system.

Motivated by the search for a body of mathematical theory to support the semantics of computational effects, we first recall the relationship between Lawvere theories and monads on Set. We generalise that relationship from Set to an arbitrary locally presentable category such as Poset and ωCpo or functor categories such as [Inj, Set] and [Inj, ωCpo]. That involves allowing the arities of Lawvere theories to be extended to being size-restricted objects of the locally presentable category. We develop a body of theory at this level of generality, in particular explaining how the relationship between generalised Lawvere theories and monads extends Gabriel–Ulmer duality.