A common solution to the problem of handling list indexing efficiently in a functional program is to build a binary tree. The tree has the given list as frontier and is of minimum height. Each internal node of the tree stores size information (actually, the size of its left subtree) to direct the search for an element at a given position in the frontier. One application was considered in my previous pearl (Bird, 1997). There are two complementary methods for building such a tree, both of which can be implemented in linear time. One method is ‘recursive’, or top down, and works by splitting the list into two equal halves, recursively building a tree for each half, and then combining the two results. The other method is ‘iterative’, or bottom up, and works by first creating a list of singleton trees, and then repeatedly combining the trees in pairs until just one tree remains. The two methods lead to different trees, but in each case the result is a tree with smallest possible height.

The challenge of reasoning about programs with (multiple) effects such as mutation, jumps, or IO dates back to the inception of program semantics in the works of Strachey and Landin. Using monads to represent individual effects and the associated equational laws to reason about them proved exceptionally effective. Even then it is not always clear what laws are to be associated with a monad—for a good reason, as we show for non-determinism. Combining expressions using different effects brings challenges not just for monads, which do not compose, but also for equational reasoning: the interaction of effects may invalidate their individual laws, as well as induce emerging properties that are not apparent in the semantics of individual effects. Overall, the problems are judging the adequacy of a law; determining if or when a law continues to hold upon addition of new effects; and obtaining and easily verifying emergent laws.

We present a solution relying on the framework of (algebraic, extensible) effects, which already proved itself for writing programs with multiple effects. Equipped with a fairly conventional denotational semantics, this framework turns useful, as we demonstrate, also for reasoning about and optimizing programs with multiple interacting effects. Unlike the conventional approach, equational laws are not imposed on programs/effect handlers, but induced from them: our starting point hence is a program (model), whose denotational semantics, besides being used directly, suggests and justifies equational laws and clarifies side conditions. The main technical result is the introduction of the notion of equivalence modulo handlers (“modulo observation”) or a particular combination of handlers—and proving it to be a congruence. It is hence usable for reasoning in any context, not just evaluation contexts—provided particular conditions are met.

Concretely, we describe several realistic handlers for non-determinism and elucidate their laws (some of which hold in the presence of any other effect). We demonstrate appropriate equational laws of non-determinism in the presence of global state, which have been a challenge to state and prove before.

This paper was guest-edited by Harry Mairson and Bruce Kapron, for our intended Special Issue on Functional Programming and Computational Complexity. Other papers submitted for the special issue were either out-of-scope or otherwise unsuitable for JFP. Even though only one paper met their high standards, this did not make Harry and Bruce's job any easier, and we thank them for their efforts.

In previous work the author has introduced a lambda calculus SLR with modal and linear types which serves as an extension of Bellantoni–Cook's function algebra BC to higher types. It is a step towards a functional programming language in which all programs run in polynomial time. While this previous work was concerned with the syntactic metatheory of SLR in this paper we develop a semantics of SLR in terms of Chu spaces over a certain category of sheaves from which it follows that all expressible functions are indeed in PTIME. We notice a similarity between the Chu space interpretation and CPS translation which as we hope will have further applications in functional programming.

In this paper we describe the discrete interval encoding tree for storing subsets of types having a total order and a predecessor and a successor function. In the following, we consider for simplicity only the case for integer sets; the generalization is not difficult.

The discrete interval encoding tree is based on the observation that the set of integers {i[mid ]a[les ]i[les ]b} can be perfectly represented by the closed interval [a, b]. The general idea is to represent a set by a binary search tree of integers in which maximal adjacent subsets are each represented by an interval. For example, inserting the sequence of numbers 6, 9, 2, 13, 8, 14, 10, 7, 5 into a binary search tree, respectively, into a discrete interval encoding tree results in the tree structures shown in figure 1.

The efficiency of the interval representation, both in terms of space and time, improves with the density of the set, i.e. with the number of adjacencies between set elements. So what we propose is a ‘diet’ (discrete interval encoding tree) for ‘fat’ sets in the sense of ‘the same amount of information with less nodes’.

In ML-style module type theory, sealing often leads to situations in which type variables must leave scope, and this creates a need for signatures that avoid such variables. Unfortunately, in general, there is no best signature that avoids a variable, so modules do not always enjoy principal signatures. This observation is called the avoidance problem. In the past, the problem has been circumvented using a variety of devices for moving variables so they can remain in scope. These devices work, but have heretofore lacked a logical foundation. They have also lacked a presentation in which the dynamic semantics is given on the same phrases as the static semantics, which limits their applications. We can provide a best supersignature avoiding a variable by fiat, by adding an existential signature that is the least upper bound of its instances. This idea is old, but a workable metatheory has not previously been worked out. This work resolves the metatheoretic issues using ideas borrowed from focused logic. We show that the new theory results in a type discipline very similar to the aforementioned devices used in prior work. In passing, this gives a type-theoretic justification for the generative stamps used in the early days of the static semantics of ML modules. All the proofs are formalized in Coq.

Many variations upon the theme of parser combinators have been proposed, too many to list here, but the main idea is simple: A parser for phrases of type α is a function that takes an input string and produces results (x, rest), where x is a value of type α, and rest is the remainder of the input after the phrase with value x has been consumed. The results are often arranged into a list, because this allows a parser to signal failure with the empty list of results, an unambiguous success with one result, or multiple possibilities with a longer ‘list of successes’.

Haskell today provides good support not only for a functional programming style, but also for an imperative one. Elements of imperative programming are needed in applications such as web servers, or to provide efficient implementations of well-known algorithms, such as many graph algorithms. However, one element of imperative programming, the global variable, is surprisingly hard to emulate in Haskell. We discuss several existing methods, none of which is really satisfactory, and finally propose a new approach based on implicit parameters. This approach is simple, safe, and efficient, although it does reveal weaknesses in Haskell's present type system.

Traditional functional languages do not have an explicit distinction between binding times. It arises implicitly, however, as one typically instantiates a higher-order function with the arguments that are known, whereas the unknown arguments remain to be taken as parameters. The distinction between ‘known’ and ‘unknown’ is closely related to the distinction between binding times like ‘compile-time’ and ‘run-time’. This leads to the use of a combination of polymorphic type inference and binding time analysis for obtaining the required information about which arguments are known.

Following the current trend in the implementation of functional languages we then transform the run-time level of the program (not the compile-time level) into categorical combinators. At this stage we have a natural distinction between two kinds of program transformations: partial evaluation, which involves the compile-time level of our notation, and algebraic transformations (i.e., the application of algebraic laws), which involves the run-time level of our notation.

By reinterpreting the combinators in suitable ways we obtain specifications of abstract interpretations (or data flow analyses). In particular, the use of combinators makes it possible to use a general framework for specifying both forward and backward analyses. The results of these analyses will be used to enable program transformations that are not applicable under all circumstances.

Finally, the combinators can also be reinterpreted to specify code generation for various (abstract) machines. To improve the efficiency of the code generated, one may apply abstract interpretations to collect extra information for guiding the code generation. Peephole optimizations may be used to improve the code at the lowest level.

Since the mid '80s, compiler writers for functional languages (especially lazy ones) have been writing papers about identifying and exploiting thunks and lambdas that are used only once. However, it has proved difficult to achieve both power and simplicity in practice. In this paper, we describe a new, modular analysis for a higher order language, which is both simple and effective. We prove the analysis sound with respect to a standard call-by-need semantics, and present measurements of its use in a full-scale, state-of-the-art optimising compiler. The analysis finds many single-entry thunks and one-shot lambdas and enables a number of program optimisations. This paper extends our preceding conference publication (Sergey et al. 2014 Proceedings of the 41st Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages (POPL 2014). ACM, pp. 335–348) with proofs, expanded report on evaluation and a detailed examination of the factors causing the loss of precision in the analysis.

The observation that program structure follows data structure is a key lesson in introductory programming: good hints for possible program designs can be found by considering the structure of the data concerned. In particular, this lesson is a core message of the influential textbook “How to Design Programs” by Felleisen, Findler, Flatt, and Krishnamurthi. However, that book discusses using only the structure of input data for guiding program design, typically leading towards structurally recursive programs. We argue that novice programmers should also be taught to consider the structure of output data, leading them also towards structurally corecursive programs.

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.

We present a short proof of a folklore result: the Girard translation from the simply typed lambda calculus to the linear lambda calculus is fully complete. The proof makes use of a notion of logical predicates for intuitionistic linear logic. While the main result is of independent interest, this paper can be read as a tutorial on this proof technique for reasoning about relations between type theories.

In this paper we describe an implementation of an interactive version of the purely functional programming language Lazy ML (LML). The most remarkable fact about the interactive system is that it is written in a pure functional style using LML, yet the efficiency still compares favourably to other conventional interpretative systems.

We describe how the system is designed, and also the exception mechanism that was added to facilitate the handling of errors in the system.

This paper describes a data-intensive application written in a lazy functional language: a server for textual information retrieval. The design illustrates the importance of interoperability, the capability of interacting with code written in other programming languages. Lazy functional programming is shown to be a powerful and elegant means of accomplishing several desirable concrete goals: delivering initial results promptly, using space economically, and avoiding unnecessary I/O. Performance results, however, are mixed.

While the quality of the results of TEX's mathematical formula layout algorithm is convincing, its original description is hard to understand since it is presented as an imperative program with complex control flow and destructive manipulations of the data structures representing formulae. In this paper, we present a re-implementation of TEX's formula layout algorithm in the functional language SML, thereby providing a more readable description of the algorithm, extracted from the monolithical TEX system.

When type inference fails, it is often difficult to pinpoint the cause of the type error among many potential candidates. Generating informative messages to remove the type error is another difficult task due to the limited availability of type information. Over the last three decades many approaches have been developed to help debug type errors. However, most of these methods suffer from one or more of the following problems: (1) Being incomplete, they miss the real cause. (2) They cover many potential causes without distinguishing them. (3) They provide little or no information for how to remove the type error. Any one of this problems can turn the type-error debugging process into a tedious and ineffective endeavor. To address this issue, we have developed a method named counter-factual typing, which (1) finds a comprehensive set of error causes in AST leaves, (2) computes an informative message on how to get rid of the type error for each error cause, and (3) ranks all messages and iteratively presents the message for the most likely error cause. The biggest technical challenge is the efficient generation of all error messages, which seems to be exponential in the size of the expression. We address this challenge by employing the idea of variational typing that systematically reuses computations for shared parts and generates all messages by typing the whole ill-typed expression only once. We have evaluated our approach over a large set of examples collected from previous publications in the literature. The evaluation result shows that our approach outperforms previous approaches and is computationally feasible.

Values belonging to lazy data types have the advantage that sub-components can be accessed without evaluating the values as a whole: unneeded components remain unevaluated. A disadvantage is that often a large amount of space and time is required to handle lazy data types properly. Many special constructor cells are needed to ‘glue’ the individual parts of a composite object together and to store it in the heap. We present a way of representing data in functional languages which makes these special constructor cells superfluous. In some cases, no heap at all is needed to store this data. To make this possible, we introduce a new kind of data type: (partially) strict non-recursive data types. The main advantage of these types is that an efficient call-by-value mechanism can be used to pass arguments. A restrictive subclass of (partially) strict non-recursive data types, partially strict tuples, is treated more comprehensively. We also give examples of important classes of applications. In particular, we show how partially strict tuples can be used to define very efficient input and output primitives. Measurements of applications written in Concurrent Clean which exploit partially strict tuples have shown that speedups of 2 to 3 times are reasonable. Moreover, much less heap space is required when partially strict tuples are used.

Concepts and techniques from modern programming languages have much to offer to the security of computer systems. This special issue is devoted to research on those concepts and techniques. Over 60 active researchers working in this area were invited to contribute. In particular, a number of the participants of the Dagstuhl Seminar on Language-Based Security were encouraged to submit. Submitted articles were reviewed by 3–4 referees. As a result of the reviewing process, five articles were selected for inclusion in the special issue.