We present a technique for automatically deriving test data generators from a given executable predicate representing the set of values we are interested in generating. The distribution of these generators is uniform over values of a given size. To make the generation efficient, we rely on laziness of the predicate, allowing us to prune the space of values quickly. In contrast, implementing test data generators by hand is labour intensive and error prone. Moreover, handwritten generators often have an unpredictable distribution of values, risking that some values are arbitrarily underrepresented. We also present a variation of the technique that has better performance, but where the distribution is skewed in a limited, albeit predictable way. Experimental evaluation of the techniques shows that the automatically derived generators are much easier to define than handwritten ones, and their performance, while lower, is adequate for some realistic applications.

It is well known that simply typed λ-terms can be used to represent numbers, as well as some other data types. We show that λ-terms of each fixed (but possibly very complicated) type can be described by a finite piece of information (a set of appropriately defined intersection types) and by a vector of natural numbers. On the one hand, the description is compositional: having only the finite piece of information for two closed λ-terms M and N, we can determine its counterpart for MN, and a linear transformation that applied to the vectors of numbers for M and N gives us the vector for MN. On the other hand, when a λ-term represents a natural number, then this number is approximated by a number in the vector corresponding to this λ-term. As a consequence, we prove that in a λ-term of a fixed type, we can store only a fixed number of natural numbers, in such a way that they can be extracted using λ-terms. More precisely, while representing k numbers in a closed λ-term of some type, we only require that there are k closed λ-terms M1,. . .,Mk such that Mi takes as argument the λ-term representing the k-tuple, and returns the i-th number in the tuple (we do not require that, using λ-calculus, one can construct the representation of the k-tuple out of the k numbers in the tuple). Moreover, the same result holds when we allow that the numbers can be extracted approximately, up to some error (even when we only want to know whether a set is bounded or not). All the results remain true when we allow the Y combinator (recursion) in our λ-terms, as well as uninterpreted constants.

The 12th International Symposium on Functional and Logic Programming was held in Kanazawa, Japan, June 4–6, 2014. The aim of the Functional and Logic Programming series of conferences is to bring together researchers interested in declarative programming including functional programming and logic programming. The series aims to promote cross-fertilization and integration between the two paradigms and it is traditionally organized and held in Japan.

Proving bounds on the resource consumption of a program by statically analyzing its source code is an important and well-studied problem. Automatic approaches for numeric programs with side effects usually apply abstract interpretation-based invariant generation to derive bounds on loops and recursion depths of function calls. This article presents an alternative approach to resource-bound analysis for numeric and heap-manipulating programs that uses type-based amortized resource analysis. As a first step towards the analysis of imperative code, the technique is developed for a first-order ML-like language with unsigned integers and arrays. The analysis automatically derives bounds that are multivariate polynomials in the numbers and the lengths of the arrays in the input. Experiments with example programs demonstrate two main advantages of amortized analysis over current abstract interpretation–based techniques. For one thing, amortized analysis can handle programs with non-linear intermediate values like f((n + m)2). For another thing, amortized analysis is compositional and works naturally for compound programs like f(g(x)).