Alexandria

Closure conversion is a program transformation used by compilers to separate code from data. Previous accounts of closure conversion use only untyped target languages. Recent studies show that translating to typed target languages is a useful methodology for building compilers, because a compiler can use the types to implement e cient data representations, calling conventions, and tag-free garbage collection. Furthermore, type-based translations facilitate security and debugging through automatic type checking, as well as correctness arguments through the method of logical relations.

We present closure conversion as a type-directed, and type-preserving translation for both the simply-typed and the polymorphic λ-calculus. Our translations are based on a simple "closures as objects" principle: higher-order functions are viewed as objects consisting of a single method (the code) and a single instance variable (the environment). In the simply-typed case, the Pierce-Turner model of object typing where objects are packages of existential type suffices. In the polymorphic case, more careful tracking of type sharing is required. We exploit a variant of the Harper-Lillibridge "translucent type" formalism to characterize the types of polymorphic closures.

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Advanced type system features, such as GADTs, type classes, and type families have have proven to be invaluable language extensions for ensuring data invariants and program correctness among others. Unfortunately, they pose a tough problem for type inference, because they introduce local type assumptions.

In this article we present a novel constraint-based type inference approach for local type assumptions. Our system, called OutsideIn(X), is parameterised over the particular underlying constraint domain X, in the same way as HM(X). This stratification allows us to use a common metatheory and inference algorithm.

Going beyond the general framework, we also give a particular constraint solver for X = type classes GADTs type families, a non-trivial challenge in its own right.

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Eff is a programming language based on the algebraic approach to computational effects, in which effects are viewed as algebraic operations and effect handlers as homomorphisms from free algebras. Eff supports first-class effects and handlers through which we may easily define new computational effects, seamlessly combine existing ones, and handle them in novel ways. We give a denotational semantics of eff and discuss a prototype implementation based on it. Through examples we demonstrate how the standard effects are treated in eff, and how eff supports programming techniques that use various forms of delimited continuations, such as backtracking, breadth-first search, selection functionals, cooperative multi-threading, and others.

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To report a compiler bug, one must often find a small test case that triggers the bug. The existing approach to automated test-case reduction, delta debugging, works by removing substrings of the original input; the result is a concatenation of substrings that delta cannot remove. We have found this approach less than ideal for reducing C programs because it typically yields test cases that are too large or even invalid (relying on undefined behavior). To obtain small and valid test cases consistently, we designed and implemented three new, domain-specific test-case reducers. The best of these is based on a novel framework in which a generic fixpoint computation invokes modular transformations that perform reduction operations. This reducer produces outputs that are, on average, more than 25 times smaller than those produced by our other reducers or by the existing reducer that is most commonly used by compiler developers. We conclude that effective program reduction requires more than straightforward delta debugging.

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In this work three different statistical language identification methods are compared, and a detailed study of the influence on those systems of some basic parameters is performed. The analyzed parameters are the size of the train set, the amount of text that we want to classify and the languages the system is able to distinguish (it will be studied not only the influence of the number of languages but also the influence of wich are the considered languages)

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