Un système d'inférence de types fondé sur la saturation des contraintes de sous-typage

International audienceThis paper presents a powerful and flexible technique for defining type inference algorithms, on an ML-like language, that involve subtyping and whose soundness can be proved. We define a typing algorithm as a set of inference rules of three distinct forms: typing rules collect subtyping constraints to be satisfied, instantiation rules instantiate type schemes, and saturation rules specify how to check the validity and consistency of collected constraints. Essentially, type inference then proceeds in two intertwined phases: one that extracts constraints and the other that saturates the sets of constraints. Our technique extends easily to the treatment of high-level features such as polymorphism, overloading, variants and pattern-matching, or generalized algebraic data types (GADTs)

Relating Staged Computation to the Record Calculus

It has been previously shown that there is a close relation between record calculus and program generation (e.g. Lisp-like quasiquotations): A translation has been defined to convert staged expressions to record calculus expressions, and it has been shown that the call-by-value semantics of the staged and the record calculi are equivalent modulo the translation and admin reductions. In this work, we investigate the relation further. The contributions are twofold: (1) We fine-tune the previously shown relation between the two operational semantics, and obtain more precise results. In particular, we show that only two kinds of admin reductions suffice, and these reductions can be applied exhaustively. (2) We define a reverse translation that converts record calculus expressions back to the staged calculus, allowing us to go back and forth between the two calculi. We believe that these results provide an important step towards reusing already-existing record calculus static analyses to reason about staged expressions

Subtyping constraints in quasi-lattices

In this report, we show the decidability and NP-completeness of the satisfiability problem for non-structural subtyping constraints in quasi-lattices. This problem, first introduced by Smolka in 1989, is important for the typing of logic and functional languages. The decidability result is obtained by generalizing Trifonov and Smith's algorithm over lattices, to the case of quasi-lattices. Similarly, we extend Pottier's algorithm for computing explicit solutions to the case of quasi-lattices. Finally we evoke some applications of these results to type inference in constraint logic programming and functional programming languages

Safe, Fast and Easy: Towards Scalable Scripting Languages

Scripting languages are immensely popular in many domains. They are characterized by a number of features that make it easy to develop small applications quickly - flexible data structures, simple syntax and intuitive semantics. However they are less attractive at scale: scripting languages are harder to debug, difficult to refactor and suffers performance penalties. Many research projects have tackled the issue of safety and performance for existing scripting languages with mixed results: the considerable flexibility offered by their semantics also makes them significantly harder to analyze and optimize. Previous research from our lab has led to the design of a typed scripting language built specifically to be flexible without losing static analyzability. In this dissertation, we present a framework to exploit this analyzability, with the aim of producing a more efficient implementation. Our approach centers around the concept of adaptive tags: specialized tags attached to values that represent how it is used in the current program. Our framework abstractly tracks the flow of deep structural types in the program, and thus can efficiently tag them at runtime. Adaptive tags allow us to tackle key issues at the heart of performance problems of scripting languages: the framework is capable of performing efficient dispatch in the presence of flexible structures

Building a Typed Scripting Language

Since the 1990s, scripting languages (e.g. Python, Ruby, JavaScript, and many others) have gained widespread popularity. Features such as ad-hoc data manipulation, dynamic structural typing, and terse syntax permit rapid engineering and improve developer productivity. Unfortunately, programs written in scripting languages execute slower and are less scalable than those written in traditional languages (such as C or Java) due to the challenge of statically analyzing scripting languages' semantics. Although various research projects have made progress on this front, corner cases in the semantics of existing scripting languages continue to defy static analysis and software engineers must generally still choose between program performance and programmer performance when selecting a language. We address that dichotomy in this dissertation by designing a scripting language with the intent of statically analyzing it. We select a set of core primitives in which common language features such as object-orientation and case analysis can be encoded and give a sound and decidable type inference system for it. Our type theory is based on subtype constraint systems but is also closely related to abstract interpretation; we use this connection to guide development of the type system and to employ a novel type soundness proof strategy based on simulation. At the heart of our approach is a type indexed record we call the onion which supports asymmetric concatenation and dispatch; we use onions to formally encode a variety of features, including records, operator overloading, objects, and mixins. An optimistic call-site polymorphism model defined herein captures the ad-hoc, case-analysis-based reasoning often used in scripting languages. Although the language in this dissertation uses a particular set of core primitives, the strategy we use to design it is general: we demonstrate a simple, formulaic process for adding features such as integers and state