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)

A Pure Embedding of Roles: Exploring 4-dimensional Dispatch for Roles in Structured Contexts

Present-day software systems have to fulfill an increasing number of requirements, which makes them more and more complex. Many systems need to anticipate changing contexts or need to adapt to changing business rules or requirements. The challenge of 21th-century software development will be to cope with these aspects. We believe that the role concept offers a simple way to adapt an object-oriented program to its changing context. In a role-based application, an object plays multiple roles during its lifetime. If the contexts are represented as first-class entities, they provide dynamic views to the object-oriented program, and if a context changes, the dynamic views can be switched easily, and the software system adapts automatically. However, the concepts of roles and dynamic contexts have been discussed for a long time in many areas of computer science. So far, their employment in an existing object-oriented language requires a specific runtime environment. Also, classical object-oriented languages and their runtime systems are not able to cope with essential role-specific features, such as true delegation or dynamic binding of roles. In addition to that, contexts and views seem to be important in software development. The traditional code-oriented approach to software engineering becomes less and less satisfactory. The support for multiple views of a software system scales much better to the needs of todays systems. However, it relies on programming languages to provide roles for the construction of views. As a solution, this thesis presents an implementation pattern for role-playing objects that does not require a specific runtime system, the SCala ROles Language (SCROLL). Via this library approach, roles are embedded in a statically typed base language as dynamically evolving objects. The approach is pure in the sense that there is no need for an additional compiler or tooling. The implementation pattern is demonstrated on the basis of the Scala language. As technical support from Scala, the pattern requires dynamic mixins, compiler-translated function calls, and implicit conversions. The details how roles are implemented are hidden in a Scala library and therefore transparent to SCROLL programmers. The SCROLL library supports roles embedded in structured contexts. Additionally, a four-dimensional, context-aware dispatch at runtime is presented. It overcomes the subtle ambiguities introduced with the rich semantics of role-playing objects. SCROLL is written in Scala, which blends a modern object-oriented with a functional programming language. The size of the library is below 1400 lines of code so that it can be considered to have minimalistic design and to be easy to maintain. Our approach solves several practical problems arising in the area of dynamical extensibility and adaptation

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

Types for Flexible Objects

Abstract. Scripting languages are popular in part due to their ex-tremely flexible objects. Features such as dynamic extension, mixins, and first-class messages improve programmability and lead to concise code. But attempts to statically type these features have met with lim-ited success. Here we present TinyBang, a small typed language in which flexible object operations can be encoded. We illustrate this flexibility by solving an open problem in OO literature: we give an encoding where objects can be extended after being messaged without compromising the expressiveness of subtyping. TinyBang’s subtype constraint system en-sures that all types are completely inferred; there are no data declarations or type annotations. We formalize TinyBang and prove the type system is sound and decidable; all examples in the paper run in our most recent implementation.

Types for Flexible Objects

Scripting languages are popular in part due to their extremely flexible objects. These languages support numerous object features, including dynamic extension, mixins, traits, and first-class messages. While some work has succeeded in typing these features individually, the solutions have limitations in some cases and no project has combined the results. In this paper we define TinyBang, a small typed language containing only functions, labeled data, a data combinator, and pattern matching. We show how it can directly express all of the aforementioned flexible object features and still have sound typing. We use a subtype constraint type inference system with several novel extensions to ensure full type inference; our algorithm refines parametric polymorphism for both flexibility and efficiency. We also use TinyBang to solve an open problem in OO literature: objects can be extended after being messaged without loss of width or depth subtyping and without dedicated metatheory. A core subset of TinyBang is proven sound and a preliminary implementation has been constructed. 1