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

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