Dynamically typed languages

Dynamically typed languages such as Python and Ruby have experienced a rapid grown in popularity in recent times. However, there is much confusion as to what makes these languages interesting relative to statically typed languages, and little knowledge of their rich history. In this chapter I explore the general topic of dynamically typed languages, how they differ from statically typed languages, their history, and their defining features

Intensional Effect Polymorphism

Type-and-effect systems are a powerful tool for program construction and verification. We describe intensional effect polymorphism, a new foundation for effect systems that integrates static and dynamic effect checking. Our system allows the effect of polymorphic code to be intensionally inspected through a lightweight notion of dynamic typing. When coupled with parametric polymorphism, the powerful system utilizes runtime information to enable precise effect reasoning, while at the same time retains strong type safety guarantees. We build our ideas on top of an imperative core calculus with regions. The technical innovations of our design include a relational notion of effect checking, the use of bounded existential types to capture the subtle interactions between static typing and dynamic typing, and a differential alignment strategy to achieve efficiency in dynamic typing. We demonstrate the applications of intensional effect polymorphism in concurrent programming, security, graphical user interface access, and memoization

Dynamically typed languages.

Dynamically typed languages such as Python and Ruby have experienced a rapid grown in popularity in recent times. However, there is much confusion as to what makes these languages interesting relative to statically typed languages, and little knowledge of their rich history. In this chapter I explore the general topic of dynamically typed languages, how they differ from statically typed languages, their history, and their defining features

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

Preemptive type checking in dynamically typed programs

With the rise of languages such as JavaScript, dynamically typed languages have gained a strong foothold in the programming language landscape. These languages are very well suited for rapid prototyping and for use with agile programming methodologies. However, programmers would benefit from the ability to detect type errors in their code early, without imposing unnecessary restrictions on their programs.Here we describe a new type inference system that identifies potential type errors through a flow-sensitive static analysis. This analysis is invoked at a very late stage, after the compilation to bytecode and initialisation of the program. It computes for every expression the variable’s present (from the values that it has last been assigned) and future (with which it is used in the further program execution) types, respectively. Using this information, our mechanism inserts type checks at strategic points in the original program. We prove that these checks, inserted as early as possible, preempt type errors earlier than existing type systems. We further show that these checks do not change the semantics of programs that do not raise type errors.Preemptive type checking can be added to existing languages without the need to modify the existing runtime environment. We show this with an implementation for the Python language and demonstrate its effectiveness on a number of benchmarks