Context-Sensitive Abstract Interpretation of Dynamic Languages

There is a vast gap in the quality of IDE tooling between static languages like Java and dynamic languages like Python or JavaScript. Modern frameworks and libraries in these languages heavily use their dynamic capabilities to achieve the best ergonomics and readability. This has a side effect of making the current generation of IDEs blind to control flow and data flow, which often breaks navigation, autocompletion and refactoring. In this thesis we propose an algorithm that can bridge this gap between tooling for dynamic and static languages by statically analyzing dynamic metaprogramming and runtime reflection in programs. We use a technique called abstract interpretation to partially execute programs and extract information that is usually only available at runtime. Our algorithm has been implemented in a prototype analyzer that can analyze programs written in a subset of JavaScript.