Advanced Metaprogramming Queries: Code Volume & Complexity

This lecture discusses the challenges of managing large codebases due to the increasing volume and complexity of code written by more individuals. It explores the trend towards higher levels of abstraction in programming languages, tools like GitHub Co-Pilot, and the use of metaprogramming frameworks. The instructor presents the concept of a Code Database (CodeDB) as a solution to extract information from large volumes of code, using techniques such as static analysis, abstract interpretation, and Datalog queries. The lecture delves into the formal definition of problems, the generation of Datalog facts from typed abstract syntax trees, and the application of Datalog rules for program analysis. It also covers identifying inefficiencies, expressing programs in Datalog, and posing various queries for code analysis.