There is a longstanding interest in our community in language extension and mechanisms for easily adding (domain-specific) features to programming languages. One notion of extensibility argues that language extensions should be independently developed such that a programmer can pick the ones needed for their task at hand and compose them with a host language. Previously we have developed some ways to ensure that the composition of language and extension specifications leads to deterministic parsers and well-defined attribute grammars to implement the language.

AI has slowly (?) made it into the reality of software engineering (SE) and programming language (PL) research and teaching over the last 10-20 years. For example, in research, think of grammar inference and pretty-printing models in PL; think of bug-prediction models and search-based software re-engineering in SE.

Computers execute software to do the tasks we expect from them. This software is written by human beings, we call this programming. The most common way to program is by writing text in a programming language. A programming language is very structured so we can be precise, but ultimately these languages are still for humans to read and write. In order to execute the written program, we need to translate it to a list of tiny instruction steps that the hardware of the computer can execute. This translation is also automated with software. The most common forms this software takes is (1) interpreters that execute a program live as they read it, or (2) compilers that translate the entire program for later execution.