The Taub Faculty of Computer Science Events and Talks

Programming languages have many similarities, and so, when writing a source-to-source transformation on one language, it would be nice to reuse code from a similar transformation for a different language. This is a fundamentally difficult problem, and previous attempts have either resorted to reimplementing the same transformation for many languages, or at best reducing multiple languages to a common intermediate representations, which necessarily destroys information and produces poor source-to-source results.

We present a new representation for programs called incremental parametric syntax, and show how it enables us to construct source-to-source transformations so that we can implement them once, and run them on each of C, Java, JavaScript, Lua, and Python. Instead of constructing a common representation for the languages, incremental parametric syntax allows us to instead construct a family of representations sharing common components, each specialized to a single-language, and to semi-automatically generate them from existing syntax definitions. Our evaluation shows that (1) once a transformation is written, relatively little work is required to configure it for a new language (2) transformations built this way output readable code which preserve the structure of the original, according to participants in our human study, and (3) despite dealing with many languages, our transformations can still handle language corner-cases, and pass 90% of compiler test suites.

Bio:
After winning the "20 Under 20" Thiel Fellowship, Jimmy Koppel graduated early from Carnegie Mellon University to found Tarski Technologies, a startup building commercial program repair technology. In 2014, he joined Apptimize as the third employee, where he obtained four pending patents in the areas of binary modification and mobile A/B testing. He is currently a second-year Ph. D. student in the Computer-Aided Programming group at MIT, with research focusing on software language engineering, generic programming, and causal inference.