Deep Learning for Programming Language Type Inference

On Friday, November 16th, as part of source{d} paper reading club [1], we are going to talk about a paper that was recently published at FSE’18: Deep Learning Type Inference [2].

ABSTRACT
Dynamically typed languages such as JavaScript and Python are
increasingly popular, yet static typing has not been totally eclipsed:
Python now supports type annotations and languages like TypeScript
offer a middle-ground for JavaScript: a strict superset of
JavaScript, to which it transpiles, coupled with a type system that
permits partially typed programs. However, static typing has a cost:
adding annotations, reading the added syntax, and wrestling with
the type system to fix type errors. Type inference can ease the
transition to more statically typed code and unlock the benefits of
richer compile-time information, but is limited in languages like
JavaScript as it cannot soundly handle duck-typing or runtime evaluation
via eval. We propose DeepTyper, a deep learning model
that understands which types naturally occur in certain contexts
and relations and can provide type suggestions, which can often
be verified by the type checker, even if it could not infer the type
initially.

DETAILS
For this special edition of the reading club, we are very lucky to be joined by one of the paper authors, Earl Barr [3].

Join us either live in our Madrid office or on Zoom [4] for a lively discussion!

Bio: Earl Barr [3] is an industry veteran and a professor at UCL. His interests and contributions to the field of Machine Learning on Code include works on the naturalness hypothesis, on bimodal program analysis and on type systems.

Links:
[1] source{d} paper reading club: https://github.com/src-d/reading-club
[2] Deep Learning Type Inference: http://vhellendoorn.github.io/PDF/fse2018-j2t.pdf
[3] Earl T. Barr's homepage: http://earlbarr.com
[4] Room 974-346-848 on zoom