There is a great appeal in using machine learning to assist in software development, as it promises to enable the experience of one software engineer to be recorded and then generalized to provide guidance to another. This creates a number of challenges and opportunities for machine learning research. In this talk, I’ll give an overview of some of our recent work on deep learning in this space, highlighting the importance of the temporal nature of developers editing software, imbuing models with understanding of program execution behaviors, and the challenge of finding the right information to provide as inputs to our models. I’ll cast the challenges in terms of existing and future developer tools.

Danny is a Staff Research Scientist at Google Brain, Adjunct Professor in Computer Science at McGill University, and a Core Industrial Member of the Mila Quebec AI Institute. His research interests are in the application of machine learning to problems involving structured data, with a specific interest in the intersection of machine learning, programming languages, and software engineering. His work has won paper awards at NeurIPS, UAI, and ICML & NeurIPS Workshops. He holds a Ph.D. from the Machine Learning group at the University of Toronto and was previously a Research Fellow at Darwin College, University of Cambridge and a Researcher at Microsoft Research Cambridge (UK).