Learning from code with graphs (keynote)

Learning from large corpora of source code ("Big Code") has seen increasing interest over the past few years. A first wave of work has focused on leveraging off-the-shelf methods from other machine learning fields such as natural language processing. While these techniques have succeeded in showing the feasibility of learning from code, and led to some initial practical solutions, they forego explicit use of known program semantics. In a range of recent work, we have tried to solve this issue by integrating deep learning techniques with program analysis methods in graphs. Graphs are a convenient, general formalism to model entities and their relationships, and are seeing increasing interest from machine learning researchers as well. In this talk, I present two applications of graph-based learning to understanding and generating programs and discuss a range of future work building on the success of this work.