Detecting Variable Misuses with Gated Graph Neural Networks


Deep Neural Networks are succeeding at a range of natural language tasks such as machine translation and text summarization. While tasks on source code (ie, formal languages) have been considered recently, most work in this area does not attempt to capitalize on the unique opportunities offered by its known syntax and structure. In this talk, I discuss how graph neural networks that use code's syntactic and semantic structured information can detect variable misuses in code without any external information (e.g. unit tests).


Miltos Allamanis ( is a postdoc at Microsoft Research, Cambridge. He is interested in applications of machine learning and natural language processing to software engineering and programming languages to create smart software engineering tools for developers. Previously, he was a PhD student at the University of Edinburgh advised by Dr. Charles Sutton.

This page was last modified on 26 Sep 2017.