Inferring Javascript types using Graph Neural Networks

Abstract

The recent use of `Big Code' with state-of-the-art deep learning methods offers promising avenues to ease program source code writing and correction. As a first step towards automatic code repair, we implemented a graph neural network model that predicts token types for Javascript programs. The predictions achieve an accuracy above $90\%$, which improves on previous similar work.

Published at the Representation Learning on Graphs and Manifolds ICLR 2019 workshop (https://rlgm.github.io/papers/)

References in corpus (2)

• Semi-Supervised Classification with Graph Convolutional Networks
• Graph Convolutional Matrix Completion