Inferring Javascript types using Graph Neural Networks
Machine Learning
2019-05-17 v1 Programming Languages
Software Engineering
Machine Learning
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 , which improves on previous similar work.
Cite
@article{arxiv.1905.06707,
title = {Inferring Javascript types using Graph Neural Networks},
author = {Jessica Schrouff and Kai Wohlfahrt and Bruno Marnette and Liam Atkinson},
journal= {arXiv preprint arXiv:1905.06707},
year = {2019}
}
Comments
Published at the Representation Learning on Graphs and Manifolds ICLR 2019 workshop (https://rlgm.github.io/papers/)