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.
- Publication:
-
arXiv e-prints
- Pub Date:
- May 2019
- DOI:
- 10.48550/arXiv.1905.06707
- arXiv:
- arXiv:1905.06707
- Bibcode:
- 2019arXiv190506707S
- Keywords:
-
- Computer Science - Machine Learning;
- Computer Science - Programming Languages;
- Computer Science - Software Engineering;
- Statistics - Machine Learning
- E-Print:
- Published at the Representation Learning on Graphs and Manifolds ICLR 2019 workshop (https://rlgm.github.io/papers/)