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/)

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Cited by in corpus (3)

• Graph Neural Networks: Taxonomy, Advances and Trends
• Adversarial Robustness for Code
• Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks