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arXiv:1905.06707cs.LG

Inferring Javascript types using Graph Neural Networks

Jessica Schrouff, Kai Wohlfahrt, Bruno Marnette, Liam Atkinson

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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.

arXiv comments
Published at the Representation Learning on Graphs and Manifolds ICLR 2019 workshop (https://rlgm.github.io/papers/)
Journal reference
DOI
Subjects
cs.LGMachine Learning cs.PLProgramming Languages cs.SESoftware Engineering stat.MLMachine Learning (Statistics)

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