@inproceedings{brockschmidt2019generative,
author = {Brockschmidt, Marc and Allamanis, Miltos and Gaunt, Alex  and Polozov, Alex},
title = {Generative Code Modeling with Graphs},
booktitle = {7th International Conference on Learning Representations},
year = {2019},
month = {May},
abstract = {Generative models for source code are an interesting structured prediction problem, requiring to reason about both hard syntactic and semantic constraints as well as about natural, likely programs. We present a novel model for this problem that uses a graph to represent the intermediate state of the generated output. The generative procedure interleaves grammar-driven expansion steps with graph augmentation and neural message passing steps. An experimental evaluation shows that our new model can generate semantically meaningful expressions, outperforming a range of strong baselines.

Get the code on GitHub >},
url = {http://approjects.co.za/?big=en-us/research/publication/generative-code-modeling-with-graphs/},
}