Poster
Generative Code Modeling with Graphs
Marc Brockschmidt · Miltiadis Allamanis · Alexander Gaunt · Oleksandr Polozov
Great Hall BC #20
Keywords: [ graph learning ] [ generative model ] [ source code ]
Generative models forsource 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. Our model generates code by interleaving 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.
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