Generative Code Modeling with GraphsDownload PDF

27 Sept 2018, 22:36 (modified: 10 Feb 2022, 11:26)ICLR 2019 Conference Blind SubmissionReaders: Everyone
Keywords: Generative Model, Source Code, Graph Learning
TL;DR: Representing programs as graphs including semantics helps when generating programs
Abstract: 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.
Code: [![github](/images/github_icon.svg) Microsoft/graph-based-code-modelling](https://github.com/Microsoft/graph-based-code-modelling)
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