GraphCodeBERT uses data flow graphs in pre-training to capture semantic code structure and reaches state-of-the-art results on code search, clone detection, translation, and refinement.
Generative Code Modeling with Graphs
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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.
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2020 1verdicts
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GraphCodeBERT: Pre-training Code Representations with Data Flow
GraphCodeBERT uses data flow graphs in pre-training to capture semantic code structure and reaches state-of-the-art results on code search, clone detection, translation, and refinement.