HAConvGNN: Hierarchical Attention Based Convolutional Graph Neural Network for Code Documentation Generation in Jupyter Notebooks

Xuye Liu, Dakuo Wang, April Wang, Lingfei Wu

Many data scientists use Jupyter notebook to experiment code, visualize results, and document rationales or interpretations. The code documentation generation CDG task in notebooks is related but different from the code summarization task in software engineering, as one documentation (markdown cell) may consist of a text (informative summary or indicative rationale) for multiple code cells. Our work aims to solve the CDG task by encoding the multiple code cells as separated AST graph structures, for which we propose a hierarchical attention-based ConvGNN component to augment the Seq2Seq network. We build a dataset with publicly available Kaggle notebooks and evaluate our model (HAConvGNN) against baseline models (e.g., Code2Seq or Graph2Seq).

Knowledge Graph



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