Code Documentation Generation
4 papers with code • 7 benchmarks • 5 datasets
Code Documentation Generation is a supervised task where a code function is the input to the model, and the model generates the documentation for this function.
Description from: CodeTrans: Towards Cracking the Language of Silicone's Code Through Self-Supervised Deep Learning and High Performance Computing
Benchmarks
These leaderboards are used to track progress in Code Documentation Generation
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CodeSearchNet
Most implemented papers
CodeBERT: A Pre-Trained Model for Programming and Natural Languages
microsoft/CodeBERT
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Findings of the Association for Computational Linguistics 2020
Results show that CodeBERT achieves state-of-the-art performance on both natural language code search and code documentation generation tasks.
HAConvGNN: Hierarchical Attention Based Convolutional Graph Neural Network for Code Documentation Generation in Jupyter Notebooks
dakuo/haconvgnn
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Findings (EMNLP) 2021
Jupyter notebook allows data scientists to write machine learning code together with its documentation in cells.
CodeTrans: Towards Cracking the Language of Silicon's Code Through Self-Supervised Deep Learning and High Performance Computing
Simultaneously, the transformer model, especially its combination with transfer learning, has been proven to be a powerful technique for natural language processing tasks.
Assemble Foundation Models for Automatic Code Summarization
jianguda/afm4acs
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Thereby, we propose a flexible and robust approach for automatic code summarization, based on neural models.
