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.
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
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.