Code Documentation Generation
6 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.
Memorization and Generalization in Neural Code Intelligence Models
The goal of this paper is to evaluate and compare the extent of memorization and generalization in neural code intelligence models.
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.
RepoAgent: An LLM-Powered Open-Source Framework for Repository-level Code Documentation Generation
Generative models have demonstrated considerable potential in software engineering, particularly in tasks such as code generation and debugging.
