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

Most implemented papers

CodeBERT: A Pre-Trained Model for Programming and Natural Languages

microsoft/CodeBERT 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.

CodeTrans: Towards Cracking the Language of Silicon's Code Through Self-Supervised Deep Learning and High Performance Computing

agemagician/CodeTrans 6 Apr 2021

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 13 Jan 2022

Thereby, we propose a flexible and robust approach for automatic code summarization, based on neural models.