# Automatic Code Documentation Generation Using GPT-3

arXiv: https://arxiv.org/abs/2209.02235 · HTML: https://mlsanity.com/paper/2209.02235/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2022.

## Built on by (most cited first, 30 of 37)

- [A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4](https://mlsanity.com/paper/2310.12321/) (2023)
- [RepoAgent: An LLM-Powered Open-Source Framework for Repository-level Code Documentation Generation](https://mlsanity.com/paper/2402.16667/) (2024)
- [A Comparative Analysis of Large Language Models for Code Documentation Generation](https://mlsanity.com/paper/2312.10349/) (2023)
- [Learning Agent-based Modeling with LLM Companions: Experiences of Novices and Experts Using ChatGPT & NetLogo Chat](https://mlsanity.com/paper/2401.17163/) (2024)
- [The Landscape and Challenges of HPC Research and LLMs](https://mlsanity.com/paper/2402.02018/) (2024)
- [Performance-Aligned LLMs for Generating Fast Code](https://mlsanity.com/paper/2404.18864/) (2024)
- [From Google Gemini to OpenAI Q* (Q-Star): A Survey of Reshaping the Generative Artificial Intelligence (AI) Research Landscape](https://mlsanity.com/paper/2312.10868/) (2023)
- [From Commit Message Generation to History-Aware Commit Message Completion](https://mlsanity.com/paper/2308.07655/) (2023)
- [Autonomous Agents in Software Development: A Vision Paper](https://mlsanity.com/paper/2311.18440/) (2023)
- [ChatGPT for PLC/DCS Control Logic Generation](https://mlsanity.com/paper/2305.15809/) (2023)
- [Multilingual Code Co-Evolution Using Large Language Models](https://mlsanity.com/paper/2307.14991/) (2023)
- [VulnLLMEval: A Framework for Evaluating Large Language Models in Software Vulnerability Detection and Patching](https://mlsanity.com/paper/2409.10756/) (2024)
- [Prompting and Fine-tuning Large Language Models for Automated Code Review Comment Generation](https://mlsanity.com/paper/2411.10129/) (2024)
- [RepoRepair: Leveraging Code Documentation for Repository-Level Automated Program Repair](https://mlsanity.com/paper/2603.01048/) (2026)
- [Semantic Similarity Loss for Neural Source Code Summarization](https://mlsanity.com/paper/2308.07429/) (2023)
- [Co-audit: tools to help humans double-check AI-generated content](https://mlsanity.com/paper/2310.01297/) (2023)
- [ChatGPT Incorrectness Detection in Software Reviews](https://mlsanity.com/paper/2403.16347/) (2024)
- [Generative AI for Self-Adaptive Systems: State of the Art and Research Roadmap](https://mlsanity.com/paper/2512.04680/) (2025)
- [Evaluating the Effectiveness of LLMs in Fixing Maintainability Issues in Real-World Projects](https://mlsanity.com/paper/2502.02368/) (2025)
- [CodeCloak: A Method for Evaluating and Mitigating Code Leakage by LLM Code Assistants](https://mlsanity.com/paper/2404.09066/) (2024)
- [Mastering the Craft of Data Synthesis for CodeLLMs](https://mlsanity.com/paper/2411.00005/) (2024)
- [MESIA: Understanding and Leveraging Supplementary Nature of Method-level Comments for Automatic Comment Generation](https://mlsanity.com/paper/2403.17357/) (2024)
- [Evaluating the Use of LLMs for Documentation to Code Traceability](https://mlsanity.com/paper/2506.16440/) (2025)
- [Evaluating Repository-level Software Documentation via Question Answering and Feature-Driven Development](https://mlsanity.com/paper/2604.06793/) (2026)
- [Assessing the Code Clone Detection Capability of Large Language Models](https://mlsanity.com/paper/2407.02402/) (2024)
- [Impact of Comments on LLM Comprehension of Legacy Code](https://mlsanity.com/paper/2506.11007/) (2025)
- [SODBench: A Large Language Model Approach to Documenting Spreadsheet Operations](https://mlsanity.com/paper/2510.19864/) (2025)
- [Q-Bridge: Code Translation for Quantum Machine Learning via LLMs](https://mlsanity.com/paper/2603.27836/) (2026)
- [Remember Your Trace: Memory-Guided Long-Horizon Agentic Framework for Consistent and Hierarchical Repository-Level Code Documentation](https://mlsanity.com/paper/2605.14563/) (2026)
- [Empowering Business Transformation: The Positive Impact and Ethical Considerations of Generative AI in Software Product Management -- A Systematic Literature Review](https://mlsanity.com/paper/2306.04605/) (2023)

## Abstract (arXiv)

Source code documentation is an important artifact for efficient software development. Code documentation could greatly benefit from automation since manual documentation is often labouring, resource and time-intensive. In this paper, we employed Codex for automatic code documentation creation. Codex is a GPT-3 based model pre-trained on both natural and programming languages. We find that Codex outperforms existing techniques even with basic settings like one-shot learning (i.e., providing only one example for training). Codex achieves an overall BLEU score of 20.6 for six different programming languages (11.2% improvement over earlier state-of-the-art techniques). Thus, Codex shows promise and warrants in-depth future studies for automatic code documentation generation to support diverse development tasks.