Automatic Code Documentation Generation Using GPT-3

Khan, Junaed Younus; Uddin, Gias

doi:10.1145/3551349.3559548

Cited by 96Software Engineering ResearchTopic ModelingNatural Language Processing Techniques

Automatic Code Documentation Generation Using GPT-3

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Khan, Junaed Younus, Uddin, Gias·2022·Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering

DOI 10.1145/3551349.3559548

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Abstract

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.

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From From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape (2025) · cites this paper as [232]

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Authors: Khan, J. Y., & Uddin, G.
Affiliations: University of Calgary (CA)
ORCID: Khan, Junaed Younus
Uddin, Gias
Venue: Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering
Publisher: ACM
Published: 2022-10-10
Pages: 1-6
DOI: 10.1145/3551349.3559548
OpenAlex: W4313563813
Cited by: 96
References: 39
Citations by year: 2026: 10 · 2025: 29 · 2024: 40 · 2023: 15 · 2022: 2
FWCI: 19.22
Type: conference paper
Language: en
Open access: green
arxiv.org
Licence: www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations: IEEE/ACM International Conference on Automated Software Engineering (ASE) (publishedVersion)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University) (cc-by)
Funders: Natural Sciences and Engineering Research Council of Canada
Alberta Innovates
University of Calgary
Topics: Physical Sciences › Computer Science › Information Systems › Software Engineering Research
Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
Keywords: GPT-3, codices, source code documentation, one-shot learning, programming language documentation, BLEU evaluation, large language models

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Automatic Code Documentation Generation Using GPT-3

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