ScholarWorks@경상국립대학교

초록

Human-written API documentation often becomes outdated, requiring developers to update it manually. Researchers have proposed identifying outdated API name references in documentation, yet have not addressed updating API documentation. Now, emerging large language models (LLMs) are capable of generating code examples and text descriptions. Then, a key question arises: Can LLMs assist in updating API documentation? In this paper, we propose an approach for leveraging an LLM to update API documentation with code change information. To evaluate this approach, we select five open-source projects that manage documentation revisions on GitHub and analyze the differences in documentation between two releases to derive ground truths. We then assess the accuracy of LLM-generated updates by comparing them to the ground truths. Our results show that LLM-generated updates achieve higher METEOR than outdated API documentation (0.771 vs 0.679). It indicates that the LLM updates are more similar to the human updates than the outdated documentation. Our results also reveal that LLMs update code-related information in API documentation with a maximum F1 score of $0.921$. © 2025 IEEE.

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