# gDoc: Automatic Generation of Structured API Documentation

arXiv: https://arxiv.org/abs/2303.13041 · HTML: https://mlsanity.com/paper/2303.13041/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2023.

## Built on by (most cited first, 2 of 2)

- [RepoAgent: An LLM-Powered Open-Source Framework for Repository-level Code Documentation Generation](https://mlsanity.com/paper/2402.16667/) (2024)
- [Scalable LLM Agent Tool Access in the Cloud](https://mlsanity.com/paper/2607.15593/) (2026)

## Abstract (arXiv)

Generating and maintaining API documentation with integrity and consistency can be time-consuming and expensive for evolving APIs. To solve this problem, several approaches have been proposed to automatically generate high-quality API documentation based on a combination of knowledge from different web sources. However, current researches are weak in handling unpopular APIs and cannot generate structured API documentation. Hence, in this poster, we propose a hybrid technique(namely textitgDoc) for the automatic generation of structured API documentation. We first present a fine-grained search-based strategy to generate the description for partial API parameters via computing the relevance between various APIs, ensuring the consistency of API documentation. Then, we employ the cross-modal pretraining Seq2Seq model M6 to generate a structured API document for each API, which treats the document generation problem as a translation problem. Finally, we propose a heuristic algorithm to extract practical parameter examples from API request logs. The experiments evaluated on the online system show that this work's approach significantly improves the effectiveness and efficiency of API document generation.