mlsanityData to 14 Oct 2026

Towards Code Summarization of APIs Based on Unofficial Documentation Using NLP Techniques

arXiv 2208.06318 ↗First posted 12 Aug 2022cs.SE cs.AIcited by 4

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Abstract (arXiv)

Each programming language comes with official documentation to guide developers with APIs, methods, and classes. However, in some cases, official documentation is not an efficient way to get the needed information. As a result, developers may consult other sources (e.g., Stack Overflow, GitHub) to learn more about an API, its implementation, usage, and other information that official documentation may not provide. In this research, we propose an automatic approach to generate summaries for APIs and methods by leveraging unofficial documentation using NLP techniques. Our findings demonstrate that the generated summaries are competitive, and can be used as a complementary source for guiding developers in software development and maintenance tasks.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 2 of its 10 references that are in the map. First posted on arXiv 12 Aug 2022.

Builds on

2 of 10 references.

References · 2

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2021Leveraging Unsupervised Learning to Summarize APIs Discussed in Stack Overflowcited by 9

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Citing papers per year: 2023: 3; 2024: 0; 2025: 1.

4 papers.

  1. 2023Improving Code Example Recommendations on Informal Documentation Using BERT and Query-Aware LSH: A Comparative Studycited by 7
  2. 2023Enhancing API Documentation through BERTopic Modeling and Summarizationcited by 1
  3. 2025Automating API Documentation with LLMs: A BERTopic Approachcited by 1
  4. 2023Leveraging Deep Learning for Abstractive Code Summarization of Unofficial Documentationcited by 0