Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
9-2025
Abstract
API documentation is often the most trusted resource for programming. Many approaches have been proposed to augment API documentation by summarizing complementary information from external resources such as Stack Overflow. Existing extractive-based summarization approaches excel in producing faithful summaries that accurately represent the source content without input length restrictions. Nevertheless, they suffer from inherent readability limitations. On the other hand, our empirical study on the abstractive-based summarization method, i.e., GPT-4, reveals that GPT-4 can generate coherent and concise summaries but presents limitations in terms of informativeness and faithfulness. We introduce APIDocBooster, an extract-then-abstract framework that seamlessly fuses the advantages of both extractive (i.e., enabling faithful summaries without length limitation) and abstractive summarization (i.e., producing coherent and concise summaries). APIDocBooster consists of two stages: (1) Context-aware Sentence Section Classification (CSSC) and (2) UPdate SUMmarization (UPSUM). CSSC classifies API-relevant information collected from multiple sources into API documentation sections. UPSUM first generates extractive summaries distinct from original API documentation and then generates abstractive summaries guided by extractive summaries through in-context learning. To enable automatic evaluation of APIDocBooster, we construct the first dataset for API documentation augmentation. Our automatic evaluation results reveal that each stage in APIDocBooster outperforms its baselines by a large margin. Our human evaluation also demonstrates the superiority of APIDocBooster over GPT-4 and shows that it improves the informativeness, relevance and faithfulness by 13.89%, 15.15%, and 30.56%, respectively.
Keywords
Summarization, Question Retrieval
Discipline
Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
2025 IEEE International Conference on Software Maintenance and Evolution, ICSME: Auckland, September 7-12: Proceedings
First Page
36
Last Page
47
ISBN
9798331595876
Identifier
10.1109/ICSME64153.2025.00014
Publisher
IEE Computer Society
City or Country
Los Alamos
Citation
YANG, Chengran; LIU, Jiakun; XU, Bowen; TREUDE, Christoph; LYU, Yunbo; HE, Junda; LI, Ming; and David LO.
APIDocBooster: An extract-then-abstract framework leveraging large language models for augmenting API documentation. (2025). 2025 IEEE International Conference on Software Maintenance and Evolution, ICSME: Auckland, September 7-12: Proceedings. 36-47.
Available at: https://ink.library.smu.edu.sg/sis_research/10503
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/ICSME64153.2025.00014