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2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE) (2017)
Urbana, IL, USA
Oct. 30, 2017 to Nov. 3, 2017
ISBN: 978-1-5386-3976-4
pp: 153-158
Yuan Tian , School of Information Systems, Singapore Management University, Singapore
Ferdian Thung , School of Information Systems, Singapore Management University, Singapore
Abhishek Sharma , School of Information Systems, Singapore Management University, Singapore
David Lo , School of Information Systems, Singapore Management University, Singapore
ABSTRACT
As the carrier of Application Programming Interfaces (APIs) knowledge, API documentation plays a crucial role in how developers learn and use an API. It is also a valuable information resource for answering API-related questions, especially when developers cannot find reliable answers to their questions online/offline. However, finding answers to API-related questions from API documentation might not be easy because one may have to manually go through multiple pages before reaching the relevant page, and then read and understand the information inside the relevant page to figure out the answers. To deal with this challenge, we develop APIBot, a bot that can answer API questions given API documentation as an input. APIBot is built on top of SiriusQA, the QA system from Sirius, a state of the art intelligent personal assistant. To make SiriusQA work well under software engineering scenario, we make several modifications over SiriusQA by injecting domain specific knowledge. We evaluate APIBot on 92 API questions, answers of which are known to be present in Java 8 documentation. Our experiment shows that APIBot can achieve a Hit@5 score of 0.706.
INDEX TERMS
Documentation, Knowledge discovery, Natural languages, Probabilistic logic, Training, Software engineering, Software
CITATION

Y. Tian, F. Thung, A. Sharma and D. Lo, "APIBot: Question answering bot for API documentation," 2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE), Urbana, IL, USA, 2017, pp. 153-158.
doi:10.1109/ASE.2017.8115628
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