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Type: Conference paper
Title: Augmenting API documentation with insights from Stack Overflow
Author: Treude, C.
Robillard, M.P.
Citation: Proceedings of the International Conference on Software Engineering, 2016 / vol.14-22-May-2016, pp.1-12
Publisher: ACM
Publisher Place: online
Issue Date: 2016
Series/Report no.: International Conference on Software Engineering
ISBN: 9781450339001
ISSN: 0270-5257
Conference Name: International Conference on Software Engineering (14 May 2016 - 22 May 2016 : Austin, USA)
Statement of
Christoph Treude, Martin P. Robillard
Abstract: Software developers need access to different kinds of information which is often dispersed among different documentation sources, such as API documentation or Stack Overflow. We present an approach to automatically augment API documentation with "insight sentences" from Stack Overflow- sentences that are related to a particular API type and that provide insight not contained in the API documentation of that type. Based on a development set of 1,574 sentences, we compare the performance of two state-of-the-art summarization techniques as well as a pattern-based approach for insight sentence extraction. We then present SISE, a novel machine learning based approach that uses as features the sentences themselves, their formatting, their question, their answer, and their authors as well as part-of-speech tags and the similarity of a sentence to the corresponding API documentation. With SISE, we were able to achieve a precision of 0.64 and a coverage of 0.7 on the development set. In a comparative study with eight software developers, we found that SISE resulted in the highest number of sentences that were considered to add useful information not found in the API documentation. These results indicate that taking into account the meta data available on Stack Overflow as well as part-of-speech tags can significantly improve unsupervised extraction approaches when applied to Stack Overflow data.
Keywords: API documentation; Stack Overflow; insight sentences
Rights: © 2016 ACM.
RMID: 0030045660
DOI: 10.1145/2884781.2884800
Appears in Collections:Computer Science publications

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