Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

5-2016

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, Insight sentences, Stack Overow

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

Proceedings of the 38th IEEE International Conference on Software Engineering (ICSE), Austin, TX, USA, 2016 May 14-22

First Page

392

Last Page

403

ISBN

9781450342056

Identifier

10.1145/2884781.2884800

Publisher

IEEE Computer Society

City or Country

Piscataway, NJ

Additional URL

https://doi.org/10.1145/2884781.2884800

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