Augmenting API Documentation with Insights from Stack Overflow.

Saved in:

Bibliographic Details
Title:	Augmenting API Documentation with Insights from Stack Overflow.
Authors:	Treude, Christoph¹ christoph.treude@adelaide.edu.au, Robillard, Martin P.² martin@cs.mcgill.ca
Source:	ICSE: International Conference on Software Engineering. 5/14/2016, p392-403. 12p.
Subjects:	Application program interfaces, Computer software developers, Stack Overflow (Company), Computer software development, Software documentation
Abstract:	Software developers need access to different kinds of information which is often dispersed among different documentation sources, such as API documentation or Stack Over ow. 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 Over ow as well as part-of-speech tags can significantly improve unsupervised extraction approaches when applied to Stack Over ow data. [ABSTRACT FROM AUTHOR]
	Copyright of ICSE: International Conference on Software Engineering is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database:	Engineering Source

Description
Abstract:	Software developers need access to different kinds of information which is often dispersed among different documentation sources, such as API documentation or Stack Over ow. 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 Over ow as well as part-of-speech tags can significantly improve unsupervised extraction approaches when applied to Stack Over ow data. [ABSTRACT FROM AUTHOR]
DOI:	10.1145/2884781.2884800