Extracting API tips from developer question and answer websites

Document Type

Conference Proceeding

Publication Date

5-1-2019

Abstract

The success of question and answer (Q&A) websites attracts massive user-generated content for using and learning APIs, which easily leads to information overload: many questions for APIs have a large number of answers containing useful and irrelevant information, and cannot all be consumed by developers. In this work, we develop DeepTip, a novel deep learning-based approach using different Convolutional Neural Network architectures, to extract short practical and useful tips from developer answers. Our extensive empirical experiments prove that DeepTip can extract useful tips from a large corpus of answers to questions with high precision (i.e., avg. 0.854) and coverage (i.e., 0.94), and it outperforms two state-of-the-art baselines by up to 56.7% and 162%, respectively, in terms of Precision. Furthermore, qualitatively, a user study is conducted with real Stack Overflow users and its results confirm that tip extraction is useful and our approach generates high-quality tips.

Identifier

85072345840 (Scopus)

ISBN

[9781728134123]

Publication Title

IEEE International Working Conference on Mining Software Repositories

External Full Text Location

https://doi.org/10.1109/MSR.2019.00058

e-ISSN

21601860

ISSN

21601852

First Page

321

Last Page

332

Volume

2019-May

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