@inbook{07aabf7b26014407a97d9f18b342872a,
title = "Recommendflow: Use topic model to automatically recommend stack Overflow Q\&A in IDE",
abstract = "Developers often look information in the web during software development and maintenance. That means they spend time to formulate query, retrieve documents and process the results from many sources of information. Stack Overflow, one of the most popular question and answer sites and the most important information sources for developers, has become one of the most important information sources for developers. In this paper, we proposed a new approach that use LDA model and Q\&A meta-information to automatically generate query from code context and recommend the retrieval Q\&A to developers. We implemented the approach in Recommendflow, an Eclipse plugin. We considered one existing recommendation model as baseline and conducted an experiment to compare our approach with baseline. Our experiment on the test data set shows that LDA-based model outperforms existing Stack Overflow recommendation model.",
keywords = "Crowd knowledge, Q\&A, Recommend system, Software development, Topic model",
author = "Sun Fumin and Wang Xu and Sun Hailong and Liu Xudong",
note = "Publisher Copyright: {\textcopyright} 2017, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.",
year = "2017",
doi = "10.1007/978-3-319-59288-6\_50",
language = "英语",
series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",
publisher = "Springer Verlag",
pages = "521--526",
booktitle = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",
address = "德国",
}