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Published in: World Wide Web 6/2018

05-02-2018

SCSMiner: mining social coding sites for software developer recommendation with relevance propagation

Authors: Yao Wan, Liang Chen, Guandong Xu, Zhou Zhao, Jie Tang, Jian Wu

Published in: World Wide Web | Issue 6/2018

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Abstract

With the advent of social coding sites, software development has entered a new era of collaborative work. Social coding sites (e.g., GitHub) can integrate social networking and distributed version control in a unified platform to facilitate collaborative developments over the world. One unique characteristic of such sites is that the past development experiences of developers provided on the sites convey the implicit metrics of developer’s programming capability and expertise, which can be applied in many areas, such as software developer recruitment for IT corporations. Motivated by this intuition, we aim to develop a framework to effectively locate the developers with right coding skills. To achieve this goal, we devise a generativ e probabilistic expert ranking model upon which a consistency among projects is incorporated as graph regularization to enhance the expert ranking and a perspective of relevance propagation illustration is introduced. For evaluation, StackOverflow is leveraged to complement the ground truth of expert. Finally, a prototype system, SCSMiner, which provides expert search service based on a real-world dataset crawled from GitHub is implemented and demonstrated.
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Footnotes
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Metadata
Title
SCSMiner: mining social coding sites for software developer recommendation with relevance propagation
Authors
Yao Wan
Liang Chen
Guandong Xu
Zhou Zhao
Jie Tang
Jian Wu
Publication date
05-02-2018
Publisher
Springer US
Published in
World Wide Web / Issue 6/2018
Print ISSN: 1386-145X
Electronic ISSN: 1573-1413
DOI
https://doi.org/10.1007/s11280-018-0526-9

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