research-article

GEMiner: Mining Social and Programming Behaviors to Identify Experts in Github

Authors:

Hao ZhongAuthors Info & Claims

Internetware '15: Proceedings of the 7th Asia-Pacific Symposium on Internetware

Pages 93 - 101

https://doi.org/10.1145/2875913.2875924

Published: 06 November 2015 Publication History

Abstract

Hosting over 10 million repositories, GitHub becomes the largest open source community in the world. Besides sharing code, Github is also a social network, in which developers can follow others or keep track of their interested projects. Considering the multi-roles of Github, integrating heterogenous data of each developer to identify experts is a challenging task. In this paper, we propose GEMiner, a novel approach to identify experts for some specific programming languages in Github. Different from previous approaches, GEMiner analyzes the social behaviors and programming behaviors of a developer to determine the expertise of the developer. When modeling social behaviors of developers, to integrate heterogenous social networks in Github, GEMiner implements a Multi-Sources PageRank algorithm. Also, GEMiner analyzes the behaviors of developers when they are programming (e.g., their commit activities and their preferred programming languages) to model programming behaviors of them. Based on our expertise models and our extracted programming languages data, GEMiner can then identify experts for some specific programming languages in Github. We conducted experiments on a real data set, and our results show that GEMiner identifies experts with 60% accuracy higher than the state-of-the-art algorithms.

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Cited By

Braga CSantos Jr PBarcellos M(2023)Help! I need somebody. A Mapping Study about Expert Identification in Software DevelopmentProceedings of the XXXVII Brazilian Symposium on Software Engineering10.1145/3613372.3613389(154-163)Online publication date: 25-Sep-2023
https://dl.acm.org/doi/10.1145/3613372.3613389
Bayati STripathi A(2021)Antecedents of Different Social Network Structures on Open Source Projects PopularitySmart Business: Technology and Data Enabled Innovative Business Models and Practices10.1007/978-3-030-67781-7_14(143-157)Online publication date: 31-Jan-2021
https://doi.org/10.1007/978-3-030-67781-7_14
Schettino VHorta VAraujo MStroele V(2019)Towards Community and Expert Detection in Open Source Global Development2019 IEEE 23rd International Conference on Computer Supported Cooperative Work in Design (CSCWD)10.1109/CSCWD.2019.8791872(350-355)Online publication date: May-2019
https://doi.org/10.1109/CSCWD.2019.8791872
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cover image ACM Other conferences

Internetware '15: Proceedings of the 7th Asia-Pacific Symposium on Internetware

November 2015

247 pages

ISBN:9781450336413

DOI:10.1145/2875913

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Key Laboratory of High Confidence Software Technologies: Key Laboratory of High Confidence Software Technologies, Ministry of Education

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 November 2015

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Internetware '15

Internetware '15: The Seventh Asia-Pacific Symposium on Internetware

November 6, 2015

Wuhan, China

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Cited By

Braga CSantos Jr PBarcellos M(2023)Help! I need somebody. A Mapping Study about Expert Identification in Software DevelopmentProceedings of the XXXVII Brazilian Symposium on Software Engineering10.1145/3613372.3613389(154-163)Online publication date: 25-Sep-2023
https://dl.acm.org/doi/10.1145/3613372.3613389
Bayati STripathi A(2021)Antecedents of Different Social Network Structures on Open Source Projects PopularitySmart Business: Technology and Data Enabled Innovative Business Models and Practices10.1007/978-3-030-67781-7_14(143-157)Online publication date: 31-Jan-2021
https://doi.org/10.1007/978-3-030-67781-7_14
Schettino VHorta VAraujo MStroele V(2019)Towards Community and Expert Detection in Open Source Global Development2019 IEEE 23rd International Conference on Computer Supported Cooperative Work in Design (CSCWD)10.1109/CSCWD.2019.8791872(350-355)Online publication date: May-2019
https://doi.org/10.1109/CSCWD.2019.8791872
Cassee NPinto GCastor FSerebrenik AZaidman AKamei YHill E(2018)How swift developers handle errorsProceedings of the 15th International Conference on Mining Software Repositories10.1145/3196398.3196428(292-302)Online publication date: 28-May-2018
https://dl.acm.org/doi/10.1145/3196398.3196428
Jamil H(2018)A Free-Choice Social Learning Network for Computational Thinking2018 IEEE 18th International Conference on Advanced Learning Technologies (ICALT)10.1109/ICALT.2018.00023(69-71)Online publication date: Jul-2018
https://doi.org/10.1109/ICALT.2018.00023

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