research-article

Profiling Developer Expertise across Software Communities with Heterogeneous Information Network Analysis

Authors:

Xiaotao SongAuthors Info & Claims

Internetware '18: Proceedings of the 10th Asia-Pacific Symposium on Internetware

Article No.: 2, Pages 1 - 9

https://doi.org/10.1145/3275219.3275226

Published: 16 September 2018 Publication History

Abstract

Knowing developer expertise is critical for achieving effective task allocation. However, it is of great challenge to accurately profile the expertise of developers over the Internet as their activities often disperse across different online communities. In this regard, the existing works either merely concern a single community, or simply sum up the expertise in individual communities. The former suffers from low accuracy due to incomplete data, while the latter impractically assumes that developer expertise is completely independent and irrelavant across communities. To overcome those limitations, we propose a new approach to profile developer expertise across software communities through heterogeneous information network (HIN) analysis. A HIN is first built by analyzing the developer activities in various communities, where nodes represent objects like developers and skills, and edges represent the relations among objects. Second, as random walk with restart (RWR) is known for its ability to capture the global structure of the whole network, we adopt RWR over the HIN to estimate the proximity of developer nodes and skill nodes, which essentially reflects developer expertise. Based on the data of 72,645 common users of GitHub and Stack Overflow, we conducted an empirical study and evaluated developer expertise using proposed approach. To evaluate the effect of our approach, we use the obtained expertise to estimate the competency of developers in answering the questions posted in Stack Overflow. The experimental results demonstrate the superiority of our approach over existing methods.

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Index Terms

Profiling Developer Expertise across Software Communities with Heterogeneous Information Network Analysis

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Internetware '18: Proceedings of the 10th Asia-Pacific Symposium on Internetware

September 2018

167 pages

ISBN:9781450365901

DOI:10.1145/3275219

Copyright © 2018 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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Institute of Software, Chinese Academy of Sciences
CCF: China Computer Federation

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Published: 16 September 2018

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Internetware '18: The Tenth Asia-Pacific Symposium on Internetware

September 16, 2018

Beijing, China

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https://doi.org/10.1007/s00521-024-09749-0
Nishiura KIkeda KSasakura MMonden A(2023)Exploring Behavioral Trustworthiness of Github DevelopersProceedings of the 2023 5th World Symposium on Software Engineering10.1145/3631991.3632005(92-95)Online publication date: 22-Sep-2023
https://dl.acm.org/doi/10.1145/3631991.3632005
Saaki MHosseini SRahmani SKangavari MHua WZhou X(2023)Value-Wise ConvNet for Transformer Models: An Infinite Time-Aware Recommender SystemIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.321923135:10(9932-9945)Online publication date: 1-Oct-2023
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Coskun THalepmollasi RHanifi KFouladi RDe Cnudde PTosun AMcIntosh SShang WPerez G(2022)Profiling developers to predict vulnerable code changesProceedings of the 18th International Conference on Predictive Models and Data Analytics in Software Engineering10.1145/3558489.3559069(32-41)Online publication date: 7-Nov-2022
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Song XYan JHuang YSun HZhang H(2022)A Collaboration-Aware Approach to Profiling Developer Expertise with Cross-Community Data2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS)10.1109/QRS57517.2022.00043(344-355)Online publication date: Dec-2022
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Vadlamani SBaysal O(2020)Studying Software Developer Expertise and Contributions in Stack Overflow and GitHub2020 IEEE International Conference on Software Maintenance and Evolution (ICSME)10.1109/ICSME46990.2020.00038(312-323)Online publication date: Sep-2020
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