research-article

Scalable Relevant Project Recommendation on GitHub

Authors:

Xiang ChenAuthors Info & Claims

Internetware '17: Proceedings of the 9th Asia-Pacific Symposium on Internetware

Article No.: 9, Pages 1 - 10

https://doi.org/10.1145/3131704.3131706

Published: 23 September 2017 Publication History

Abstract

GitHub, one of the largest social coding platforms, fosters a flexible and collaborative development process. In practice, developers in the open source software platform need to find projects relevant to their development work to reuse their function, explore ideas of possible features, or analyze the requirements for their projects. Recommending relevant projects to a developer is a difficult problem considering that there are millions of projects hosted on GitHub, and different developers may have different requirements on relevant projects. In this paper, we propose a scalable and personalized approach to recommend projects by leveraging both developers' behaviors and project features. Based on the features of projects created by developers and their behaviors to other projects, our approach automatically recommends top N most relevant software projects to developers. Moreover, to improve the scalability of our approach, we implement our approach in a parallel processing frame (i.e., Apache Spark) to analyze large-scale data on GitHub for efficient recommendation. We perform an empirical study on the data crawled from GitHub, and the results show that our approach can efficiently recommend relevant software projects with a relatively high precision fit for developers' interests.

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Cited By

Zhou YSun YWu JZhang JHuang G(2024)Wiki2GH: A Recommendation Service to Link Software Engineering Knowledge to Practical DevelopmentService Science10.1007/978-981-97-5760-2_14(203-220)Online publication date: 19-Aug-2024
https://doi.org/10.1007/978-981-97-5760-2_14
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
https://dl.acm.org/doi/10.1145/3558489.3559069
Wu JSun YZhang J(2022)An Open-source Repository Retrieval Service Using Functional Semantics for Software Developers2022 International Conference on Service Science (ICSS)10.1109/ICSS55994.2022.00012(12-20)Online publication date: May-2022
https://doi.org/10.1109/ICSS55994.2022.00012
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cover image ACM Other conferences

Internetware '17: Proceedings of the 9th Asia-Pacific Symposium on Internetware

September 2017

172 pages

ISBN:9781450353137

DOI:10.1145/3131704

Conference Chairs:
Hong Mei,
Jian Lyu,
Zhi Jin,
Wenyun Zhao

Copyright © 2017 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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Fudan University
SIGSOFT: ACM Special Interest Group on Software Engineering
CCF: China Computer Federation

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 September 2017

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Internetware'17

Internetware'17: The Eighth Asia-Pacific Symposium on Internetware

September 23, 2017

Shanghai, China

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Overall Acceptance Rate 55 of 111 submissions, 50%

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Cited By

Zhou YSun YWu JZhang JHuang G(2024)Wiki2GH: A Recommendation Service to Link Software Engineering Knowledge to Practical DevelopmentService Science10.1007/978-981-97-5760-2_14(203-220)Online publication date: 19-Aug-2024
https://doi.org/10.1007/978-981-97-5760-2_14
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
https://dl.acm.org/doi/10.1145/3558489.3559069
Wu JSun YZhang J(2022)An Open-source Repository Retrieval Service Using Functional Semantics for Software Developers2022 International Conference on Service Science (ICSS)10.1109/ICSS55994.2022.00012(12-20)Online publication date: May-2022
https://doi.org/10.1109/ICSS55994.2022.00012
Yang HSun SWen JCai HMateen M(2022)Improving Personalized Project Recommendation on GitHub Based on Deep Matrix FactorizationCollaborative Computing: Networking, Applications and Worksharing10.1007/978-3-030-92635-9_19(318-332)Online publication date: 1-Jan-2022
https://doi.org/10.1007/978-3-030-92635-9_19
Kim JWi JKim Y(2021)Sequential Recommendations on GitHub RepositoryApplied Sciences10.3390/app1104158511:4(1585)Online publication date: 10-Feb-2021
https://doi.org/10.3390/app11041585
Zhang PXiong FLeung HSong W(2021)FunkR-pDAE: Personalized Project Recommendation Using Deep LearningIEEE Transactions on Emerging Topics in Computing10.1109/TETC.2018.28707349:2(886-900)Online publication date: 1-Apr-2021
https://doi.org/10.1109/TETC.2018.2870734
Zhou YWu JSun Y(2021)GHTRec: A Personalized Service to Recommend GitHub Trending Repositories for Developers2021 IEEE International Conference on Web Services (ICWS)10.1109/ICWS53863.2021.00049(314-323)Online publication date: Sep-2021
https://doi.org/10.1109/ICWS53863.2021.00049
Zheng ZWang LXu JWu TWu STao X(2018)Measuring and Predicting the Relevance Ratings between FLOSS Projects using Topic FeaturesProceedings of the 10th Asia-Pacific Symposium on Internetware10.1145/3275219.3275222(1-10)Online publication date: 16-Sep-2018
https://dl.acm.org/doi/10.1145/3275219.3275222

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