poster

A recommender system for developer onboarding

Authors:

Baishakhi RayAuthors Info & Claims

ICSE '18: Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings

Pages 319 - 320

https://doi.org/10.1145/3183440.3194989

Published: 27 May 2018 Publication History

Get Access

Abstract

Successfully onboarding open source projects in GitHub is difficult for developers, because it is time-consuming for them to search an expected project by a few query words from numerous repositories, and developers suffer from various social and technical barriers in joined projects. Frequently failed onboarding postpones developers' development schedule, and the evolutionary progress of open source projects. To mitigate developers' costly efforts for onboarding, we propose a ranking model NNLRank (Neural Network for List-wise Ranking) to recommend projects that developers are likely to contribute many commits. Based on 9 measured project features, NNLRank learns a ranking function (represented by a neural network, optimized by a list-wise ranking loss function) to score a list of candidate projects, where top-n scored candidates are recommended to a target developer. We evaluate NNLRank by 2044 succeeded onboarding decisions from GitHub developers, comparing with a related model LP (Link Prediction), and 3 other typical ranking models. Results show that NNLRank can provide developers with effective recommendation, substantially outperforming baselines.

References

[1]

Casey Casalnuovo, Bogdan Vasilescu, Premkumar Devanbu, and Vladimir Filkov. 2015. Developer onboarding in Github: the role of prior social links and language experience. In Proceedings of the 2015 10th Joint Meeting on Foundations of Software Engineering. ACM, 817--828.

Digital Library

Google Scholar

[2]

Georgios Gousios and Diomidis Spinellis. 2012. GHTorrent: GitHub's data from a firehose. In Proceedings of the 9th IEEE Working Conference on Mining Software Repositories. IEEE Press, 12--21.

Digital Library

Google Scholar

[3]

Jungpil Hahn, Jae Yun Moon, and Chen Zhang. 2008. Emergence of new project teams from open source software developer networks: Impact of prior collaboration ties. Information Systems Research 19, 3 (2008), 369--391.

Crossref

Google Scholar

[4]

Robert Hecht-Nielsen et al. 1988. Theory of the backpropagation neural network. Neural Networks 1, Supplement-1 (1988), 445--448.

Crossref

Google Scholar

[5]

Thorsten Joachims. 2006. Training linear SVMs in linear time. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 217--226.

Digital Library

Google Scholar

[6]

Tadej Matek and Svit Timej Zebec. 2016. GitHub open source project recommendation system. arXiv preprint arXiv.1602.02594 (2016).

Google Scholar

[7]

Igor Steinmacher, Marco Aurelio Graciotto Silva, Marco Aurelio Gerosa, and David F Redmiles. 2015. A systematic literature review on the barriers faced by newcomers to open source software projects. Information and Software Technology 59 (2015), 67--85.

Digital Library

Google Scholar

[8]

Fen Xia, Tie-Yan Liu, Jue Wang, Wensheng Zhang, and Hang Li. 2008. Listwise approach to learning to rank: theory and algorithm. In Proceedings of the 25th international conference on Machine learning. ACM, 1192--1199.

Digital Library

Google Scholar

Cited By

View all

Linåker JPapatheocharous EOlsson T(2022)How to characterize the health of an Open Source Software project? A snowball literature review of an emerging practiceProceedings of the 18th International Symposium on Open Collaboration10.1145/3555051.3555067(1-12)Online publication date: 7-Sep-2022
https://dl.acm.org/doi/10.1145/3555051.3555067
Liu CGao CXia XLo DGrundy JYang X(2021)On the Reproducibility and Replicability of Deep Learning in Software EngineeringACM Transactions on Software Engineering and Methodology10.1145/347753531:1(1-46)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3477535
Liu CXia XLo DLiu ZHassan ALi S(2021)CodeMatcher: Searching Code Based on Sequential Semantics of Important Query WordsACM Transactions on Software Engineering and Methodology10.1145/346540331:1(1-37)Online publication date: 28-Sep-2021
https://dl.acm.org/doi/10.1145/3465403
Show More Cited By

Index Terms

A recommender system for developer onboarding
1. Software and its engineering
  1. Software creation and management
    1. Collaboration in software development
      1. Open source model

Recommendations

Collaborative factorization for recommender systems
SIGIR '13: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval

Recommender system has become an effective tool for information filtering, which usually provides the most useful items to users by a top-k ranking list. Traditional recommendation techniques such as Nearest Neighbors (NN) and Matrix Factorization (MF) ...
Improving Accuracy of Recommender System by Item Clustering

Recommender System (RS) predicts user's ratings towards items, and then recommends highly-predicted items to user. In recent years, RS has been playing more and more important role in the agent research field. There have been a great deal of researches ...
A New Approach for Recommender System
ICACS '17: Proceedings of the 1st International Conference on Algorithms, Computing and Systems

In today's e-commerce environment, Collaborative Filtering (CF) is a widely used algorithm for recommender system, which is to identify the users who have similar preferences to the target user, and to predict the preference of the target user according ...

Comments

Information & Contributors

Information

Published In

ICSE '18: Proceedings of the 40th International Conference on Software Engineering: Companion Proceeedings

May 2018

231 pages

ISBN:9781450356633

DOI:10.1145/3183440

Conference Chair:
Michel Chaudron
Chalmers University of Technology, University of Gothenburg, Sweden
,
General Chair:
Ivica Crnkovic
Chalmers University of Technology, University of Gothenburg, Sweden
,
Program Chairs:
Marsha Chechik
University of Toronto, Canada
,
Mark Harman
Facebook and University College London, United Kingdom

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 May 2018

Check for updates

Author Tags

Qualifiers

Poster

Conference

ICSE '18

Sponsor:

SIGSOFT
IEEE-CS

ICSE '18: 40th International Conference on Software Engineering

May 27 - June 3, 2018

Gothenburg, Sweden

Acceptance Rates

Overall Acceptance Rate 276 of 1,856 submissions, 15%

Upcoming Conference

ICSE 2025

2025 IEEE/ACM 46th International Conference on Software Engineering

April 26 - May 3, 2025

Ottawa , ON , Canada

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
237
Total Downloads

Downloads (Last 12 months)13
Downloads (Last 6 weeks)0

Reflects downloads up to 23 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Linåker JPapatheocharous EOlsson T(2022)How to characterize the health of an Open Source Software project? A snowball literature review of an emerging practiceProceedings of the 18th International Symposium on Open Collaboration10.1145/3555051.3555067(1-12)Online publication date: 7-Sep-2022
https://dl.acm.org/doi/10.1145/3555051.3555067
Liu CGao CXia XLo DGrundy JYang X(2021)On the Reproducibility and Replicability of Deep Learning in Software EngineeringACM Transactions on Software Engineering and Methodology10.1145/347753531:1(1-46)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3477535
Liu CXia XLo DLiu ZHassan ALi S(2021)CodeMatcher: Searching Code Based on Sequential Semantics of Important Query WordsACM Transactions on Software Engineering and Methodology10.1145/346540331:1(1-37)Online publication date: 28-Sep-2021
https://dl.acm.org/doi/10.1145/3465403
Foundjem AEghan EAdams B(2021)Onboarding vs. Diversity, Productivity, and QualityProceedings of the 43rd International Conference on Software Engineering10.1109/ICSE43902.2021.00097(1033-1045)Online publication date: 22-May-2021
https://dl.acm.org/doi/10.1109/ICSE43902.2021.00097

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Collaborative factorization for recommender systems

Improving Accuracy of Recommender System by Item Clustering

A New Approach for Recommender System