Characterization and prediction of popular projects on GitHub

Junxiao Han; Shuiguang Deng; Xin Xia; Dongjing Wang; Jianwei Yin

doi:10.1109/COMPSAC.2019.00013

Characterization and prediction of popular projects on GitHub

Junxiao Han, Shuiguang Deng, Xin Xia, Dongjing Wang, Jianwei Yin

Department of Software Systems & Cybersecurity

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

31 Citations (Scopus)

Abstract

GitHub is a large and popular open source project platform, which hosts various open source projects. Despite the prevalence of GitHub platform, not every project has gained high popularity. Identification of popular projects on GitHub can help developers choose proper projects to follow or contribute to, as well as provide guidance in building a popular project. In this paper, we propose an approach to predict the popularity of GitHub projects. We first conducted online surveys with GitHub users to determine the threshold (the number of stars of a project) of popular and unpopular projects. Next, we extract 35 features from both GitHub and Stack Overflow, which are divided into three dimensions: project, evolutionary, and project owner. A random forest classifier is built based on these features to identify popular GitHub projects. To evaluate the performance of our approach, we collect a large-scale dataset from GitHub which contains a total of 409,784 GitHub projects and 174,784 GitHub users. Our model achieves an average AUC of 0.76, which statistically significantly improves state-of-the-art by a substantial margin. We also study which features are of the most importance in distinguishing popular projects from unpopular ones. Experimental results show that number of branches, number of open issues, and number of contributors play the most important roles in identification of popular projects, and all of them have large effect size.

Original language	English
Title of host publication	Proceedings - 2019 IEEE 43rd Annual Computer Software and Applications Conference, COMPSAC 2019
Editors	Vladimir Getov, Jean-Luc Gaudiot, Nariyoshi Yamai, Stelvio Cimato, Morris Chang, Yuuichi Teranishi, Ji-Jiang Yang, Hong Va Leong, Hossian Shahriar, Michiharu Takemoto, Dave Towey, Hiroki Takakura, Atilla Elci, Susumu Takeuchi, Satish Puri
Place of Publication	Piscataway NJ USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	21-26
Number of pages	6
ISBN (Electronic)	9781728126074
DOIs	https://doi.org/10.1109/COMPSAC.2019.00013
Publication status	Published - 2019
Event	International Computer Software and Applications Conference 2019 - Milwaukee, United States of America Duration: 15 Jul 2019 → 19 Jul 2019 Conference number: 43rd https://ieeecompsac.computer.org/2019/ https://ieeexplore.ieee.org/xpl/conhome/8746989/proceeding (Proceedings)

Conference

Conference	International Computer Software and Applications Conference 2019
Abbreviated title	COMPSAC 2019
Country/Territory	United States of America
City	Milwaukee
Period	15/07/19 → 19/07/19
Internet address	https://ieeecompsac.computer.org/2019/ https://ieeexplore.ieee.org/xpl/conhome/8746989/proceeding (Proceedings)

Keywords

Feature Engineering
GitHub Project
Popularity
Prediction Model

Access to Document

10.1109/COMPSAC.2019.00013

Cite this

Han, J., Deng, S., Xia, X., Wang, D., & Yin, J. (2019). Characterization and prediction of popular projects on GitHub. In V. Getov, J.-L. Gaudiot, N. Yamai, S. Cimato, M. Chang, Y. Teranishi, J.-J. Yang, H. V. Leong, H. Shahriar, M. Takemoto, D. Towey, H. Takakura, A. Elci, S. Takeuchi, & S. Puri (Eds.), Proceedings - 2019 IEEE 43rd Annual Computer Software and Applications Conference, COMPSAC 2019 (pp. 21-26). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/COMPSAC.2019.00013

Han, Junxiao ; Deng, Shuiguang ; Xia, Xin et al. / Characterization and prediction of popular projects on GitHub. Proceedings - 2019 IEEE 43rd Annual Computer Software and Applications Conference, COMPSAC 2019. editor / Vladimir Getov ; Jean-Luc Gaudiot ; Nariyoshi Yamai ; Stelvio Cimato ; Morris Chang ; Yuuichi Teranishi ; Ji-Jiang Yang ; Hong Va Leong ; Hossian Shahriar ; Michiharu Takemoto ; Dave Towey ; Hiroki Takakura ; Atilla Elci ; Susumu Takeuchi ; Satish Puri. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2019. pp. 21-26

@inproceedings{3faa0a6d4ddc4006b2e8f712a1264b4d,

title = "Characterization and prediction of popular projects on GitHub",

abstract = "GitHub is a large and popular open source project platform, which hosts various open source projects. Despite the prevalence of GitHub platform, not every project has gained high popularity. Identification of popular projects on GitHub can help developers choose proper projects to follow or contribute to, as well as provide guidance in building a popular project. In this paper, we propose an approach to predict the popularity of GitHub projects. We first conducted online surveys with GitHub users to determine the threshold (the number of stars of a project) of popular and unpopular projects. Next, we extract 35 features from both GitHub and Stack Overflow, which are divided into three dimensions: project, evolutionary, and project owner. A random forest classifier is built based on these features to identify popular GitHub projects. To evaluate the performance of our approach, we collect a large-scale dataset from GitHub which contains a total of 409,784 GitHub projects and 174,784 GitHub users. Our model achieves an average AUC of 0.76, which statistically significantly improves state-of-the-art by a substantial margin. We also study which features are of the most importance in distinguishing popular projects from unpopular ones. Experimental results show that number of branches, number of open issues, and number of contributors play the most important roles in identification of popular projects, and all of them have large effect size.",

keywords = "Feature Engineering, GitHub Project, Popularity, Prediction Model",

author = "Junxiao Han and Shuiguang Deng and Xin Xia and Dongjing Wang and Jianwei Yin",

year = "2019",

doi = "10.1109/COMPSAC.2019.00013",

language = "English",

pages = "21--26",

editor = "Vladimir Getov and Jean-Luc Gaudiot and Nariyoshi Yamai and Stelvio Cimato and Morris Chang and Yuuichi Teranishi and Ji-Jiang Yang and Leong, {Hong Va} and Hossian Shahriar and Michiharu Takemoto and Dave Towey and Hiroki Takakura and Atilla Elci and Susumu Takeuchi and Satish Puri",

booktitle = "Proceedings - 2019 IEEE 43rd Annual Computer Software and Applications Conference, COMPSAC 2019",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States of America",

note = "International Computer Software and Applications Conference 2019, COMPSAC 2019 ; Conference date: 15-07-2019 Through 19-07-2019",

url = "https://ieeecompsac.computer.org/2019/, https://ieeexplore.ieee.org/xpl/conhome/8746989/proceeding",

}

Han, J, Deng, S, Xia, X, Wang, D & Yin, J 2019, Characterization and prediction of popular projects on GitHub. in V Getov, J-L Gaudiot, N Yamai, S Cimato, M Chang, Y Teranishi, J-J Yang, HV Leong, H Shahriar, M Takemoto, D Towey, H Takakura, A Elci, S Takeuchi & S Puri (eds), Proceedings - 2019 IEEE 43rd Annual Computer Software and Applications Conference, COMPSAC 2019. IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, pp. 21-26, International Computer Software and Applications Conference 2019, Milwaukee, Wisconsin, United States of America, 15/07/19. https://doi.org/10.1109/COMPSAC.2019.00013

Characterization and prediction of popular projects on GitHub. / Han, Junxiao; Deng, Shuiguang; Xia, Xin et al.
Proceedings - 2019 IEEE 43rd Annual Computer Software and Applications Conference, COMPSAC 2019. ed. / Vladimir Getov; Jean-Luc Gaudiot; Nariyoshi Yamai; Stelvio Cimato; Morris Chang; Yuuichi Teranishi; Ji-Jiang Yang; Hong Va Leong; Hossian Shahriar; Michiharu Takemoto; Dave Towey; Hiroki Takakura; Atilla Elci; Susumu Takeuchi; Satish Puri. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, 2019. p. 21-26.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

TY - GEN

T1 - Characterization and prediction of popular projects on GitHub

AU - Han, Junxiao

AU - Deng, Shuiguang

AU - Xia, Xin

AU - Wang, Dongjing

AU - Yin, Jianwei

N1 - Conference code: 43rd

PY - 2019

Y1 - 2019

N2 - GitHub is a large and popular open source project platform, which hosts various open source projects. Despite the prevalence of GitHub platform, not every project has gained high popularity. Identification of popular projects on GitHub can help developers choose proper projects to follow or contribute to, as well as provide guidance in building a popular project. In this paper, we propose an approach to predict the popularity of GitHub projects. We first conducted online surveys with GitHub users to determine the threshold (the number of stars of a project) of popular and unpopular projects. Next, we extract 35 features from both GitHub and Stack Overflow, which are divided into three dimensions: project, evolutionary, and project owner. A random forest classifier is built based on these features to identify popular GitHub projects. To evaluate the performance of our approach, we collect a large-scale dataset from GitHub which contains a total of 409,784 GitHub projects and 174,784 GitHub users. Our model achieves an average AUC of 0.76, which statistically significantly improves state-of-the-art by a substantial margin. We also study which features are of the most importance in distinguishing popular projects from unpopular ones. Experimental results show that number of branches, number of open issues, and number of contributors play the most important roles in identification of popular projects, and all of them have large effect size.

AB - GitHub is a large and popular open source project platform, which hosts various open source projects. Despite the prevalence of GitHub platform, not every project has gained high popularity. Identification of popular projects on GitHub can help developers choose proper projects to follow or contribute to, as well as provide guidance in building a popular project. In this paper, we propose an approach to predict the popularity of GitHub projects. We first conducted online surveys with GitHub users to determine the threshold (the number of stars of a project) of popular and unpopular projects. Next, we extract 35 features from both GitHub and Stack Overflow, which are divided into three dimensions: project, evolutionary, and project owner. A random forest classifier is built based on these features to identify popular GitHub projects. To evaluate the performance of our approach, we collect a large-scale dataset from GitHub which contains a total of 409,784 GitHub projects and 174,784 GitHub users. Our model achieves an average AUC of 0.76, which statistically significantly improves state-of-the-art by a substantial margin. We also study which features are of the most importance in distinguishing popular projects from unpopular ones. Experimental results show that number of branches, number of open issues, and number of contributors play the most important roles in identification of popular projects, and all of them have large effect size.

KW - Feature Engineering

KW - GitHub Project

KW - Popularity

KW - Prediction Model

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U2 - 10.1109/COMPSAC.2019.00013

DO - 10.1109/COMPSAC.2019.00013

M3 - Conference Paper

AN - SCOPUS:85072692151

SP - 21

EP - 26

BT - Proceedings - 2019 IEEE 43rd Annual Computer Software and Applications Conference, COMPSAC 2019

A2 - Getov, Vladimir

A2 - Gaudiot, Jean-Luc

A2 - Yamai, Nariyoshi

A2 - Cimato, Stelvio

A2 - Chang, Morris

A2 - Teranishi, Yuuichi

A2 - Yang, Ji-Jiang

A2 - Leong, Hong Va

A2 - Shahriar, Hossian

A2 - Takemoto, Michiharu

A2 - Towey, Dave

A2 - Takakura, Hiroki

A2 - Elci, Atilla

A2 - Takeuchi, Susumu

A2 - Puri, Satish

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - Piscataway NJ USA

T2 - International Computer Software and Applications Conference 2019

Y2 - 15 July 2019 through 19 July 2019

ER -

Han J, Deng S, Xia X, Wang D, Yin J. Characterization and prediction of popular projects on GitHub. In Getov V, Gaudiot JL, Yamai N, Cimato S, Chang M, Teranishi Y, Yang JJ, Leong HV, Shahriar H, Takemoto M, Towey D, Takakura H, Elci A, Takeuchi S, Puri S, editors, Proceedings - 2019 IEEE 43rd Annual Computer Software and Applications Conference, COMPSAC 2019. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. 2019. p. 21-26 doi: 10.1109/COMPSAC.2019.00013

Characterization and prediction of popular projects on GitHub

Abstract

Conference

Keywords

Access to Document

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