Article

Co-evolution of project documentation and popularity within github

Authors:

Karan Aggarwal,

Abram Hindle,

Eleni StrouliaAuthors Info & Claims

MSR 2014: Proceedings of the 11th Working Conference on Mining Software Repositories

Pages 360 - 363

https://doi.org/10.1145/2597073.2597120

Published: 31 May 2014 Publication History

Get Access

Abstract

Github is a very popular collaborative software-development platform that provides typical source-code management and issue tracking features augmented by strong social-networking features such as following developers and watching projects. These features help ``spread the word'' about individuals and projects, building the reputation of the former and increasing the popularity of the latter. In this paper, we investigate the relation between project popularity and regular, consistent documentation updates. We found strong indicators that consistently popular projects exhibited consistent documentation effort and that this effort tended to attract more documentation collaborators. We also found that frameworks required more documentation effort than libraries to achieve similar adoption success, especially in the initial phase.

References

[1]

L. Dabbish, C. Stuart, J. Tsay, and J. Herbsleb. Social coding in github: transparency and collaboration in an open software repository. In Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work, pages 1277–1286. ACM, 2012.

Digital Library

Google Scholar

[2]

B. Dagenais and M. P. Robillard. Creating and evolving developer documentation: understanding the decisions of open source contributors. In Proceedings of the eighteenth ACM SIGSOFT international symposium on Foundations of software engineering, pages 127–136. ACM, 2010.

Digital Library

Google Scholar

[3]

G. Gousios. The ghtorrent dataset and tool suite. In Proceedings of the 10th Working Conference on Mining Software Repositories, MSR’13, pages 233–236, 2013.

Digital Library

Google Scholar

[4]

M. J. Lee, B. Ferwerda, J. Choi, J. Hahn, J. Y. Moon, and J. Kim. Github developers use rockstars to overcome overflow of news. In CHI’13 Extended Abstracts on Human Factors in Computing Systems, pages 133–138. ACM, 2013.

Digital Library

Google Scholar

[5]

L. Shi, H. Zhong, T. Xie, and M. Li. An empirical study on evolution of api documentation. In Fundamental Approaches to Software Engineering, pages 416–431. Springer, 2011.

Digital Library

Google Scholar

[6]

M.-A. Storey, C. Treude, A. van Deursen, and L.-T. Cheng. The impact of social media on software engineering practices and tools. In Proceedings of the FSE/SDP workshop on Future of software engineering research, pages 359–364. ACM, 2010.

Digital Library

Google Scholar

Cited By

View all

Rashid EMastorakis N(2024)Monotonic and Linear Relations between Growth of Quality vs Growth in Quantity in Open-Source Software ProjectsWSEAS TRANSACTIONS ON COMPUTERS10.37394/23205.2024.23.423(41-50)Online publication date: 8-Apr-2024
https://doi.org/10.37394/23205.2024.23.4
Puangjaktha PChoetkiertikul MTuarob S(2024)“Paper, Meet Code”: A Deep Learning Approach to Linking Scholarly Articles With GitHub RepositoriesIEEE Access10.1109/ACCESS.2024.339976712(68410-68426)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3399767
Sitthithanasakul SChinthanet BKula RNetsiwawichian NLeelaprute PManaskasemsak BRungsawang AMatsumoto K(2023)Do Developers Present Proficient Code Snippets in Their README Files? An Analysis of PyPI Libraries in GitHubJournal of Information Processing10.2197/ipsjjip.31.67931(679-688)Online publication date: 2023
https://doi.org/10.2197/ipsjjip.31.679
Show More Cited By

Index Terms

Co-evolution of project documentation and popularity within github
1. General and reference
  1. Cross-computing tools and techniques
    1. Metrics
2. Information systems
  1. Information systems applications

Recommendations

Predicting Arabic Tweet Popularity by Use of Data and Text Mining Techniques
MEDES '14: Proceedings of the 6th International Conference on Management of Emergent Digital EcoSystems

Among the emerging "social media" or "social networks" applications facilitating communication between individuals, such as YouTube, LinkedIn, and Facebook, Twitter has become one of the most-used of these applications in Arab countries. Twitter works ...
Machine Learning Approach to Analyze and Predict the Popularity of Tweets with Images
Challenges and Opportunities in the Digital Era
Abstract
Social Media platforms play a major role in spreading information. Twitter, is one such platform which is used by millions of people to share information every day. Twitter with the recent introduction of a feature that helps its users to attach ...
Analyzing the Impact of User Behaviors on the Popularity of Tweets: A Use Case from Masking Conversations During the Covid-19 Pandemic
NLPIR '22: Proceedings of the 2022 6th International Conference on Natural Language Processing and Information Retrieval

The Covid-19 pandemic has unleashed an infodemic of misinformation especially about important health measures such as vaccines and masks. Social media companies have struggled to keep up with identifying content that separates these falsehoods from the ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

MSR 2014: Proceedings of the 11th Working Conference on Mining Software Repositories

May 2014

427 pages

ISBN:9781450328630

DOI:10.1145/2597073

General Chair:
Premkumar Devanbu
University of California at Davis, USA
,
Program Chairs:
Sung Kim
Hong Kong University of Science and Technology, China
,
Martin Pinzger
University of Klagenfurt, Austria

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 the author(s) 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].

In-Cooperation

TCSE: IEEE Computer Society's Tech. Council on Software Engin.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 31 May 2014

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Conference

ICSE '14

Sponsor:

SIGSOFT

ICSE '14: 36th International Conference on Software Engineering

May 31 - June 1, 2014

Hyderabad, India

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

35
Total Citations
View Citations
771
Total Downloads

Downloads (Last 12 months)64
Downloads (Last 6 weeks)7

Reflects downloads up to 13 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Rashid EMastorakis N(2024)Monotonic and Linear Relations between Growth of Quality vs Growth in Quantity in Open-Source Software ProjectsWSEAS TRANSACTIONS ON COMPUTERS10.37394/23205.2024.23.423(41-50)Online publication date: 8-Apr-2024
https://doi.org/10.37394/23205.2024.23.4
Puangjaktha PChoetkiertikul MTuarob S(2024)“Paper, Meet Code”: A Deep Learning Approach to Linking Scholarly Articles With GitHub RepositoriesIEEE Access10.1109/ACCESS.2024.339976712(68410-68426)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3399767
Sitthithanasakul SChinthanet BKula RNetsiwawichian NLeelaprute PManaskasemsak BRungsawang AMatsumoto K(2023)Do Developers Present Proficient Code Snippets in Their README Files? An Analysis of PyPI Libraries in GitHubJournal of Information Processing10.2197/ipsjjip.31.67931(679-688)Online publication date: 2023
https://doi.org/10.2197/ipsjjip.31.679
Zhou YGong LLi LZhang HYu XKeung J(2023)How to Make Your Deep Learning Repository Popular: A Case Study on GithubSSRN Electronic Journal10.2139/ssrn.4351202Online publication date: 2023
https://doi.org/10.2139/ssrn.4351202
Duran JSakib MEisty NSpezzano F(2023)Evaluating Code Metrics in GitHub Repositories Related to Fake News and Misinformation2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)10.1109/SERA57763.2023.10197739(182-188)Online publication date: 23-May-2023
https://doi.org/10.1109/SERA57763.2023.10197739
Wang TWang SChen T(2023)Study the correlation between the readme file of GitHub projects and their popularityJournal of Systems and Software10.1016/j.jss.2023.111806205(111806)Online publication date: Nov-2023
https://doi.org/10.1016/j.jss.2023.111806
Karanikiotis TSymeonidis A(2023)Towards Extracting Reusable and Maintainable Code SnippetsSoftware Technologies10.1007/978-3-031-37231-5_9(187-206)Online publication date: 19-Jul-2023
https://doi.org/10.1007/978-3-031-37231-5_9
Seker ADiri BArslan HAmasyalı M(2022)Open Source Software Development ChallengesResearch Anthology on Agile Software, Software Development, and Testing10.4018/978-1-6684-3702-5.ch102(2134-2164)Online publication date: 2022
https://doi.org/10.4018/978-1-6684-3702-5.ch102
Xia TFu WShu RAgrawal RMenzies T(2022)Predicting health indicators for open source projects (using hyperparameter optimization)Empirical Software Engineering10.1007/s10664-022-10171-027:6Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1007/s10664-022-10171-0
Seker ADiri BArslan HAmasyalı M(2021)Open Source Software Development ChallengesResearch Anthology on Usage and Development of Open Source Software10.4018/978-1-7998-9158-1.ch003(33-62)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-9158-1.ch003
Show More Cited By

View Options

Get Access

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

Predicting Arabic Tweet Popularity by Use of Data and Text Mining Techniques

Machine Learning Approach to Analyze and Predict the Popularity of Tweets with Images

Analyzing the Impact of User Behaviors on the Popularity of Tweets: A Use Case from Masking Conversations During the Covid-19 Pandemic