scholarly journals Social Network Analysis of Developers' and Users' Mailing Lists of Some Free Open Source Software

2015 ◽  
Author(s):  
Armel Jacques Nzekon Nzeko'o ◽  
Matthieu Latapy ◽  
Maurice Tchuente
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Open Source ◽  
Open Source Software ◽  
Free Open Source Software ◽  
Mailing Lists ◽  
Free Open Source

Finding Influential Nodes in Sourceforge.net Using Social Network Analysis

2021 ◽  
pp. 156-166
Author(s):  
K.G. Srinivasa ◽  
Ganesh Chandra Deka ◽  
Krishnaraj P.M.
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Open Source ◽  
Information Flow ◽  
Open Source Software ◽  
Small Set ◽  
Influential Nodes

The contribution of volunteers in the development of Free and Open Source Software in Sourceforge.net is studied in this paper. Using Social Network analysis, the small set of developers who can maximize the information flow in the network are discovered. The propagation of top developers across past three years are also studied. The four algorithms used to find top influential developers gives almost similar results. The movement of top developers over past years years was also consistent.

Free Software Development

2005 ◽  
pp. 152-173 ◽  
Author(s):  
Margaret S. Elliott ◽  
Walt Scacchi
Keyword(s):  
Software Development ◽  
Open Source ◽  
Open Source Software ◽  
Free Software ◽  
Work Practices ◽  
Mediated Communication ◽  
Open Source Software Development ◽  
Free Open Source Software ◽  
Mailing Lists ◽  
Free Open Source

This chapter presents an empirical study of a free software development community and how its virtual organizational culture influences its work practices. Results show that beliefs in free software and freedom of choice, and values in cooperative work and community influence work practices and norms. The authors wish to convey the importance of understanding the deeply held beliefs and values of the free software movement by showing how a free software development community uses computer-mediated communication in the form of IRC (instant messaging), mailing lists, and summary digests to mitigate and resolve conflicts, build a community, reinforce beliefs, and facilitate teamwork. Results are intended to assist future contributors and managers of free/open source software development projects in understanding the social world surrounding free/open source software development.

Who connects with whom? A social network analysis of an online open source software community

First Monday ◽  
2011 ◽  
Author(s):  
Cuihua Shen ◽  
Peter Monge
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Open Source ◽  
Open Source Software ◽  
Online Community ◽  
Online Social Network ◽  
The Social ◽  
Collaboration Dynamics ◽  
The Web

By examining “who connects with whom” in an online community using social network analysis, this study tests the social drivers that shape the collaboration dynamics among a group of participants from SourceForge, the largest open source community on the Web. The formation of the online social network was explored by testing two distinct network attachment logics: strategic selection and homophily. Both logics received some support. Taken together, the results are suggestive of a “performance-based clustering” phenomenon within the OSS online community in which most collaborations involve accomplished developers, and novice developers tend to partner with less accomplished and less experienced peers.

Finding Influential Nodes in Sourceforge.net Using Social Network Analysis

2018 ◽  
pp. 104-116
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Open Source ◽  
Information Flow ◽  
Open Source Software ◽  
Small Set ◽  
Influential Nodes

The contribution of volunteers in the development of Free and Open Source Software in Sourceforge.net is studied in this paper. Using Social Network analysis, the small set of developers who can maximize the information flow in the network are discovered. The propagation of top developers across past three years are also studied. The four algorithms used to find top influential developers gives almost similar results. The movement of top developers over past years years was also consistent.

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