Social network analysis of open source software: A review and categorisation

Abstract

and used or discarded based on predetermined inclusion and exclusion criteria. Research which focuses on the success factors of Open Source Software through Network Analysis is also examined. Results: A subjective categorisation is established for the papers: Structure, Lifecycle and Communication. It was found that the structure of a project has a large bearing on project success, with developers having previously worked together being indicative of project success. Other structure indicators of success are having a small but structured hierarchy, a diverse user and developer base, and project prominence. However, it was found that information on how these structures appear and evolve over time is lacking, and future research into temporal data models to determine project success information is suggested. Conclusions: A categorisation of existing research on Social Network Analysis is provided as a basis for further research. Further work into the lifecycle of OSS projects through Social Network Analysis of temporal project information is suggested.

Social-technical network effects in open source software communities: understanding the impacts of dependency networks on project success

“…Our results show that, even though both developer and module network measures can predict OSS project success when used separately, developer network measures are stronger predictors of OSS project success when both types of measures are considered simultaneously. Second, our research extends the literature on network effects in OSS development (McClean et al. , 2020).…”

“…Our results show that, even though both developer and module network measures can predict OSS project success when used separately, developer network measures are stronger predictors of OSS project success when both types of measures are considered simultaneously. Second, our research extends the literature on network effects in OSS development (McClean et al, 2020). Although some network metrics such as degree centrality, betweenness centrality and closeness centrality have been used to explain project success, task accomplishment and information quality (Sasidharan et al, 2012;Singh et al, 2011), this is the first investigation to on the effects of these network metrics in both module networks of the technical sub-system and developer networks of the social sub-system on project success.…”

“…Many researchers have theoretically and empirically studied the network effects of dependency networks in OSS development (Temizkan and Kumar 2015;Wang et al, 2019;Yang et al, 2021). To understand the success of an OSS project, social network analysis (SNA) has been used to examine the patterns in social dependency networks (McClean et al, 2020;Tan et al, 2007). The connectedness and embeddedness of social networks have been found to influence project success (Grewal et al, 2006;Tan et al, 2007), but little research has investigated the betweenness centrality and closeness centrality effects of the module dependency network (Grewal et al, 2006;Singh et al, 2011).…”

“…, 2021). To understand the success of an OSS project, social network analysis (SNA) has been used to examine the patterns in social dependency networks (McClean et al. , 2020; Tan et al.…”

A Systematic Literature Review on Trust in the Software Ecosystem

“…The number of engineers working on a project is the most frequently cited factor (Crowston, Annabi and Howison, 2003;S. Jansen, 2014;Sen, Singh and Borle, 2012;McClean, Greer and Jurek-Loughry, 2020;Midha and Palvia, 2012;Koch and Neumann, 2008;Walt Scacchi, 2007). These studies show that engineers positively impact the success of a project, especially for open source projects.…”