Latent social structure in open source projects

By Christian Bird, David S. Pattison, Raissa M. D’Souza, Vladimir Filkov, Premkumar T. Devanbu. SIGSOFT '08/FSE-16, 2008

311 citations recorded by the source corpus.

Commercial software project managers design project organizational structure carefully, mindful of available skills, division of labour, geographical boundaries, etc. These organizational "cathedrals" are to be contrasted with the "bazaar-like" nature of Open Source Software (OSS) Projects, which have no pre-designed organizational structure. Any structure that exists is dynamic, self-organizing, latent, and usually not explicitly stated. Still, in large, complex, successful, OSS projects, we do expect that subcommunities will form spontaneously within the developer teams. Studying these subcommunities, and their behavior can shed light on how successful OSS projects self-organize. This phenomenon could well hold important lessons for how commercial software teams might be organized. Building on known well-established techniques for detecting community structure in complex networks, we extract and study latent subcommunities from the email social network of several projects: Apache HTTPD, Python, PostgresSQL, Perl, and Apache ANT. We then validate them with software development activity history. Our results show that subcommunities do indeed spontaneously arise within these projects as the projects evolve. These subcommunities manifest most strongly in technical discussions, and are significantly connected with collaboration behaviour.

Subfields: Computer Science Applications, Information Systems, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/9cf9bb212d73a54686181cc9ab4d3488204baac4

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In Statistical and Nonlinear Physics

Citations in the corpus

Finding social roles in Wikipedia (2011, methodology): “In this way, Wikipedia is similar to open source software development, and in both domains, researchers have studied questions about why people participate [4], [27], the quality of the resulting artifacts [1], [23], and how coordination relates to the quality and structure of the work [3] [17].”
Improving bug triage with bug tossing graphs (2009, background): “Goals for this research include understanding the developer structure, improving the development process, and locating faults [6–8,22].”
The Mirroring Hypothesis: Theory, Evidence and Exceptions (2016, background): “However, three other studies found no evidence of strict partitioning within codebases nor any correspondence between identifiable organizational groups and specific modules (Bird et al., 2008; Bowman and Holt, 1998; Gutwin et al., 2004).”
How do Data Science Workers Collaborate? Roles, Workflows, and Tools (2020, result): “In contrast to the current literature in data science, software engineering has built a strong literature on collaborative practices in software development [42, 53, 87], including in both open source communities [9, 17] and industry teams [6].”
The promises and perils of mining GitHub (2009, methodology): “Researchers have also used this data to create recommender systems [3], [4], [5], study evolution patterns [6], [7], [8], predict bugs [9], [10], [11], and examine collaboration [12], [13], [14].”
Gender and Tenure Diversity in GitHub Teams (2015, background): “In general, in OSS projects the existence of latent social structures (communities) corresponding to code modules has been documented [7].”
Anomaly detection in online social networks (2014, background): “…has also been used to detect important and in-ﬂuential individuals (Shetty and Adibi, 2005), individuals fulﬁlling particular roles within a community (Welser et al., 2011), levels of community participation (Bird et al., 2008), and unusual patterns in email traﬃc (Eberle and Holder, 2007).”
The promises and perils of mining git (2009, unknown): “A. E. Housman, A Shropshire Lad”