Abstract: Effective software engineering demands a coordinated effort. Unfortunately, a comprehensive view on developer coordination is rarely available to support software-engineering decisions, despite the significant implications on software quality, software architecture, and developer productivity. We present a fine-grained, verifiable, and fully automated approach to capture a view on developer coordination, based on commit information and source-code structure, mined from version-control systems. We apply methodology from network analysis and machine learning to identify developer communities automatically. Compared to previous work, our approach is fine-grained, and identifies statistically significant communities using order-statistics and a community-verification technique based on graph conductance. To demonstrate the scalability and generality of our approach, we analyze ten open-source projects with complex and active histories, written in various programming languages. By surveying 53 open-source developers from the ten projects, we validate the authenticity of inferred community structure with respect to reality. Our results indicate that developers of open-source projects form statistically significant community structures and this particular view on collaboration largely coincides with developers’ perceptions of real-world collaboration.
Keywords: Open source, social network analysis, developer networks, developer communities, respository mining, conductance
Reference: Mitchell Joblin, Wolfgang Mauerer, Sven Apel, Janet Siegmund, Dirk Riehle. “From Developer Networks to Verified Communities: A Fine-Grained Approach.” In Proceedings of the 37th International Conference on Software Engineering (ICSE 2015). IEEE Press, 563-573.
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