Understanding a developer social network and its evolution

  • Authors:

  • Qiaona Hong;Sunghun Kim;S. C. Cheung;Christian Bird

  • Affiliations:

  • Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong;Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong;Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong;Microsoft Research, USA

  • Venue:
  • ICSM '11 Proceedings of the 2011 27th IEEE International Conference on Software Maintenance
  • Year:
  • 2011

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Abstract

With the growing number of large scale software projects, software development and maintenance demands the participation of larger groups. Having a thorough understanding of the group of developers is critical for improving development and maintenance quality and reducing cost. In contrast to most commercial software endeavors, developers in open source software (OSS) projects enjoy more freedom to organize and contribute to a project in their own working style. Their interactions through various means in the project generate a latent developer social network (DSN). We have observed that developers and their relationships in these DSNs change continually under the influence of differences in the set of active developers and their changing activities. Revealing and understanding the structure and evolution of these social networks as well as their similarities and differences from other more general social networks (GSNs) is of value to our software engineering community, as it allows us to begin building an understanding of how well the findings from other fields based on GSNs apply to DSN. In this paper, we compare DSNs with popular GSNs such as Facebook, Twitter, Cyworld (a large social network in South Korea), and the Amazon recommendation network. We found, for instance, that while most social networks exhibit power law degree distributions, our DSNs do not. In addition, we also examine how DSNs evolve over time, highlighting how events within a project (such as a release of new software or the departure of prominent developers) impact the makeup of the DSNs, and observe the evolution of topological properties such as modularity and the paths of communities within these networks.