Empirical Software Engineering

, Volume 22, Issue 1, pp 407–435 | Cite as

Stochastic actor-oriented modeling for studying homophily and social influence in OSS projects

  • David Kavaler
  • Vladimir Filkov


Open Source Software projects are communities in which people “learn the ropes” from each other. The social and technical activities of developers evolve together, and as they link to each other they get organized in a network of changing socio-technical connections. Traces of those activities, or behaviors, are typically visible to all, in project repositories and through communication between them. Thus, in principle it may be possible to study those traces to tell which of the observable socio-technical behaviors of developers in these projects are responsible for the forming of persistent links between them. It may also be possible to tell the extent to which links participate in the spread of potential behavioral influences. Since OSS projects change in both social and technical activity over time, static approaches, that either ignore time or simplify it to a few slices, are frequently inadequate to study these networks. On the other hand, ad-hoc dynamic approaches are often only loosely supported by theory and can yield misleading findings. Here we adapt the stochastic actor-oriented models from social network analysis. These models enable the study of the interplay between behavior, influence and network architecture, for dynamic networks, in a statistically sound way. We apply the stochastic actor-oriented models in case studies of two Apache Software Foundation projects, and study code ownership and developer productivity as behaviors. For those, we find evidence of significant social selection effects (homophily) in both projects, but in different directions. However, we find no evidence for the spread (social influence) of either code ownership or developer productivity behaviors through the networks.


Actor oriented models Apache Open source Social influence Social selection Homophily Siena 



The authors want to thank Mohammad Gharehyazie for sharing his ASF project data. We thank Saheel Godhane for fruitful discussion during the early stages of the project. We thank anonymous reviewers for their helpful suggestions on prior versions of manuscript. The authors gratefully acknowledge support from the Air Force Office of Scientific Research, award FA955-11-1-0246, and a faculty grant from UC Davis. The authors are thankful for generous support from UC Davis.


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Computer Science DepartmentUniversity of California, DavisDavisUSA

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