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Ranking Developer Candidates By Social Links

Author

Listed:
  • QI XUAN

    (Department of Automation, Zhejiang University of Technology, Hangzhou 310023, P. R. China;
    Department of Computer Science, University of California, Davis, CA 95616-8562, USA)

  • CHENBO FU

    (Department of Automation, Zhejiang University of Technology, Hangzhou 310023, P. R. China)

  • LI YU

    (Department of Automation, Zhejiang University of Technology, Hangzhou 310023, P. R. China)

Abstract

In open source software (OSS) projects, participants initially communicate with others and then may become developers if they are deemed worthy by the community. Recent studies indicate that the abundance of established social links of a participant is the strongest predictor to his/her promotion. Having reliable rankings of the candidates is key to recruiting and maintaining a successful operation of an OSS project. This paper adopts degree-based, PageRank, and Hits ranking algorithms to rank developer candidates in OSS projects based on their social links. We construct several types of social networks based on the communications between the participants in Apache OSS projects, then train and test the ranking algorithms in these networks. We find that, for all the ranking algorithms under study, the rankings of emergent developers in temporal networks are higher than those in cumulative ones, indicating that the more recent communications of a developer in a project are more important to predict his/her first commit in the project. By comparison, the simple degree-based and the PageRank ranking algorithms in temporal undirected weighted networks behave better than the others in identifying emergent developers based on four performance indicators, and are thus recommended to be applied in the future.

Suggested Citation

  • Qi Xuan & Chenbo Fu & Li Yu, 2014. "Ranking Developer Candidates By Social Links," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 17(07n08), pages 1-19.
    • Handle: RePEc:wsi:acsxxx:v:17:y:2014:i:07n08:n:s0219525915500058
    DOI: 10.1142/S0219525915500058
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    Cited by:

    1. Neda Mohammadi & Qi Wang & John E Taylor, 2016. "Diffusion Dynamics of Energy Saving Practices in Large Heterogeneous Online Networks," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-23, October.

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