Whom are you going to call? determinants of @-mentions in Github discussions


Open Source Software (OSS) project success relies on crowd contributions. When an issue arises in pull-request based systems, @-mentions are used to call on people to task; previous studies have shown that @-mentions in discussions are associated with faster issue resolution. In most projects there may be many developers who could technically handle a variety of tasks. But OSS supports dynamic teams distributed across a wide variety of social and geographic backgrounds, as well as levels of involvement. It is, then, important to know whom to call on, i.e., who can be relied or trusted with important task-related duties, and why. In this paper, we sought to understand which observable socio-technical attributes of developers can be used to build good models of them being future @-mentioned in GitHub issues and pull request discussions. We built overall and project-specific predictive models of future @-mentions, in order to capture the determinants of @-mentions in each of two hundred GitHub projects, and to understand if and how those determinants differ between projects. We found that visibility, expertise, and productivity are associated with an increase in @-mentions, while responsiveness is not, in the presence of a number of control variables. Also, we find that though project-specific differences exist, the overall model can be used for cross-project prediction, indicating its GitHub-wide utility.

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  1. 1.

    E.g., developers of upstream libraries rarely respond in the downstream project.

  2. 2.

    Developers were asked about communication methods, not explicitly the @-mention.

  3. 3.

    Described in Section 4.2, a reply @-mention is directed towards someone already in the discussion; a call @-mention is directed towards someone not yet in the discussion. In our data, there is indeed a very high correlation between reply @-mentions and discussion length (0.812); however, there is a relatively low correlation between call @-mentions and discussion length (0.283). As our focus is on call @-mentions, correlation between reply @-mentions and discussion length is not a threat.

  4. 4.


  5. 5.

    PyGithub did not handle properly some Null responses from GitHub’s API.

  6. 6.

    Note that pull requests are a subset of issues.

  7. 7.

    Though we do use outdegree in our model as well.

  8. 8.

    E.g., standard algorithms require a full adjacency matrix to be in memory at once; memory will be exhausted for networks of our size.

  9. 9.

    This measure is originally called d by Bluthgen et al., but we will use δ here to reserve d to represent developers.

  10. 10.

    We do not use \(\mathcal {MAF}\), we use an analogous form for our social networks.

  11. 11.


  12. 12.

    We use issues fixed before closing as proxy for bugs; a higher value need not imply lack of aptitude, but it indicates a change in expected coding behavior and expertise.

  13. 13.

    \(\mathcal {ISS_{\kappa }}\) is not used for the zero component; it is undefined when call mentions are 0.

  14. 14.


  15. 15.

    We could not perform this in-depth study for discussions not in English.


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Kavaler, D., Devanbu, P. & Filkov, V. Whom are you going to call? determinants of @-mentions in Github discussions. Empir Software Eng 24, 3904–3932 (2019). https://doi.org/10.1007/s10664-019-09728-3

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  • Github
  • @-mention
  • Mention
  • Tagging
  • Social tagging
  • Issue