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Mentors matter: Association of mentors with project success in the Apache Software Foundation Incubator

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  • Curtis Atkisson

Abstract

Mentoring has been a subject of study for 50 years. Most studies of mentoring programs evaluate the effect of the program on the participants but do not evaluate if different mentors have different effects on mentees. Open-source software (OSS) is software with a license that allows it to be freely used by other people. Such software has become foundational to the world economy. However, many OSS projects get abandoned by their creators. Various nonprofit organizations have arisen to help OSS projects become sustainable. One of the key services offered by many of these nonprofit organizations is a mentorship program where experienced OSS developers advise nascent projects on how to achieve sustainability. We use data from the Apache Software Foundation Incubator program where 303 mentors have mentored 286 projects, with most mentoring more than one project, to address this question: Is who a project has as a mentor associated with variation in project success? Who a project has as a mentor accounts for 45% of the variation in project outcomes, with some mentors being associated with positive and some with negative outcomes. These mentors could offer insights into how to improve the mentoring program. This result also demonstrates, more broadly, that the nature of specific mentoring relationships may be important to understanding how mentors impact outcomes in other mentoring programs.

Suggested Citation

  • Curtis Atkisson, 2022. "Mentors matter: Association of mentors with project success in the Apache Software Foundation Incubator," PLOS ONE, Public Library of Science, vol. 17(8), pages 1-15, August.
  • Handle: RePEc:plo:pone00:0272764
    DOI: 10.1371/journal.pone.0272764
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    References listed on IDEAS

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    1. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    2. Tjur, Tue, 2009. "Coefficients of Determination in Logistic Regression Models—A New Proposal: The Coefficient of Discrimination," The American Statistician, American Statistical Association, vol. 63(4), pages 366-372.
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