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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Information and Soft...arrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
Information and Software Technology
Article . 2021 . Peer-reviewed
License: Elsevier TDM
Data sources: Crossref
DBLP
Article . 2021
Data sources: DBLP
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Mining the Technical Roles of GitHub Users

Authors: João Eduardo Montandon; Marco Túlio Valente; Luciana Lourdes Silva;

Mining the Technical Roles of GitHub Users

Abstract

Abstract Context:Modern software development demands high levels of technical specialization. These conditions make IT companies focus on creating cross-functional teams, such as frontend, backend, and mobile developers. In this context, the success of software projects is highly influenced by the expertise of these teams in each field. Objective:In this paper, we investigate machine-learning based approaches to automatically identify the technical roles of open source developers. Method:For this, we first build a ground truth with 2284 developers labeled in six different roles: backend, frontend, full-stack, mobile, devops, and data science. Then, we build three different machine-learning models used to identify these roles. Results:These models presented competitive results for precision (0.88) and AUC (0.89) when identifying all six roles. Moreover, our results show that programming-languages are the most relevant features to predict the investigated roles. Conclusion:The approach proposed in this paper can assist companies during their hiring process, such as by recommending developers with the expertise required by job positions.

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    citations
    The total number of citations received by all articles of the researcher of interest.
    35
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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citations
The total number of citations received by all articles of the researcher of interest.
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
35
Top 10%
Top 10%
Top 10%
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