Newcomer Candidate: Characterizing Contributions of a Novice Developer to GitHub

Ifraz Rehman, Dong Wang, Raula Gaikovina Kula, Takashi Ishio, Kenichi Matsumoto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

To attract, onboard, and retain any newcomer in Open Source Software (OSS) projects is vital to their livelihood. Recent studies conclude that OSS projects risk failure due to abandonment and poor participation of newcomers. Evidence suggests more new users are joining GitHub, however, the extent to which they contribute to OSS projects is unknown. In this study, we coin the term 'newcomer candidate' to describe new users to the GitHub platform. Our objective is to track and characterize their initial contributions. As a preliminary survey, we collected 208 newcomer candidate contributions in GitHub. Using this dataset, we then plan to track their contributions to reveal insights. We will use a mixed-methods approach, i.e., quantitative and qualitative, to identify whether or not newcomer candidates practice social coding, the kinds of their contributions, projects they target, and the proportion that they eventually onboard to an OSS project.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE International Conference on Software Maintenance and Evolution, ICSME 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages855
Number of pages1
ISBN (Electronic)9781728156194
DOIs
Publication statusPublished - Sep 2020
Externally publishedYes
Event36th IEEE International Conference on Software Maintenance and Evolution, ICSME 2020 - Virtual, Adelaide, Australia
Duration: Sep 27 2020Oct 3 2020

Publication series

NameProceedings - 2020 IEEE International Conference on Software Maintenance and Evolution, ICSME 2020

Conference

Conference36th IEEE International Conference on Software Maintenance and Evolution, ICSME 2020
Country/TerritoryAustralia
CityVirtual, Adelaide
Period9/27/2010/3/20

All Science Journal Classification (ASJC) codes

  • Software
  • Safety, Risk, Reliability and Quality
  • Modelling and Simulation

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