The Effect of “Good First Issue” Indicators upon Newcomer Developers

Identifying Improvements for Newcomer Task Recommendation

Master thesis (2020)

Authors

J.W.D. Alderliesten Electrical Engineering, Mathematics and Computer Science

Contributors

A.E. Zaidman Software Engineering - EEMCS (mentor)
Rafael Bidarra Computer Graphics and Visualisation - EEMCS (graduation committee member)
G. Gousios Software Engineering - EEMCS (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2020 David Alderliesten
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Published Date

07-07-2020

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

The recommendation of tasks for newcomers within a software project throughgood first issues is being done within the domain of software development, such as onGithubplatform. These issues aim to help newcomers identify tasks that are suitablefor them and their level of expertise within the project. This thesis report investigatesthe effectiveness regarding developer onboarding and task completion ofgood firstissues by data mining a set of 105 repositories and manually analyzing at most 30good first issues and 30 initial commits per sampled project. It was found that, althoughgood first issues are effective at developer onboarding, and developers perceivegoodfirst issues as being useful, changes can be made to the types of tasks suggested asgood first issues to match the types of initial contributions made by newcomers. It wasalso found that developers with less than a year of experience favoreddocumentation-related contributions for their first commit to a project.