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Empirical Software Engineering

Erschienen in:

21.11.2017

Persistent code contribution: a ranking algorithm for code contribution in crowdsourced software

verfasst von: Michail Tsikerdekis

Erschienen in: Empirical Software Engineering | Ausgabe 4/2018

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Abstract

Measuring code contribution in crowdsourced software is essential for ranking contributors to a project or distributing revenue. Past studies have demonstrated that there is variation between different code contribution measures and their ability for ranking users accurately. This study proposes a new code contribution ranking algorithm, Persistent Code Contribution (PCC), that aims to be language independent, quality aware and provide a ranking balance between new and senior users. PCC tracks the number of characters contributed by a user and ranks each character based on the number of subsequent revisions that each character survived for. It also tracks lines that may have been moved between revisions in the code and attributes character changes to the appropriate user that committed them to a repository. A ranking comparison between existing code contribution measures is performed to determine the similarities and differences, and, quantitative as well as qualitative evidence is presented as a means to validate the algorithm.

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Titel: Persistent code contribution: a ranking algorithm for code contribution in crowdsourced software
verfasst von: Michail Tsikerdekis
Publikationsdatum: 21.11.2017
Verlag: Springer US
Erschienen in: Empirical Software Engineering / Ausgabe 4/2018
Print ISSN: 1382-3256
Elektronische ISSN: 1573-7616
DOI: https://doi.org/10.1007/s10664-017-9575-4

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