Tsikerdekis, M. (2018).
Persistent Code Contribution: A Ranking Algorithm for Code Contribution in Crowdsourced Software.
Empirical Software Engineering. 24(3), 1871-1894. doi: 10.1007/s10664-017-9575-4
Impact Factor: 3.275
© 2017 Springer Science+Business Media, LLC, part of Springer Nature 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.