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| Title | GiLA: GitHub Label Analyzer |
| Authors | Javier Luis Canovas Izquierdo, Valerio Cosentino, Belen Rolandi, Alexandre Bergel, Jordi Cabot |
| Publication date | 2015 |
| Abstract | Reporting bugs, asking for new features and in general giving any kind of feedback is a common way to contribute to an Open-Source Software (OSS) project. In GitHub, the largest code hosting service for OSS, this feedback is typically expressed as new issues for the project managed by an issue-tracking system available in each new project repository. Among other features, the issue tracker allows creating and assigning labels to issues with the goal of helping the project community to better classify and manage those issues (e.g., facilitating the identification of issues for top priority components or candidate developers that could solve them). Nevertheless, as the project grows a manual browsing of the project issues is no longer feasible. In this paper we present GiLA, a tool which generates a set of visualizations to facilitate the analysis of issues in a project depending on their label-based categorization. We believe our visualizations are useful to see the most popular labels (and their relationships) in a project, identify the most active community members for those labels and compare the typical issue evolution for each label category. |
| Pages | 479-483 |
| Conference name | IEEE International Conference on Software Analysis, Evolution, and Reengineering |
| Publisher | IEEE Computer Society Press (Los Alamitos, CA, USA) |
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