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Publication type: Article in scientific journal
Type of review: Peer review (publication)
Title: Predicting issue types on GitHub
Authors: Kallis, Rafael
Di Sorbo, Andrea
Canfora, Gerardo
Panichella, Sebastiano
et. al: No
DOI: 10.1016/j.scico.2020.102598
Published in: Science of Computer Programming
Volume(Issue): 205
Issue: 102598
Issue Date: 2020
Publisher / Ed. Institution: Elsevier
ISSN: 0167-6423
Language: English
Subjects: Software maintenance and evolution; Issue report management; Labeling unstructured data
Subject (DDC): 005: Computer programming, programs and data
Abstract: Software maintenance and evolution involves critical activities for the success of software projects. To support such activities and keep code up-to-date and error-free, software communities make use of issue trackers, i.e., tools for signaling, handling, and addressing the issues occurring in software systems. However, in popular projects, tens or hundreds of issue reports are daily submitted. In this context, identifying the type of each submitted report (e.g., bug report, feature request, etc.) would facilitate the management and the prioritization of the issues to address. To support issue handling activities, in this paper, we propose Ticket Tagger, a GitHub app analyzing the issue title and description through machine learning techniques to automatically recognize the types of reports submitted on GitHub and assign labels to each issue accordingly. We empirically evaluated the tool's prediction performance on about 30,000 GitHub issues. Our results show that the Ticket Tagger can identify the correct labels to assign to GitHub issues with reasonably high effectiveness. Considering these results and the fact that the tool is designed to be easily integrated in the GitHub issue management process, Ticket Tagger consists in a useful solution for developers.
Fulltext version: Accepted version
License (according to publishing contract): Licence according to publishing contract
Restricted until: 2022-12-31
Departement: School of Engineering
Organisational Unit: Institute of Applied Information Technology (InIT)
Published as part of the ZHAW project: COSMOS – DevOps for Complex Cyber-physical Systems of Systems
Appears in collections:Publikationen School of Engineering

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Kallis, R., Di Sorbo, A., Canfora, G., & Panichella, S. (2020). Predicting issue types on GitHub. Science of Computer Programming, 205(102598).
Kallis, R. et al. (2020) ‘Predicting issue types on GitHub’, Science of Computer Programming, 205(102598). Available at:
R. Kallis, A. Di Sorbo, G. Canfora, and S. Panichella, “Predicting issue types on GitHub,” Science of Computer Programming, vol. 205, no. 102598, 2020, doi: 10.1016/j.scico.2020.102598.
KALLIS, Rafael, Andrea DI SORBO, Gerardo CANFORA und Sebastiano PANICHELLA, 2020. Predicting issue types on GitHub. Science of Computer Programming. 2020. Bd. 205, Nr. 102598. DOI 10.1016/j.scico.2020.102598
Kallis, Rafael, Andrea Di Sorbo, Gerardo Canfora, and Sebastiano Panichella. 2020. “Predicting Issue Types on GitHub.” Science of Computer Programming 205 (102598).
Kallis, Rafael, et al. “Predicting Issue Types on GitHub.” Science of Computer Programming, vol. 205, no. 102598, 2020,

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