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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-21159
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dc.contributor.authorKallis, Rafael-
dc.contributor.authorDi Sorbo, Andrea-
dc.contributor.authorCanfora, Gerardo-
dc.contributor.authorPanichella, Sebastiano-
dc.date.accessioned2021-01-06T16:10:22Z-
dc.date.available2021-01-06T16:10:22Z-
dc.date.issued2020-
dc.identifier.issn0167-6423de_CH
dc.identifier.issn1872-7964de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/21159-
dc.description.abstractSoftware 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.de_CH
dc.language.isoende_CH
dc.publisherElsevierde_CH
dc.relation.ispartofScience of Computer Programmingde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectSoftware maintenance and evolutionde_CH
dc.subjectIssue report managementde_CH
dc.subjectLabeling unstructured datade_CH
dc.subject.ddc005: Computerprogrammierung, Programme und Datende_CH
dc.titlePredicting issue types on GitHubde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Informatik (InIT)de_CH
dc.identifier.doi10.1016/j.scico.2020.102598de_CH
dc.identifier.doi10.21256/zhaw-21159-
zhaw.funding.euinfo:eu-repo/grantAgreement/EC/H2020/957254//DevOps for Complex Cyber-physical Systems/COSMOSde_CH
zhaw.issue102598de_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statusacceptedVersionde_CH
zhaw.volume205de_CH
zhaw.embargo.end2022-12-31de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedSoftware Systemsde_CH
zhaw.funding.zhawCOSMOS – DevOps for Complex Cyber-physical Systems of Systemsde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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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). https://doi.org/10.1016/j.scico.2020.102598
Kallis, R. et al. (2020) ‘Predicting issue types on GitHub’, Science of Computer Programming, 205(102598). Available at: https://doi.org/10.1016/j.scico.2020.102598.
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). https://doi.org/10.1016/j.scico.2020.102598.
Kallis, Rafael, et al. “Predicting Issue Types on GitHub.” Science of Computer Programming, vol. 205, no. 102598, 2020, https://doi.org/10.1016/j.scico.2020.102598.


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