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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-19309
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dc.contributor.authorKallis, Rafael-
dc.contributor.authorDi Sorbo, Andrea-
dc.contributor.authorCanfora, Gerardo-
dc.contributor.authorPanichella, Sebastiano-
dc.date.accessioned2020-01-30T10:54:05Z-
dc.date.available2020-01-30T10:54:05Z-
dc.date.issued2019-
dc.identifier.isbn978-1-7281-3094-1de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/19309-
dc.description​© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.de_CH
dc.description.abstractSoftware maintenance is crucial for software projects evolution and success: code should be kept up-to-date and error-free, this with little effort and continuous updates for the end-users. In this context, issue trackers are essential tools for creating, managing and addressing the several (often hundreds of) issues that occur in software systems. A critical aspect for handling and prioritizing issues involves the assignment of labels to them (e.g., for projects hosted on GitHub), in order to determine the type (e.g., bug report, feature request and so on) of each specific issue. Although this labeling process has a positive impact on the effectiveness of issue processing, the current labeling mechanism is scarcely used on GitHub. In this demo, we introduce a tool, called Ticket Tagger, which leverages machine learning strategies on issue titles and descriptions for automatically labeling GitHub issues. Ticket Tagger automatically predicts the labels to assign to issues, with the aim of stimulating the use of labeling mechanisms in software projects, this to facilitate the issue management and prioritization processes. Along with the presentation of the tool's architecture and usage, we also evaluate its effectiveness in performing the issue labeling/classification process, which is critical to help maintainers to keep control of their workloads by focusing on the most critical issue tickets.de_CH
dc.language.isoende_CH
dc.publisherIEEEde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleTicket tagger : machine learning driven issue classificationde_CH
dc.typeKonferenz: Paperde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Informatik (InIT)de_CH
dc.identifier.doi10.1109/ICSME.2019.00070de_CH
dc.identifier.doi10.21256/zhaw-19309-
zhaw.conference.details2019 IEEE International Conference on Software Maintenance and Evolution (ICSME), Cleveland, OH, USA, 29 Sept.-4 Oct. 2019de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end409de_CH
zhaw.pages.start406de_CH
zhaw.publication.statusacceptedVersionde_CH
zhaw.publication.reviewPeer review (Abstract)de_CH
zhaw.title.proceedings2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)de_CH
zhaw.webfeedService Engineeringde_CH
zhaw.author.additionalNode_CH
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Kallis, R., Di Sorbo, A., Canfora, G., & Panichella, S. (2019). Ticket tagger : machine learning driven issue classification [Conference paper]. 2019 IEEE International Conference on Software Maintenance and Evolution (ICSME), 406–409. https://doi.org/10.1109/ICSME.2019.00070
Kallis, R. et al. (2019) ‘Ticket tagger : machine learning driven issue classification’, in 2019 IEEE International Conference on Software Maintenance and Evolution (ICSME). IEEE, pp. 406–409. Available at: https://doi.org/10.1109/ICSME.2019.00070.
R. Kallis, A. Di Sorbo, G. Canfora, and S. Panichella, “Ticket tagger : machine learning driven issue classification,” in 2019 IEEE International Conference on Software Maintenance and Evolution (ICSME), 2019, pp. 406–409. doi: 10.1109/ICSME.2019.00070.
KALLIS, Rafael, Andrea DI SORBO, Gerardo CANFORA und Sebastiano PANICHELLA, 2019. Ticket tagger : machine learning driven issue classification. In: 2019 IEEE International Conference on Software Maintenance and Evolution (ICSME). Conference paper. IEEE. 2019. S. 406–409. ISBN 978-1-7281-3094-1
Kallis, Rafael, Andrea Di Sorbo, Gerardo Canfora, and Sebastiano Panichella. 2019. “Ticket Tagger : Machine Learning Driven Issue Classification.” Conference paper. In 2019 IEEE International Conference on Software Maintenance and Evolution (ICSME), 406–9. IEEE. https://doi.org/10.1109/ICSME.2019.00070.
Kallis, Rafael, et al. “Ticket Tagger : Machine Learning Driven Issue Classification.” 2019 IEEE International Conference on Software Maintenance and Evolution (ICSME), IEEE, 2019, pp. 406–9, https://doi.org/10.1109/ICSME.2019.00070.


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