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https://doi.org/10.21256/zhaw-19309| Publication type: | Conference paper |
| Type of review: | Peer review (abstract) |
| Title: | Ticket tagger : machine learning driven issue classification |
| Authors: | Kallis, Rafael Di Sorbo, Andrea Canfora, Gerardo Panichella, Sebastiano |
| et. al: | No |
| DOI: | 10.1109/ICSME.2019.00070 10.21256/zhaw-19309 |
| Proceedings: | 2019 IEEE International Conference on Software Maintenance and Evolution (ICSME) |
| Page(s): | 406 |
| Pages to: | 409 |
| Conference details: | 2019 IEEE International Conference on Software Maintenance and Evolution (ICSME), Cleveland, OH, USA, 29 Sept.-4 Oct. 2019 |
| Issue Date: | 2019 |
| Publisher / Ed. Institution: | IEEE |
| ISBN: | 978-1-7281-3094-1 |
| Language: | English |
| Subject (DDC): | 006: Special computer methods |
| Abstract: | Software 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. |
| Further 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. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/19309 |
| Fulltext version: | Accepted version |
| License (according to publishing contract): | Licence according to publishing contract |
| Departement: | School of Engineering |
| Organisational Unit: | Institute of Computer Science (InIT) |
| Appears in collections: | Publikationen School of Engineering |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2019_Kallis-etal_Ticket-Tagger-Tool-Demo_ICSME.pdf | Accepted Version | 308.63 kB | Adobe PDF |  View/Open |
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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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