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
Proceedings: 2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)
Pages : 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) : 004: Computer science
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.
URI: https://digitalcollection.zhaw.ch/handle/11475/19309
Fulltext version : Updated version
License (according to publishing contract) : Licence according to publishing contract
Departement: School of Engineering
Organisational Unit: Institute of Applied Information Technology (InIT)
Appears in Collections:Publikationen School of Engineering

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