DEDÍK, Václav and Bruno ROSSI. Automated Bug Triaging in an Industrial Context. Online. In 42nd Euromicro Conference on Software Engineering and Advanced Applications (SEAA) 2016. Not specified: IEEE, 2016, p. 363-367. ISBN 978-1-5090-2819-1. Available from: https://dx.doi.org/10.1109/SEAA.2016.20.
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Basic information
Original name Automated Bug Triaging in an Industrial Context
Authors DEDÍK, Václav (203 Czech Republic, belonging to the institution) and Bruno ROSSI (380 Italy, guarantor, belonging to the institution).
Edition Not specified, 42nd Euromicro Conference on Software Engineering and Advanced Applications (SEAA) 2016, p. 363-367, 5 pp. 2016.
Publisher IEEE
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher United States of America
Confidentiality degree is not subject to a state or trade secret
Publication form electronic version available online
RIV identification code RIV/00216224:14330/16:00090406
Organization unit Faculty of Informatics
ISBN 978-1-5090-2819-1
ISSN 2376-9505
Doi http://dx.doi.org/10.1109/SEAA.2016.20
UT WoS 000386649000052
Keywords in English Software Bug Triaging; Bug Reports; Bug Assignment;Machine Learning; Text Classification; Industrial Scale
Tags firank_B
Tags International impact, Reviewed
Changed by Changed by: Bruno Rossi, PhD, učo 232464. Changed: 20/11/2019 10:03.
Abstract
There is an increasing need to introduce some form of automation within the bug triaging process, so that no time is wasted on the initial assignment of issues. However, there is a gap in current research, as most of the studies deal with open source projects, ignoring the industrial context and needs. In this paper, we report our experience in dealing with the automation of the bug triaging process within a research-industry cooperation. After reporting the requirements and needs that were set within the industrial project, we compare the analysis results with those from an open source project used frequently in related research (Firefox). In spite of the fact that the projects have different size and development process, the data distributions are similar and the best models as well. We found out that more easily configurable models (such as SVM+TF–IDF) are preferred, and that top-x recommendations, number of issues per developers, and online learning can all be relevant factors when dealing with an industrial collaboration.
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