D 2016

Automated Bug Triaging in an Industrial Context

DEDÍK, Václav and Bruno ROSSI

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

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

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

DOI

UT WoS

000386649000052

Keywords in English

Software Bug Triaging; Bug Reports; Bug Assignment;Machine Learning; Text Classification; Industrial Scale

Tags

Tags

International impact, Reviewed
Změněno: 20/11/2019 10:03, Bruno Rossi, PhD

Abstract

V originále

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.