Automated Bug Triaging in an Industrial Context

DEDÍK, Václav a 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, s. 363-367. ISBN 978-1-5090-2819-1. Dostupné z: https://dx.doi.org/10.1109/SEAA.2016.20.

Základní údaje

Originální název

Automated Bug Triaging in an Industrial Context

Autoři

DEDÍK, Václav (203 Česká republika, domácí) a Bruno ROSSI (380 Itálie, garant, domácí)

Vydání

Not specified, 42nd Euromicro Conference on Software Engineering and Advanced Applications (SEAA) 2016, od s. 363-367, 5 s. 2016

Nakladatel

IEEE

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Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Kód RIV

RIV/00216224:14330/16:00090406

Organizační jednotka

Fakulta informatiky

ISBN

978-1-5090-2819-1

ISSN

DOI

http://dx.doi.org/10.1109/SEAA.2016.20

UT WoS

000386649000052

Klíčová slova anglicky

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

Štítky

firank_B

Příznaky

Mezinárodní význam, Recenzováno

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Anotace

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.