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An HMM-based approach for automatic detection and classification of duplicate bug reports

Title:

An HMM-based approach for automatic detection and classification of duplicate bug reports

Ebrahimi, Neda, Trabelsi, Abdelaziz, Islam, Md. Shariful, Hamou-Lhadj, Abdelwahab and Khanmohammadi, Kobra (2019) An HMM-based approach for automatic detection and classification of duplicate bug reports. Information and Software Technology . ISSN 09505849 (In Press)

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Official URL: http://dx.doi.org/10.1016/j.infsof.2019.05.007

Abstract

Software projects rely on their issue tracking systems to guide maintenance activities of software developers. Bug reports submitted to the issue tracking systems carry crucial information about the nature of the crash (such as texts from users or developers and execution information about the running functions before the occurrence of a crash). Typically, big software projects receive thousands of reports every day.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Electrical and Computer Engineering
Item Type:Article
Refereed:Yes
Authors:Ebrahimi, Neda and Trabelsi, Abdelaziz and Islam, Md. Shariful and Hamou-Lhadj, Abdelwahab and Khanmohammadi, Kobra
Journal or Publication:Information and Software Technology
Date:14 May 2019
Funders:
  • Natural Sciences and Engineering Research Council of Canada (NSERC)
Digital Object Identifier (DOI):10.1016/j.infsof.2019.05.007
Keywords:Duplicate bug reports; Stack traces; Hidden Markov models; Machine learning; Mining software repositories
ID Code:985443
Deposited By: Monique Lane
Deposited On:03 Jun 2019 18:40
Last Modified:14 May 2021 01:00

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