On Fusing Artificial and Convolutional Neural Network Features for Automatic Bug Assignments

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aut.relation.endpage1
aut.relation.issue99
aut.relation.journalIEEE Access
aut.relation.startpage1
aut.relation.volumePP
dc.contributor.authorDipongkor, Atish Kumar
dc.contributor.authorIslam, Md Saiful
dc.contributor.authorHussain, Ishtiaque
dc.contributor.authorYongchareon, Sira
dc.contributor.authorMistry, Sajib
dc.date.accessioned2023-09-05T04:07:22Z
dc.date.available2023-09-05T04:07:22Z
dc.date.issued2023-05-08
dc.description.abstractAutomated bug report assignment is critical for large-scale software projects where reported bugs are frequent and expert developers are required to fix them on time. Finding an appropriate developer with the necessary skill sets and prior experience in fixing similar bugs is difficult and can be an expensive process, depending on the severity of the reported bug. To address this issue, researchers have proposed several machine learning and deep learning-based automated bug report assignment techniques that make use of historical data on reported bugs as well as fixer information. However, there is still room for improvement in the performance of these techniques. In this paper, we propose a novel deep learning-based approach that utilizes two sets of features from the reported bugs’ textual data, namely contextual information and the occurrence of repeating keywords. We develop convolutional neural network and artificial neural network modules to mine these features. We then fuse these two sets of extracted features to assign a bug to an appropriate developer. We conduct extensive experiments on eight benchmark datasets of open-source, real-world software projects to assess the effectiveness of our approach. The experimental results demonstrate that our information fusion-based approach outperforms previous models and improves automated bug report assignment. Furthermore, we debug the errors of our proposed model and publish all source code so that future researchers can contribute to this problem.
dc.identifier.citationIEEE Access, ISSN: 2169-3536 (Print); 2169-3536 (Online), Institute of Electrical and Electronics Engineers (IEEE), PP(99), 1-1. doi: 10.1109/access.2023.3273595
dc.identifier.doi10.1109/access.2023.3273595
dc.identifier.issn2169-3536
dc.identifier.issn2169-3536
dc.identifier.urihttp://hdl.handle.net/10292/16649
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urihttps://ieeexplore.ieee.org/document/10121041
dc.rights.accessrightsOpenAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject46 Information and Computing Sciences
dc.subject4602 Artificial Intelligence
dc.subject4605 Data Management and Data Science
dc.subject4611 Machine Learning
dc.subject4612 Software Engineering
dc.subjectNeurosciences
dc.subject08 Information and Computing Sciences
dc.subject09 Engineering
dc.subject10 Technology
dc.subject40 Engineering
dc.subject46 Information and computing sciences
dc.titleOn Fusing Artificial and Convolutional Neural Network Features for Automatic Bug Assignments
dc.typeJournal Article
pubs.elements-id506393
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