Using Tri-Relation Networks for Effective Software Fault-Proneness Prediction

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dc.contributor.ORCID0000-0002-1021-4753 (Wong, WE)
dc.contributor.VIAF65810187 (Wong, WE)
dc.contributor.authorLi, Y.
dc.contributor.authorWong, W. Eric
dc.contributor.authorLee, Shou-Yu
dc.contributor.authorWotawa, F.
dc.contributor.utdAuthorWong, W. Eric
dc.contributor.utdAuthorLee, Shou-Yu
dc.date.accessioned2020-06-02T18:04:42Z
dc.date.available2020-06-02T18:04:42Z
dc.date.issued2019-05-15
dc.description.abstractSoftware modules and developers are two core elements during the process of software development. Previous studies have shown that analyzing relations between 1) software modules; (2) developers; and (3) modules and developers, is critical to understand how they interact with each other, which ultimately affects software quality. Specifically, relations such as developer contribution relation, module dependency relation, and developer collaboration relation have been used independently or in pairs to build networks for software fault-proneness prediction. However, none of them investigate the joint effort of these three relations. Bearing this in mind, in this paper, we propose a tri-relation network, a weighted network that integrates developer contribution, module dependency, and developer collaboration relations to study their combined impact on software quality. Four network node centrality metrics are further derived from the proposed network to predict the fault-proneness of a given software module at the file level. Moreover, we have explored a mechanism to refine current networks in order to further improve the effectiveness of software fault-proneness prediction. © 2013 IEEE.
dc.description.departmentErik Jonsson School of Engineering and Computer Science
dc.identifier.bibliographicCitationLi, Y., W. Eric Wong, S. -Y Lee, and F. Wotawa. 2019. "Using Tri-Relation Networks for Effective Software Fault-Proneness Prediction." IEEE Access 7: 63066-63080, doi:10.1109/ACCESS.2019.2916615
dc.identifier.issn2169-3536
dc.identifier.urihttp://dx.doi.org/10.1109/ACCESS.2019.2916615
dc.identifier.urihttps://hdl.handle.net/10735.1/8662
dc.identifier.volume7
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.rightsCC BY 4.0 (Attribution)
dc.rights©2019 The Authors
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.source.journalIEEE Access
dc.subjectOnline social networks
dc.subjectComputer software--Design and construction
dc.titleUsing Tri-Relation Networks for Effective Software Fault-Proneness Prediction
dc.type.genrearticle

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