Qualitative Clustering of Software Repositories Based on Software Metrics

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dc.authoridFarina, Mirko/0000-0001-8342-6549
dc.authoridBugayenko, Yegor/0000-0001-6370-0678
dc.authoridDaniakin, Kirill/0000-0001-7834-7993
dc.authoridSucci, Giancarlo/0000-0001-8847-0186
dc.authorwosidFarina, Mirko/AAN-1331-2021
dc.authorwosidSucci, Giancarlo/E-4064-2016
dc.contributor.authorBugayenko, Yegor
dc.contributor.authorDaniakin, Kirill
dc.contributor.authorFarina, Mirko
dc.contributor.authorKholmatova, Zamira
dc.contributor.authorKruglov, Artem
dc.contributor.authorPedrycz, Witold
dc.contributor.authorSucci, Giancarlo
dc.date.accessioned2024-05-19T14:46:43Z
dc.date.available2024-05-19T14:46:43Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractSoftware repositories contain a wealth of information about the aspects related to software development process. For this reason, many studies analyze software repositories using methods of data analytics with a focus on clustering. Software repository clustering has been applied in studying software ecosystems such as GitHub, defect and technical debt prediction, software remodularization. Although some interesting insights have been reported, the considered studies exhibited some limitations. The limitations are associated with the use of individual clustering methods and manifesting in the shortcomings of the obtained results. In this study, to alleviate the existing limitations we engage multiple cluster validity indices applied to multiple clustering methods and carry out consensus clustering. To our knowledge, this study is the first to apply the consensus clustering approach to analyze software repositories and one of the few to apply the consensus clustering to software metrics. Intensive experimental studies are reported for software repository metrics data consisting of a number of software repositories each described by software metrics. We revealed seven clusters of software repositories and relate them to developers' activity. It is advocated that the proposed clustering environment could be useful for facilitating the decision making process for business investors and open-source community with the help of the Gartner's hype cycle.en_US
dc.description.sponsorshipHuawei Technologies under the Theoretically Objective Measurements of Software Development Projects(TOM)en_US
dc.description.sponsorshipThis work was supported by Huawei Technologies under the Theoretically Objective Measurements of Software Development Projects(TOM).en_US
dc.identifier.doi10.1109/ACCESS.2023.3244495
dc.identifier.endpage14727en_US
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-85148935041en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage14716en_US
dc.identifier.urihttps://doi.org10.1109/ACCESS.2023.3244495
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5580
dc.identifier.volume11en_US
dc.identifier.wosWOS:000936285700001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeee-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIeee Accessen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240519_kaen_US
dc.subjectSoftware Engineeringen_US
dc.subjectSoftware Development Managementen_US
dc.subjectSoftware Metricsen_US
dc.subjectClustering Methodsen_US
dc.subjectEmpirical Software Engineeringen_US
dc.subjectClusteringen_US
dc.subjectAnalysis Of Software Repositoriesen_US
dc.titleQualitative Clustering of Software Repositories Based on Software Metricsen_US
dc.typeArticleen_US

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