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Toward applying agglomerative hierarchical clustering in improving the software development effort estimation

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dc.title Toward applying agglomerative hierarchical clustering in improving the software development effort estimation en
dc.contributor.author Vo Van, Hai
dc.contributor.author Ho, Le Thi Kim Nhung
dc.contributor.author Jašek, Roman
dc.relation.ispartof Lecture Notes in Networks and Systems
dc.identifier.issn 2367-3370 Scopus Sources, Sherpa/RoMEO, JCR
dc.identifier.issn 2367-3389 Scopus Sources, Sherpa/RoMEO, JCR
dc.identifier.isbn 978-3-03-109069-1
dc.date.issued 2022
utb.relation.volume 501
dc.citation.spage 353
dc.citation.epage 371
dc.event.title 11th Computer Science On-line Conference, CSOC 2022
dc.event.location online
dc.event.sdate 2022-04-26
dc.event.edate 2022-04-26
dc.type conferenceObject
dc.language.iso en
dc.publisher Springer Science and Business Media Deutschland GmbH
dc.identifier.doi 10.1007/978-3-031-09070-7_30
dc.relation.uri https://link.springer.com/chapter/10.1007/978-3-031-09070-7_30
dc.subject software effort estimation en
dc.subject function point analysis en
dc.subject hierarchical clustering en
dc.subject machine learning en
dc.description.abstract Background: There are many studies on the effect of data clustering on the effort estimation process. Most of them are on partitioning and density-based clustering, and some use hierarchical clustering but fewer details on the linkage methods. Aim: we concentrate on the aspect of the agglomerative hierarchical clustering algorithm's effectiveness on the accuracy of the effort estimation. Method: We used the agglomerative hierarchical clustering algorithm to group the data into clusters then performed the IFPUG FPA method for effort estimation. The ISBSG dataset was used in this study. The number of clusters is determined using the dendrogram's cut points. Different cut points and linkage methods were employed to cluster the dataset for the comparison. The estimated results of these clusters were compared with the result from the whole dataset without clustering. Result: with the selected number of clusters, results are consistently better than without clustering with all selected evaluation criteria. Conclusion: the accuracy of the effort estimation can be significantly improved when using agglomerative hierarchical clustering. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1011085
utb.identifier.obdid 43884097
utb.identifier.scopus 2-s2.0-85135034057
utb.identifier.wok 000893645700030
utb.source d-scopus
dc.date.accessioned 2022-08-17T13:17:25Z
dc.date.available 2022-08-17T13:17:25Z
dc.description.sponsorship Faculty of Applied Informatics, Tomas Bata University in Zlin [IGA/CebiaTech/2022/001]
utb.ou Department of Computer and Communication Systems
utb.contributor.internalauthor Vo Van, Hai
utb.contributor.internalauthor Ho, Le Thi Kim Nhung
utb.contributor.internalauthor Jašek, Roman
utb.fulltext.sponsorship This work was supported by the Faculty of Applied Informatics, Tomas Bata University in Zlin, under project IGA/CebiaTech/2022/001.
utb.wos.affiliation [Vo Van Hai; Ho Le Le Le Nhung; Jasek, Roman] Tomas Bata Univ Zlin, Dept Comp & Commun Syst, Nam TGM 5555, Zlin 76001, Czech Republic
utb.scopus.affiliation Department of Computer and Communication Systems, Tomas Bata University in Zlin, Nam. TGM 5555, Zlin, 76001, Czech Republic
utb.fulltext.projects IGA/CebiaTech/2022/001
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