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An evaluation of technical and environmental complexity factors for improving use case points estimation

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dc.title An evaluation of technical and environmental complexity factors for improving use case points estimation en
dc.contributor.author Ho, Le Thi Kim Nhung
dc.contributor.author Huynh Thai, Hoc
dc.contributor.author Vo Van, Hai
dc.relation.ispartof Advances in Intelligent Systems and Computing
dc.identifier.issn 2194-5357 Scopus Sources, Sherpa/RoMEO, JCR
dc.identifier.isbn 978-3-03-063321-9
dc.date.issued 2020
utb.relation.volume 1294
dc.citation.spage 757
dc.citation.epage 768
dc.event.title 4th Computational Methods in Systems and Software, CoMeSySo 2020
dc.event.location online
dc.event.sdate 2020-10-14
dc.event.edate 2020-10-17
dc.type conferenceObject
dc.language.iso en
dc.publisher Springer Science and Business Media Deutschland GmbH
dc.identifier.doi 10.1007/978-3-030-63322-6_64
dc.relation.uri https://link.springer.com/chapter/10.1007/978-3-030-63322-6_64
dc.subject multiple linear regression en
dc.subject software effort estimation en
dc.subject Use Case Points en
dc.description.abstract This paper presents a proposed method for improving the prediction ability of the Use Case Points method. Our main goal is to use the Least Absolute Shrinkage and Selection Operator Regression methods to find out which of the technical and environmental complexity factors significantly affect the accuracy of the Use Case Points method. Two regression models were used to calculate the selected significant variables. The results of several evaluation measures show that the proposed estimation method ability is better than the original Use Case Points method. The Sum of Squared Error of the proposed method is better than the results obtained by the original one. The study also enables project managers to understand how to assess the technical and environmental complexity factors better - since they do have an important impact on effort estimation. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1010144
utb.identifier.obdid 43882279
utb.identifier.scopus 2-s2.0-85098212049
utb.source d-scopus
dc.date.accessioned 2021-01-08T14:02:34Z
dc.date.available 2021-01-08T14:02:34Z
utb.contributor.internalauthor Ho, Le Thi Kim Nhung
utb.contributor.internalauthor Huynh Thai, Hoc
utb.contributor.internalauthor Vo Van, Hai
utb.fulltext.affiliation Ho Le Thi Kim Nhung, Huynh Thai Hoc, Vo Van Hai Faculty of Applied Informatics, Tomas Bata University in Zlin, Nad Stranemi 4511, 76001 Zlin, Czech Republic {lho,huynh_thai,vo_van}@utb.cz
utb.fulltext.dates -
utb.fulltext.sponsorship This work was supported by the Faculty of Applied Informatics, Tomas Bata University in Zlín, under Project SV13202001020-PU30, Project IGA/CebiaTech/2020/001, and Project RVO/FAI/2020/002.
utb.scopus.affiliation Faculty of Applied Informatics, Tomas Bata University in Zlin, Nad Stranemi 4511, Zlin, 76001, Czech Republic
utb.fulltext.projects SV13202001020-PU30
utb.fulltext.projects IGA/CebiaTech/2020/001
utb.fulltext.projects RVO/FAI/2020/002
utb.fulltext.faculty Faculty of Applied Informatics
utb.fulltext.faculty Faculty of Applied Informatics
utb.fulltext.faculty Faculty of Applied Informatics
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