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Propose-specific information related to prediction level at x and mean magnitude of relative error: A case study of software effort estimation

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dc.title Propose-specific information related to prediction level at x and mean magnitude of relative error: A case study of software effort estimation en
dc.contributor.author Huynh Thai, Hoc
dc.contributor.author Šilhavý, Petr
dc.contributor.author Fajkus, Martin
dc.contributor.author Prokopová, Zdenka
dc.contributor.author Šilhavý, Radek
dc.relation.ispartof Mathematics
dc.identifier.issn 2227-7390 Scopus Sources, Sherpa/RoMEO, JCR
dc.date.issued 2022
utb.relation.volume 10
utb.relation.issue 24
dc.type article
dc.language.iso en
dc.publisher MDPI
dc.identifier.doi 10.3390/math10244649
dc.relation.uri https://www.mdpi.com/2227-7390/10/24/4649
dc.subject mean magnitude of relative error en
dc.subject prediction level at x en
dc.subject sig en
dc.subject software effort estimation en
dc.description.abstract The prediction level at x (PRED(x)) and mean magnitude of relative error (MMRE) are measured based on the magnitude of relative error between real and predicted values. They are the standard metrics that evaluate accurate effort estimates. However, these values might not reveal the magnitude of over-/under-estimation. This study aims to define additional information associated with the PRED(x) and MMRE to help practitioners better interpret those values. We propose the formulas associated with the PRED(x) and MMRE to express the level of scatters of predictive values versus actual values on the left (sig(Left)), on the right (sig(Right)), and on the mean of the scatters (sig). We depict the benefit of the formulas with three use case points datasets. The proposed formulas might contribute to enriching the value of the PRED(x) and MMRE in validating the effort estimation. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1011341
utb.identifier.obdid 43884087
utb.identifier.scopus 2-s2.0-85144651757
utb.identifier.wok 000904479300001
utb.source j-scopus
dc.date.accessioned 2023-02-15T08:06:29Z
dc.date.available 2023-02-15T08:06:29Z
dc.description.sponsorship RVO/FAI/2021/002
dc.description.sponsorship Faculty of Applied Informatics, Tomas Bata University in Zlin; [RVO/FAI/2021/002]
dc.rights Attribution 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by/4.0/
dc.rights.access openAccess
utb.contributor.internalauthor Huynh Thai, Hoc
utb.contributor.internalauthor Šilhavý, Petr
utb.contributor.internalauthor Fajkus, Martin
utb.contributor.internalauthor Prokopová, Zdenka
utb.contributor.internalauthor Šilhavý, Radek
utb.fulltext.sponsorship This work was supported by the Faculty of Applied Informatics, Tomas Bata University in Zlin, under Project No. RVO/FAI/2021/002.
utb.wos.affiliation [Thai, Hoc Huynh; Silhavy, Petr; Fajkus, Martin; Prokopova, Zdenka; Silhavy, Radek] Tomas Bata Univ Zlin, Fac Appl Informat, Stranemi 4511, Zlin 76001, Czech Republic; [Thai, Hoc Huynh] Van Lang Univ, Fac Informat Technol, Sch Engn & Technol, Ho Chi Minh City 700000, Vietnam
utb.scopus.affiliation Faculty of Applied Informatics, Tomas Bata University in Zlin, Nad Stranemi 4511, Zlin, 76001, Czech Republic; Faculty of Information Technology, School of Engineering and Technology, Van Lang University, Ho Chi Minh City, 700000, Viet Nam
utb.fulltext.projects RVO/FAI/2021/002
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Attribution 4.0 International Kromě případů, kde je uvedeno jinak, licence tohoto záznamu je Attribution 4.0 International