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Improving the performance of effort estimation in terms of function point analysis by balancing datasets

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dc.title Improving the performance of effort estimation in terms of function point analysis by balancing datasets en
dc.contributor.author Huynh Thai, Hoc
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.isbn 978-3-031-21434-9
dc.date.issued 2023
utb.relation.volume 596 LNNS
dc.citation.spage 705
dc.citation.epage 714
dc.event.title 6th Computational Methods in Systems and Software, CoMeSySo 2022
dc.event.location online
dc.event.sdate 2022-10-10
dc.event.edate 2022-10-15
dc.type conferenceObject
dc.language.iso en
dc.publisher Springer Science and Business Media Deutschland GmbH
dc.identifier.doi 10.1007/978-3-031-21435-6_60
dc.relation.uri https://link.springer.com/chapter/10.1007/978-3-031-21435-6_60
dc.subject Adj-Effort en
dc.subject balance class weight en
dc.subject deep learning en
dc.subject software effort estimation en
dc.description.abstract This research proposes an approach to improve the performance of effort estimation based on the balancing of each group for categorical variables. The proposed model is based on function point analysis, Industry Sector, and deep learning. The Pytorch library is used to build the deep learning model with the dataset ISBSG (release 2020). The accuracy of our model is compared with that of the Adj-Effort approach. We adopt the prediction level at 0.3, Mean Absolute Error, Mean Balanced Relative Error, Mean Inverted Balanced Relative Error, and Standardised Accuracy as criteria for validation. The findings demonstrate that our proposed model outweighs the unbalanced and Adj-Effort approaches. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. en
utb.faculty Faculty of Applied Informatics
utb.faculty Faculty of Management and Economics
utb.faculty Faculty of Humanities
dc.identifier.uri http://hdl.handle.net/10563/1011428
utb.identifier.obdid 43884989
utb.identifier.scopus 2-s2.0-85148048260
utb.source d-scopus
dc.date.accessioned 2023-03-15T07:46:33Z
dc.date.available 2023-03-15T07:46:33Z
dc.description.sponsorship IGA/CebiaTech/2022/001
utb.contributor.internalauthor Huynh Thai, Hoc
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 Zlín, under project IGA/CebiaTech/2022/001.
utb.scopus.affiliation Faculty of Applied Informatics, Tomas Bata University in Zlin, Nad Stranemi 4511, Zlin, 76001, Czech Republic
utb.fulltext.projects IGA/CebiaTech/2022/001
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