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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 |