A data-driven cost estimation model for agile development based on Kolmogorov-Arnold Networks and AdamW optimization

Over the past two decades, agile development has become a mainstream software engineering paradigm due to its flexibility and iterative nature. However, accurate cost estimation in agile projects remains challenging, mainly due to frequent requirement changes and data scarcity. Traditional estimatio...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Journal of King Saud University. Computer and information sciences Jg. 37; H. 5; S. 85 - 21
Hauptverfasser:	Zhao, Xiaoyan, Xiong, Xin, Mansor, Zulkefli, Razali, Rozilawati, Ahmad Nazri, Mohd Zakree, Li, Liangyu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cham Springer International Publishing 01.07.2025 Springer Nature B.V Springer
Schlagworte:	Accuracy Adaptive Moment Estimation with Weight Decay algorithm Agile development Algorithms Artificial intelligence Artificial neural networks Computer Imaging Computer Science Cost estimation Database Management Function points Kolmogorov-Arnold Networks Machine Learning Modelling Original Paper Pattern Recognition and Graphics Polynomials Research methodology Software development Software engineering Software Engineering/Programming and Operating Systems Stability Systems and Data Security Theory of Computation Vision Adaptive Moment Estimation with Weight Decay algorithm Cost estimation Kolmogorov-Arnold Networks Agile development
ISSN:	1319-1578, 2213-1248, 1319-1578
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Abstract	Over the past two decades, agile development has become a mainstream software engineering paradigm due to its flexibility and iterative nature. However, accurate cost estimation in agile projects remains challenging, mainly due to frequent requirement changes and data scarcity. Traditional estimation methods and existing machine learning models often fail to adapt effectively to the dynamic agile environment. To address these issues, this paper proposes a novel cost estimation model combining Kolmogorov-Arnold Networks (KAN) with the AdamW optimizer. KAN captures complex nonlinear relationships through hierarchical polynomial mapping, making it suitable for modeling dynamic cost variations. AdamW improves convergence speed and stability with adaptive learning rates and momentum mechanisms. To alleviate data scarcity, the SMOTE-NC technique is applied to generate 1000 synthetic samples based on 75 actual agile project data. K-fold cross-validation is used to enhance the model’s generalization ability. Experimental results demonstrate that the proposed KAN-AdamW model achieves superior performance, with a Mean Absolute Error (MAE) of 11,504.08 and a Mean Relative Error (MRE) of 0.12-outperforming traditional Artificial Neural Networks (ANN) and function point-based models. The model also shows strong performance in accuracy and R-squared metrics, indicating high predictive stability and precision. This study offers a data-driven and effective solution for agile cost estimation and provides empirical support for addressing data limitations using SMOTE-NC. Furthermore, it highlights the potential of KAN for broader applications in cost modeling.
AbstractList	Over the past two decades, agile development has become a mainstream software engineering paradigm due to its flexibility and iterative nature. However, accurate cost estimation in agile projects remains challenging, mainly due to frequent requirement changes and data scarcity. Traditional estimation methods and existing machine learning models often fail to adapt effectively to the dynamic agile environment. To address these issues, this paper proposes a novel cost estimation model combining Kolmogorov-Arnold Networks (KAN) with the AdamW optimizer. KAN captures complex nonlinear relationships through hierarchical polynomial mapping, making it suitable for modeling dynamic cost variations. AdamW improves convergence speed and stability with adaptive learning rates and momentum mechanisms. To alleviate data scarcity, the SMOTE-NC technique is applied to generate 1000 synthetic samples based on 75 actual agile project data. K-fold cross-validation is used to enhance the model’s generalization ability. Experimental results demonstrate that the proposed KAN-AdamW model achieves superior performance, with a Mean Absolute Error (MAE) of 11,504.08 and a Mean Relative Error (MRE) of 0.12-outperforming traditional Artificial Neural Networks (ANN) and function point-based models. The model also shows strong performance in accuracy and R-squared metrics, indicating high predictive stability and precision. This study offers a data-driven and effective solution for agile cost estimation and provides empirical support for addressing data limitations using SMOTE-NC. Furthermore, it highlights the potential of KAN for broader applications in cost modeling. Abstract Over the past two decades, agile development has become a mainstream software engineering paradigm due to its flexibility and iterative nature. However, accurate cost estimation in agile projects remains challenging, mainly due to frequent requirement changes and data scarcity. Traditional estimation methods and existing machine learning models often fail to adapt effectively to the dynamic agile environment. To address these issues, this paper proposes a novel cost estimation model combining Kolmogorov-Arnold Networks (KAN) with the AdamW optimizer. KAN captures complex nonlinear relationships through hierarchical polynomial mapping, making it suitable for modeling dynamic cost variations. AdamW improves convergence speed and stability with adaptive learning rates and momentum mechanisms. To alleviate data scarcity, the SMOTE-NC technique is applied to generate 1000 synthetic samples based on 75 actual agile project data. K-fold cross-validation is used to enhance the model’s generalization ability. Experimental results demonstrate that the proposed KAN-AdamW model achieves superior performance, with a Mean Absolute Error (MAE) of 11,504.08 and a Mean Relative Error (MRE) of 0.12-outperforming traditional Artificial Neural Networks (ANN) and function point-based models. The model also shows strong performance in accuracy and R-squared metrics, indicating high predictive stability and precision. This study offers a data-driven and effective solution for agile cost estimation and provides empirical support for addressing data limitations using SMOTE-NC. Furthermore, it highlights the potential of KAN for broader applications in cost modeling.
ArticleNumber	85
Author	Mansor, Zulkefli Razali, Rozilawati Xiong, Xin Ahmad Nazri, Mohd Zakree Li, Liangyu Zhao, Xiaoyan
Author_xml	– sequence: 1 givenname: Xiaoyan orcidid: 0009-0008-8959-6301 surname: Zhao fullname: Zhao, Xiaoyan email: p125511@siswa.ukm.edu.my organization: Faculty of Information Science & Technology, Universiti Kebangsaan Malaysia – sequence: 2 givenname: Xin surname: Xiong fullname: Xiong, Xin organization: Faculty of Information Science & Technology, Universiti Kebangsaan Malaysia – sequence: 3 givenname: Zulkefli surname: Mansor fullname: Mansor, Zulkefli organization: Center for Software Technology and Management, Universiti Kebangsaan Malaysia – sequence: 4 givenname: Rozilawati surname: Razali fullname: Razali, Rozilawati organization: Center for Software Technology and Management, Universiti Kebangsaan Malaysia – sequence: 5 givenname: Mohd Zakree surname: Ahmad Nazri fullname: Ahmad Nazri, Mohd Zakree organization: Center for Artificial Intelligence Technology, Universiti Kebangsaan Malaysia – sequence: 6 givenname: Liangyu surname: Li fullname: Li, Liangyu email: p129763@siswa.ukm.edu.my organization: Faculty of Information Science & Technology, Universiti Kebangsaan Malaysia
BookMark	eNp9kUuPFCEUhYkZE9tx_oArEtcoz4JadiY-Jk50o3FJqOLSqbaK20JNG_31YpfRnYRAcnPOd7mcp-QqYwZCngv-UnBuX1XdlmJcGsY5N47ZR2QnpVBMSO2uyE4o0TNhrHtCbmo9NpGwndGq25F1T2NYA4tlOkOmI9aVQl2nJawTZrpghJkmLDQcphlohDPMeFogr3QIFSJtovc4L3jAgme2LxnnSD_A-h3L10pDjnQfw_KF4qlBp58X7DPyOIW5ws2f-5p8fvP60-07dv_x7d3t_p6NysmVwQhDF1Uwwdi2O0jJxUEp53RsJ_S9TtoEbUPvrEu9MmaIziYFobNBGHVN7jZuxHD0p9KmKj88hslfClgOPpR1GmfwKvIk9SD1aKQWfSNGaXm0sqH6PqnGerGxTgW_PbQv8kd8KLk93ytpLNeGG9FUclONBWstkP52Fdz_DstvYfkWlr-E5W0zqc1UmzgfoPxD_8f1C4dJmVk
Cites_doi	10.1145/3663365 10.1109/ICRAIE51050.2020.9358309 10.30598/barekengvol17iss4pp2263-2272 10.1109/ACCESS.2023.3312716 10.1109/TPEL.2024.3486048 10.47836/pjst.29.2.08 10.5120/ijca2022922238 10.1109/MHS.1995.494215 10.1016/j.gsf.2024.101815 10.1016/j.advengsoft.2016.01.008 10.1109/DASA53625.2021.9682239 10.1007/s00500-022-07352-w 10.1007/978-3-642-12538-6_6 10.1016/j.radphyschem.2022.110232 10.1109/GCWkshp64532.2024.11100692 10.1016/j.neunet.2021.01.020 10.9734/ajrcos/2024/v17i5436 10.1109/ICASSP49660.2025.10890669 10.1109/ACCESS.2024.3407546 10.1016/j.advengsoft.2022.103329 10.1002/smr.2539 10.1016/j.advengsoft.2022.103159 10.3390/math11061477 10.1016/j.advengsoft.2013.12.007 10.1137/0202009 10.1007/s00521-023-09000-2 10.1007/s10462-021-10132-x 10.1109/TSE.2021.3080666 10.11591/ijai.v11.i1.pp265-275 10.1145/3743128 10.1023/A:1008202821328 10.1007/s41870-021-00669-z 10.1016/j.energy.2024.133417 10.1007/s11334-021-00420-8 10.1109/TPAMI.2024.3382294
ContentType	Journal Article
Copyright	The Author(s) 2025 The Author(s) 2025. This work is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml	– notice: The Author(s) 2025 – notice: The Author(s) 2025. This work is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
DBID	C6C AAYXX CITATION 8FE 8FG ABUWG AFKRA ARAPS AZQEC BENPR BGLVJ CCPQU DWQXO GNUQQ HCIFZ JQ2 K7- P62 PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS DOA
DOI	10.1007/s44443-025-00058-7
DatabaseName	Springer Nature OA Free Journals CrossRef ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials ProQuest Central Technology collection ProQuest One Community College ProQuest Central ProQuest Central Student SciTech Premium Collection ProQuest Computer Science Collection Computer Science Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic ProQuest Publicly Available Content ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China DOAJ Open Access Full Text
DatabaseTitle	CrossRef Publicly Available Content Database Advanced Technologies & Aerospace Collection Computer Science Database ProQuest Central Student Technology Collection ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection ProQuest One Academic Eastern Edition ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Technology Collection ProQuest SciTech Collection ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest One Academic UKI Edition ProQuest Central Korea ProQuest Central (New) ProQuest One Academic ProQuest One Academic (New)
DatabaseTitleList	Publicly Available Content Database
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Computer Science
EISSN	2213-1248 1319-1578
EndPage	21
ExternalDocumentID	oai_doaj_org_article_3d0f24b24c52419a98d270d72a6799f3 10_1007_s44443_025_00058_7
GrantInformation_xml	– fundername: Fundamental Research Grant Scheme (FRGS) grantid: FRGS/1/2022/ICT01/UKM/02/1
GroupedDBID	--K 0R~ 4.4 457 5VS AAEDT AAEDW AAIKJ AAJSJ AALRI AASML AAXUO AAYWO ABEEZ ABMAC ACGFS ACULB ADBBV ADEZE ADVLN AEXQZ AFGXO AFJKZ AFTJW AGHFR AITUG ALMA_UNASSIGNED_HOLDINGS AMRAJ APXCP BCNDV C6C EBS FDB GROUPED_DOAJ IXB KQ8 O-L O9- OK1 ROL SES SOJ SSZ XH2 AAQXK AAYXX ABWVN AFFHD AFKRA AGQPQ ARAPS ASPBG AVWKF AZFZN BENPR BGLVJ CCPQU CITATION EJD FEDTE FGOYB HCIFZ HVGLF HZ~ IPNFZ K7- M41 PHGZM PHGZT PIMPY PQGLB R2- RIG 8FE 8FG ABUWG AZQEC DWQXO GNUQQ JQ2 P62 PKEHL PQEST PQQKQ PQUKI PRINS
ID	FETCH-LOGICAL-c382t-eceb6d3a5a57a576eff8db33884d338e994f45a47a9878f9355bd87f3ea67a153
IEDL.DBID	K7-
ISICitedReferencesCount	0
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001513913900004&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1319-1578
IngestDate	Mon Nov 03 22:04:11 EST 2025 Sat Nov 29 14:36:13 EST 2025 Sat Nov 29 07:36:38 EST 2025 Tue Jul 29 01:10:27 EDT 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	5
Keywords	Adaptive Moment Estimation with Weight Decay algorithm Cost estimation Kolmogorov-Arnold Networks Agile development
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c382t-eceb6d3a5a57a576eff8db33884d338e994f45a47a9878f9355bd87f3ea67a153
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0009-0008-8959-6301
OpenAccessLink	https://www.proquest.com/docview/3257045051?pq-origsite=%requestingapplication%
PQID	3257045051
PQPubID	7424686
PageCount	21
ParticipantIDs	doaj_primary_oai_doaj_org_article_3d0f24b24c52419a98d270d72a6799f3 proquest_journals_3257045051 crossref_primary_10_1007_s44443_025_00058_7 springer_journals_10_1007_s44443_025_00058_7
PublicationCentury	2000
PublicationDate	2025-07-01
PublicationDateYYYYMMDD	2025-07-01
PublicationDate_xml	– month: 07 year: 2025 text: 2025-07-01 day: 01
PublicationDecade	2020
PublicationPlace	Cham
PublicationPlace_xml	– name: Cham – name: Amsterdam
PublicationTitle	Journal of King Saud University. Computer and information sciences
PublicationTitleAbbrev	J. King Saud Univ. Comput. Inf. Sci
PublicationYear	2025
Publisher	Springer International Publishing Springer Nature B.V Springer
Publisher_xml	– name: Springer International Publishing – name: Springer Nature B.V – name: Springer
References	G Jiang (58_CR18) 2022; 16 MA Ramessur (58_CR28) 2021; 13 W Rosa (58_CR31) 2021; 48 JH Holland (58_CR16) 1973; 2 F Sofia (58_CR35) 2024; 4 Q Bushra (58_CR5) 2021; 9 CH Rashid (58_CR29) 2023; 11 FH Alshammari (58_CR3) 2022; 26 S Mirjalili (58_CR24) 2014; 69 58_CR25 S-H Johannes (58_CR19) 2021; 137 58_CR26 J Pasuksmit (58_CR27) 2024; 56 58_CR41 58_CR20 58_CR42 58_CR21 SG Tetteh (58_CR40) 2024; 17 58_CR43 58_CR22 58_CR44 58_CR38 58_CR39 S Mirjalili (58_CR23) 2016; 95 E Rodríguez Sánchez (58_CR30) 2023; 11 A Javed (58_CR17) 2024; 12 58_CR1 SA Butt (58_CR7) 2023; 175 58_CR2 58_CR12 58_CR34 58_CR6 Z Chen (58_CR9) 2024; 40 M Fadzil (58_CR13) 2022; 200 58_CR14 58_CR36 58_CR4 58_CR37 P Zhou (58_CR45) 2024; 46 58_CR10 58_CR32 58_CR8 58_CR11 AA Hassan (58_CR15) 2023; 35 58_CR33
References_xml	– ident: 58_CR4 – volume: 56 start-page: 1 year: 2024 ident: 58_CR27 publication-title: ACM Comput Surv doi: 10.1145/3663365 – ident: 58_CR33 doi: 10.1109/ICRAIE51050.2020.9358309 – ident: 58_CR21 – ident: 58_CR42 doi: 10.30598/barekengvol17iss4pp2263-2272 – volume: 11 start-page: 99268 year: 2023 ident: 58_CR29 publication-title: IEEE Access. doi: 10.1109/ACCESS.2023.3312716 – ident: 58_CR8 – volume: 40 start-page: 4602 issue: 3 year: 2024 ident: 58_CR9 publication-title: IEEE Trans Power Electron doi: 10.1109/TPEL.2024.3486048 – volume: 9 start-page: 1467 year: 2021 ident: 58_CR5 publication-title: Inf Technol Indust – ident: 58_CR26 doi: 10.47836/pjst.29.2.08 – ident: 58_CR39 doi: 10.5120/ijca2022922238 – ident: 58_CR10 – ident: 58_CR12 doi: 10.1109/MHS.1995.494215 – ident: 58_CR11 doi: 10.1016/j.gsf.2024.101815 – volume: 95 start-page: 51 year: 2016 ident: 58_CR23 publication-title: Adv Eng Softw doi: 10.1016/j.advengsoft.2016.01.008 – ident: 58_CR14 doi: 10.1109/DASA53625.2021.9682239 – volume: 26 start-page: 10993 year: 2022 ident: 58_CR3 publication-title: Soft Comput doi: 10.1007/s00500-022-07352-w – ident: 58_CR44 doi: 10.1007/978-3-642-12538-6_6 – volume: 200 year: 2022 ident: 58_CR13 publication-title: Radiation Phys Chem doi: 10.1016/j.radphyschem.2022.110232 – ident: 58_CR43 doi: 10.1109/GCWkshp64532.2024.11100692 – volume: 137 start-page: 119 year: 2021 ident: 58_CR19 publication-title: Neural Netw doi: 10.1016/j.neunet.2021.01.020 – volume: 17 start-page: 30 issue: 5 year: 2024 ident: 58_CR40 publication-title: Asian J Res Comput Sci doi: 10.9734/ajrcos/2024/v17i5436 – ident: 58_CR34 doi: 10.1109/ICASSP49660.2025.10890669 – ident: 58_CR22 – volume: 16 start-page: 1 issue: 2 year: 2022 ident: 58_CR18 publication-title: Int J Inf Technol Syst Approach (IJITSA) – volume: 12 start-page: 79138 year: 2024 ident: 58_CR17 publication-title: IEEE Access doi: 10.1109/ACCESS.2024.3407546 – volume: 175 year: 2023 ident: 58_CR7 publication-title: Adv Eng Softw doi: 10.1016/j.advengsoft.2022.103329 – ident: 58_CR1 doi: 10.1002/smr.2539 – ident: 58_CR6 doi: 10.1016/j.advengsoft.2022.103159 – volume: 11 start-page: 1477 year: 2023 ident: 58_CR30 publication-title: Mathematics doi: 10.3390/math11061477 – volume: 69 start-page: 46 year: 2014 ident: 58_CR24 publication-title: Grey wolf optimizer. Adv Eng Softw doi: 10.1016/j.advengsoft.2013.12.007 – volume: 2 start-page: 88 issue: 2 year: 1973 ident: 58_CR16 publication-title: SIAM J Comput doi: 10.1137/0202009 – volume: 35 start-page: 24069 issue: 34 year: 2023 ident: 58_CR15 publication-title: Neural Comput Appl doi: 10.1007/s00521-023-09000-2 – ident: 58_CR41 – ident: 58_CR20 – ident: 58_CR2 doi: 10.1007/s10462-021-10132-x – volume: 4 start-page: 73 issue: 1 year: 2024 ident: 58_CR35 publication-title: J Artif Intell Res Appl – volume: 48 start-page: 3117 issue: 8 year: 2021 ident: 58_CR31 publication-title: IEEE Trans Softw Eng doi: 10.1109/TSE.2021.3080666 – ident: 58_CR25 doi: 10.11591/ijai.v11.i1.pp265-275 – ident: 58_CR36 doi: 10.1145/3743128 – ident: 58_CR37 doi: 10.1023/A:1008202821328 – volume: 13 start-page: 1101 year: 2021 ident: 58_CR28 publication-title: Int J Inf Technol doi: 10.1007/s41870-021-00669-z – ident: 58_CR38 doi: 10.1016/j.energy.2024.133417 – ident: 58_CR32 doi: 10.1007/s11334-021-00420-8 – volume: 46 start-page: 6486 issue: 9 year: 2024 ident: 58_CR45 publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2024.3382294
SSID	ssj0001765436
Score	2.3097785
Snippet	Over the past two decades, agile development has become a mainstream software engineering paradigm due to its flexibility and iterative nature. However,... Abstract Over the past two decades, agile development has become a mainstream software engineering paradigm due to its flexibility and iterative nature....
SourceID	doaj proquest crossref springer
SourceType	Open Website Aggregation Database Index Database Publisher
StartPage	85
SubjectTerms	Accuracy Adaptive Moment Estimation with Weight Decay algorithm Agile development Algorithms Artificial intelligence Artificial neural networks Computer Imaging Computer Science Cost estimation Database Management Function points Kolmogorov-Arnold Networks Machine Learning Modelling Original Paper Pattern Recognition and Graphics Polynomials Research methodology Software development Software engineering Software Engineering/Programming and Operating Systems Stability Systems and Data Security Theory of Computation Vision
SummonAdditionalLinks	– databaseName: DOAJ Open Access Full Text dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3BatwwEBUh9JBL2jQN3XYTdOgtFV3bkiUdt6VLIGXpIaF7E7JGCoWsXWw339-R7N3NBkIuNcZgWwcxM54ZWfPeEPKJg85sNnMsVIIzvPNMlxLYzDoXSgHBDuz6P-RyqVYr_fNRq69YEzbQAw-C-1LALOS8yrkTGGy01QpyOQOZ21JqHRLPJ2Y9jxZT6e-KjJjJBC2KKJ0M7XJEzCTcHMcjbl-KCKoWism9qJTI-_cyziebpCn2LN6Q4zFppPNhsifkwNdvyetNQwY6fp-npJ_TWPHJoI0-jLqm62lk0RjgiTR1vaGYpVJ7h86Awq5giMZoBhQHXTf36-auaZsHNm_r5h7ocqgU76itgc7Brn_RBh3NekRwviO3i-83367Y2FaBuULlPfPOVyUUVlgh8Sx9CAoqXKoqDnj1WvPAheUSZS1ViATsFSgZCo8it-ghz8hh3dT-PaGlDryQWSg9VzzIspKgMmudt15UWSYn5HIjVvNnYM8wW57kpASDSjBJCQZHf42S346MzNfpAdqDGe3BvGQPEzLd6M2Mn2Nnitirj2Oyl03I540ud6-fn9KH_zGlj-QoT7YWi3yn5LBv__pz8so99L-79iIZ7j_z5e46 priority: 102 providerName: Directory of Open Access Journals
Title	A data-driven cost estimation model for agile development based on Kolmogorov-Arnold Networks and AdamW optimization
URI	https://link.springer.com/article/10.1007/s44443-025-00058-7 https://www.proquest.com/docview/3257045051 https://doaj.org/article/3d0f24b24c52419a98d270d72a6799f3
Volume	37
WOSCitedRecordID	wos001513913900004&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2213-1248 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001765436 issn: 1319-1578 databaseCode: DOA dateStart: 20250101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVPQU databaseName: Computer Science Database customDbUrl: eissn: 2213-1248 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001765436 issn: 1319-1578 databaseCode: K7- dateStart: 20250301 isFulltext: true titleUrlDefault: http://search.proquest.com/compscijour providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2213-1248 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001765436 issn: 1319-1578 databaseCode: BENPR dateStart: 20250301 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2213-1248 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001765436 issn: 1319-1578 databaseCode: PIMPY dateStart: 20250301 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwELag5cCF8hRbysoHbmCxTvw8oS1qBSqsVghEOVmOHyukblyStL8f23G6KhJciKJIcaxopBnPjMcz3wDwiliJNV4Y5BtKUHxzSDJu0UIb4xm1Xo_o-p_4aiXOz-W6BNz6klY56cSsqG0wKUb-tk7t1ki01_jd5S-Uukal09XSQuMu2MdVhZOcn3G0i7HwVDnJSq1Mrpgj8UoHlzSVU1OB-C17lGH7b_mafxyPZqtzevC_9D4ED4q_CZejgDwCd1z7GBxMvRxgWdpPwLCEKVkU2S6pP2hCP8AEwDFWNsLcMAdGBxfqTdQj0O5yjWAyhBbGSWfhYhs2oQvXaNm14cLC1Zhk3kPdWri0evsdhqijtqX48yn4dnry9f0HVDoyIFOLakDOuIbZWlNNebyZ817YJu5yBbHx6aQknlBNuJaCC5-w2xsruK-dZlxH5foM7LWhdc8BZNKTmmPPHBHEc9ZwK7DWxmlHG4z5DLye-KIuR-ANdQOxnLmoIhdV5qKKs48T625mJtDsPBC6jSprUNV24SvSVMTQ6LfISKOt-MLyKhInpa9n4GhipSoruVc7Ps7Am0kYdp__TtLhv__2AtyvshimzN8jsDd0V-4luGeuh599Nwf7xyer9Zd5DhHMs1THsfXHz-sfvwHe7_9G
linkProvider	ProQuest
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB5VWyS4UJ5ioYAPcAKLTeLEzgGh5VF1tdvVHoooJ-P4sULqxiUJRfwpfiPjPLoqEtx6IIoiJbEiJ_n8eWzPzAfwjJk8UtFEU1ekjOKZpXnGDZ0orV2WGqe67PoLvlyKk5N8tQO_hliY4FY5cGJL1MbrMEf-Kglyawz76-jN2TcaVKPC6uogodHBYm5__sAhW_169h7_7_M4Pvhw_O6Q9qoCVCcibqjVtshMolKVctwz65wwBY7UBDN4tHnOHEsV4wqH48KF_OOFEdwlVmVcRUElAil_lyHYxQh2V7Oj1eftrA4PsZpZH53Txugx3MJSaRoCuFNB-aUesBUKuGTd_rEg2_ZzB3v_2xe6BTd7i5pMuyZwG3ZseQf2BrUK0pPXXWimJLjDUlMFgifa1w0JKUa62E3SSgIRNOGJWiNTErP1piKhqzcEC8396cavfeXP6bQq_akhy86NviaqNGRq1OYT8cjCmz689R58vJJ3vw-j0pf2AZAsdyzhkcssE8zxrOBGREppq2xaRBEfw4sBB_KsSy0iL5JIt6iRiBrZokZi6bcBKhclQ1rw9oKv1rJnGZmYiYtZETOdomWWYx1NzCeGx1i5PHfJGPYH6Mieq2q5xc0YXg7g297-e5Ue_vtpT-H64fHRQi5my_kjuBG3TSD4Oe_DqKm-28dwTZ83X-vqSd-KCHy5alj-BjDgWqs
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+data-driven+cost+estimation+model+for+agile+development+based+on+Kolmogorov-Arnold+Networks+and+AdamW+optimization&rft.jtitle=Journal+of+King+Saud+University.+Computer+and+information+sciences&rft.au=Zhao%2C+Xiaoyan&rft.au=Xiong%2C+Xin&rft.au=Mansor%2C+Zulkefli&rft.au=Razali%2C+Rozilawati&rft.date=2025-07-01&rft.pub=Springer+Nature+B.V&rft.eissn=1319-1578&rft.volume=37&rft.issue=5&rft.spage=85&rft_id=info:doi/10.1007%2Fs44443-025-00058-7
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1319-1578&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1319-1578&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1319-1578&client=summon