Effort Estimation in Software Development Projects Using Supervised Machine Learning Techniques

Autores:: Getial-Barragan, Jesus
Timaran-Pereira, Ricardo
Bastidas, David-Ramiro

Tipo de recurso:: Article of journal

Fecha de publicación:: 2025

Institución:: Universidad Pedagógica y Tecnológica de Colombia

Repositorio:: RiUPTC: Repositorio Institucional UPTC

Idioma:

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dc.title.en-US.fl_str_mv	Effort Estimation in Software Development Projects Using Supervised Machine Learning Techniques
title	Effort Estimation in Software Development Projects Using Supervised Machine Learning Techniques
spellingShingle	Effort Estimation in Software Development Projects Using Supervised Machine Learning Techniques Effort Estimation Machine Learning Random Forest Decision Tree Software Development Aprendizaje automático Árbol de decisión Bosque aleatorio Desarrollo de software Estimación de esfuerzo
title_short	Effort Estimation in Software Development Projects Using Supervised Machine Learning Techniques
title_full	Effort Estimation in Software Development Projects Using Supervised Machine Learning Techniques
title_fullStr	Effort Estimation in Software Development Projects Using Supervised Machine Learning Techniques
title_full_unstemmed	Effort Estimation in Software Development Projects Using Supervised Machine Learning Techniques
title_sort	Effort Estimation in Software Development Projects Using Supervised Machine Learning Techniques
dc.creator.fl_str_mv	Getial-Barragan, Jesus Timaran-Pereira, Ricardo Bastidas, David-Ramiro
dc.contributor.author.eng.fl_str_mv	Getial-Barragan, Jesus Timaran-Pereira, Ricardo Bastidas, David-Ramiro
dc.subject.none.fl_str_mv	Effort Estimation Machine Learning Random Forest Decision Tree Software Development Aprendizaje automático Árbol de decisión Bosque aleatorio Desarrollo de software Estimación de esfuerzo
topic	Effort Estimation Machine Learning Random Forest Decision Tree Software Development Aprendizaje automático Árbol de decisión Bosque aleatorio Desarrollo de software Estimación de esfuerzo
publishDate	2025
dc.date.accessioned.none.fl_str_mv	2025-10-17 19:50:06 2026-04-16T17:47:50Z
dc.date.available.none.fl_str_mv	2025-10-17 19:50:06
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dc.relation.references.none.fl_str_mv	V. Thakur, K. Dutta, “Machine learning based effort estimation models for software development projects related datasets with diverse features,” in Proceedings of the 2nd International Conference on Computational Intelligence, Communication Technology and Networks, Ghaziabad, India, 2025, pp. 807-813. https://doi.org/10.1109/CICTN64563.2025.10932601 X. Zhao, X. Xiong, Z. Mansor, R. Razali, M. Z. Ahmad Nazri, L. Li “A data-driven cost estimation model for agile development based on Kolmogorov-Arnold networks and AdamW optimization,” Journal of King Saud University – Computer and Information Sciences, vol. 37, e85, 2025. https://doi.org/10.1007/s44443-025-00058-7 J. A. Timana Peña, C. Piñeros Rodríguez, L. Sierra Martínez, D. Peluffo Ordóñez, “Effort estimation in agile software development: A systematic map study,” INGE CUC, vol. 19, no. 1, pp. 22-36, 2023. https://doi.org/10.17981/ingecuc.19.1.2023.03 M. Perkusich, L. C. e Silva, A. Costa, F. Ramos, R. Saraiva, A. Freire, et al., “Intelligent software engineering in the context of agile software development: A systematic literature review,” Information and Software Technology, vol. 119, e106241, Mar. 2020. https://doi.org/10.1016/j.infsof.2019.106241 M. Fernández-Diego, F. González-Ladrón-de-Guevara, “Potential and limitations of the ISBSG dataset in enhancing software engineering research: A mapping review,” Information and Software Technology, vol. 56, no. 6, pp. 527–544, Jun. 2014. https://doi.org/10.1016/j.infsof.2014.01.003 A. B. Nassif, M. Azzeh, L. F. Capretz, D. Ho, “A comparison between decision trees and decision tree forest models for software development effort estimation,” in Third International Conference on Communications and Information Technology (ICCIT), Beirut, Lebanon, 2013, pp. 220-224. https://doi.org/10.1109/ICCITechnology.2013.6579553 Ritu and P. Bhambri, “Enhancing software development effort estimation with a cloud-based data framework using use case points, fuzzy logic, and machine learning,” Discover Computing, vol. 28, e143, 2025. https://doi.org/10.1007/s10791-025-09668-1 A. Najm, A. Zakrani, A. Marzak, “Decision trees based software development effort estimation: A systematic mapping study,” in Proceedings of the International Conference on Computer Science and Renewable Energies, Agadir, Morocco, 2019, pp. 1–6. https://doi.org/10.1109/ICCSRE.2019.8807544 M. Hosni, A. Idri, A. Abran, “Investigating heterogeneous ensembles with filter feature selection for software effort estimation,” in Proceedings of the ACM International Conference on Software Engineering and Knowledge Engineering, Pittsburgh, USA, Jul. 2017, pp. 207-220. https://doi.org/10.1145/3143434.3143456 I. A. Al-Naimy, M. A. Al-Jawaherry, “Software effort estimation using ensemble learning methods,” AIP Conference Proceedings, vol. 3264, no. 1, e040021, Mar. 2025. https://doi.org/10.1063/5.0259225 A. G. Priya Varshini, A. Kumari J. Ramprasath, S. Rishi R, S. Balakrishnan, D. Deepak, “Optimized Convolutional Neural Network Model for Software Effort Estimation,” in Proceedings of the 2024 Third International Conference on Smart Technologies in Systems and Networking Computing, Villupuram, India, 2024, pp. 1-6. https://doi.org/10.1109/ICSTSN61422.2024.10671053 A. Idri, I. Abnane, “Fuzzy analogy based effort estimation: An empirical comparative study,” in Proceedings of the IEEE International Conference on Computer and Information Technology, Helsinki, Finland, 2017, pp. 114-121. https://doi.org/10.1109/CIT.2017.29 P. Rai, D. K. Verma, S. Kumar, “A hybrid model for prediction of software effort based on team size,” IET Software, vol. 15, no. 6, pp. 546-556, Dec. 2021. https://doi.org/10.1049/sfw2.12048 A. Zakrani, M. Hain, A. Namir, “Software development effort estimation using random forests: An empirical study and evaluation,” International Journal of Intelligent Engineering and Systems, vol. 11, no. 6, pp. 300-311, Dec. 2018. https://doi.org/10.22266/ijies2018.1231.30 ISBSG, Academic research projects, 2023. https://www.isbsg.org A. M. Shimaoka, R. C. Ferreira, A. Goldman, “The evolution of CRISP-DM for Data Science: Methods, Processes and Frameworks,” SBC Reviews, vol. 4, no. 1, pp. 28-43, Oct. 2024. https://doi.org/10.5753/reviews.2024.3757 F. González-Ladrón-de-Guevara, M. Fernández-Diego, “ISBSG variables most frequently used for software effort estimation: A mapping review,” in Proceedings of the 8th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, Torino, Italy, Sep. 2014, pp. 1-4. https://doi.org/10.1145/2652524.2652550 B. Alsaadi, K. Saeedi, “Data-driven effort estimation techniques of agile user stories: A systematic literature review,” Artificial Intelligence Review, vol. 55, no. 7, pp. 5485-5516, Oct. 2022. https://doi.org/10.1007/s10462-021-10132-x
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dc.rights.none.fl_str_mv	Jesus Getial-Barragan, Ricardo Timaran-Pereira, David-Ramiro Bastidas - 2025
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spelling	Getial-Barragan, JesusTimaran-Pereira, RicardoBastidas, David-Ramiro2025-10-17 19:50:062026-04-16T17:47:50Z2025-10-17 19:50:062025-10-170121-1129https://repositorio.uptc.edu.co/handle/001/2161510.19053/uptc.01211129.v34.n73.2025.201942357-5328https://doi.org/10.19053/uptc.01211129.v34.n73.2025.20194application/pdftext/xmlUniversidad Pedagógica y Tecnológica de ColombiaJesus Getial-Barragan, Ricardo Timaran-Pereira, David-Ramiro Bastidas - 2025https://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessThis work is licensed under a Creative Commons Attribution 4.0 International License.http://purl.org/coar/access_right/c_abf2https://revistas.uptc.edu.co/index.php/ingenieria/article/view/20194Effort EstimationMachine LearningRandom ForestDecision TreeSoftware DevelopmentAprendizaje automáticoÁrbol de decisiónBosque aleatorioDesarrollo de softwareEstimación de esfuerzoEffort Estimation in Software Development Projects Using Supervised Machine Learning TechniquesArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1info:eu-repo/semantics/articleJournal articleinfo:eu-repo/semantics/publishedVersionTexthttp://purl.org/coar/version/c_970fb48d4fbd8a85V. Thakur, K. Dutta, “Machine learning based effort estimation models for software development projects related datasets with diverse features,” in Proceedings of the 2nd International Conference on Computational Intelligence, Communication Technology and Networks, Ghaziabad, India, 2025, pp. 807-813. https://doi.org/10.1109/CICTN64563.2025.10932601X. Zhao, X. Xiong, Z. Mansor, R. Razali, M. Z. Ahmad Nazri, L. Li “A data-driven cost estimation model for agile development based on Kolmogorov-Arnold networks and AdamW optimization,” Journal of King Saud University – Computer and Information Sciences, vol. 37, e85, 2025. https://doi.org/10.1007/s44443-025-00058-7J. A. Timana Peña, C. Piñeros Rodríguez, L. Sierra Martínez, D. Peluffo Ordóñez, “Effort estimation in agile software development: A systematic map study,” INGE CUC, vol. 19, no. 1, pp. 22-36, 2023. https://doi.org/10.17981/ingecuc.19.1.2023.03M. Perkusich, L. C. e Silva, A. Costa, F. Ramos, R. Saraiva, A. Freire, et al., “Intelligent software engineering in the context of agile software development: A systematic literature review,” Information and Software Technology, vol. 119, e106241, Mar. 2020. https://doi.org/10.1016/j.infsof.2019.106241M. Fernández-Diego, F. González-Ladrón-de-Guevara, “Potential and limitations of the ISBSG dataset in enhancing software engineering research: A mapping review,” Information and Software Technology, vol. 56, no. 6, pp. 527–544, Jun. 2014. https://doi.org/10.1016/j.infsof.2014.01.003A. B. Nassif, M. Azzeh, L. F. Capretz, D. Ho, “A comparison between decision trees and decision tree forest models for software development effort estimation,” in Third International Conference on Communications and Information Technology (ICCIT), Beirut, Lebanon, 2013, pp. 220-224. https://doi.org/10.1109/ICCITechnology.2013.6579553Ritu and P. Bhambri, “Enhancing software development effort estimation with a cloud-based data framework using use case points, fuzzy logic, and machine learning,” Discover Computing, vol. 28, e143, 2025. https://doi.org/10.1007/s10791-025-09668-1A. Najm, A. Zakrani, A. Marzak, “Decision trees based software development effort estimation: A systematic mapping study,” in Proceedings of the International Conference on Computer Science and Renewable Energies, Agadir, Morocco, 2019, pp. 1–6. https://doi.org/10.1109/ICCSRE.2019.8807544M. Hosni, A. Idri, A. Abran, “Investigating heterogeneous ensembles with filter feature selection for software effort estimation,” in Proceedings of the ACM International Conference on Software Engineering and Knowledge Engineering, Pittsburgh, USA, Jul. 2017, pp. 207-220. https://doi.org/10.1145/3143434.3143456I. A. Al-Naimy, M. A. Al-Jawaherry, “Software effort estimation using ensemble learning methods,” AIP Conference Proceedings, vol. 3264, no. 1, e040021, Mar. 2025. https://doi.org/10.1063/5.0259225A. G. Priya Varshini, A. Kumari J. Ramprasath, S. Rishi R, S. Balakrishnan, D. Deepak, “Optimized Convolutional Neural Network Model for Software Effort Estimation,” in Proceedings of the 2024 Third International Conference on Smart Technologies in Systems and Networking Computing, Villupuram, India, 2024, pp. 1-6. https://doi.org/10.1109/ICSTSN61422.2024.10671053A. Idri, I. Abnane, “Fuzzy analogy based effort estimation: An empirical comparative study,” in Proceedings of the IEEE International Conference on Computer and Information Technology, Helsinki, Finland, 2017, pp. 114-121. https://doi.org/10.1109/CIT.2017.29P. Rai, D. K. Verma, S. Kumar, “A hybrid model for prediction of software effort based on team size,” IET Software, vol. 15, no. 6, pp. 546-556, Dec. 2021. https://doi.org/10.1049/sfw2.12048A. Zakrani, M. Hain, A. Namir, “Software development effort estimation using random forests: An empirical study and evaluation,” International Journal of Intelligent Engineering and Systems, vol. 11, no. 6, pp. 300-311, Dec. 2018. https://doi.org/10.22266/ijies2018.1231.30ISBSG, Academic research projects, 2023. https://www.isbsg.orgA. M. Shimaoka, R. C. Ferreira, A. Goldman, “The evolution of CRISP-DM for Data Science: Methods, Processes and Frameworks,” SBC Reviews, vol. 4, no. 1, pp. 28-43, Oct. 2024. https://doi.org/10.5753/reviews.2024.3757F. González-Ladrón-de-Guevara, M. Fernández-Diego, “ISBSG variables most frequently used for software effort estimation: A mapping review,” in Proceedings of the 8th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, Torino, Italy, Sep. 2014, pp. 1-4. https://doi.org/10.1145/2652524.2652550B. Alsaadi, K. Saeedi, “Data-driven effort estimation techniques of agile user stories: A systematic literature review,” Artificial Intelligence Review, vol. 55, no. 7, pp. 5485-5516, Oct. 2022. https://doi.org/10.1007/s10462-021-10132-xe2019473e2019434Revista Facultad de Ingenieríahttps://revistas.uptc.edu.co/index.php/ingenieria/article/download/20194/16515https://revistas.uptc.edu.co/index.php/ingenieria/article/download/20194/16804Núm. 73 , Año 2025 : July-September 2025 (Continuous Publication)PublicationOREORE.xmltext/xml2721https://repositorio.uptc.edu.co/bitstreams/6cffd553-77a4-402a-a973-3fb10fb4b71c/download17cd089e27b2c26a726fe80550acedc9MD51falseAnonymousREAD001/21615oai:repositorio.uptc.edu.co:001/216152026-10-29 14:05:13.524https://creativecommons.org/licenses/by/4.0metadata.onlyhttps://repositorio.uptc.edu.coRepositorio Institucional UPTCbdigital@metabiblioteca.com

Effort Estimation in Software Development Projects Using Supervised Machine Learning Techniques

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