Integración de herramienta para el seguimiento ágil mediante el uso de Machine Learning
Resumo
El enfoque ágil ha impulsado la necesidad de herramientas avanzadas para la gestión de proyectos, especialmente en entornos multiproyecto. Este trabajo presenta la integración de un modelo de Machine Learning (ML) en Worki, una plataforma de seguimiento ágil, para analizar datos históricos de Sprints y evaluar el rendimiento. El modelo clasifica y evalúa el progreso y ritmo de trabajo, mostrando los resultados directamente en la plataforma. Esto permite una visión basada en datos, facilitando la detección temprana de anomalías y mejorando la eficiencia en la gestión de proyectos.
Palavras-chave:
Aprendizaje automático, Gestión ágil, Progreso en sprints, Optimización multiproyecto
Referências
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Pérez Castillo, Y. J., Orantes Jiménez, S. D., & Letelier Torres, P. O. (2024). Sprint management in agile approach: Progress and velocity evaluation applying machine learning. Information, 15(11), 726. DOI: 10.3390/info15110726
Dam, H., Tran, T., Grundy, J., Ghose, A., & Kamei, Y. (2019). Towards effective AI-powered agile project management. IEEE/ACM 41st International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER), 41-44. DOI: 10.1109/ICSE-NIER.2019.00019
Digital.ai. (2024). The 17th state of agile report. Analyst Reports - Digital.ai. Recuperado de [link]
Gaona-Cuevas, M., Guerrero, V., & Vera-Rivera, F. (2024). The Smart Product Backlog: A Classification Model of User Stories. IEEE Access, 12, 150008-150019. DOI: 10.1109/ACCESS.2024.3478833
Mahdi, M., Zabil, M., Ahmad, A., Ismail, R., Yusoff, Y., Cheng, L., Naidu, H. (2021). Software Project Management Using Machine Learning Technique—A Review. Applied Sciences, 11(11), 5183. DOI: 10.3390/app11115183
Mamatha, R., & Suma, K. (2021, October). Role of machine learning in software project management. Journal of Physics: Conference Series, 2040(1), 012038. DOI: 10.1088/1742-6596/2040/1/012038
Meiliana, Daniella, G., Wijaya, N., Putra, N., & Efata, R. (2023, January). Agile software development effort estimation based on product backlog items. Procedia Computer Science, 227, 186-193. DOI: 10.1016/j.procs.2023.10.516
Olivares, R., Noel, R., Guzmán, S., Miranda, D., & Munoz, R. (2024, May). Intelligent learning-based methods for determining the ideal team size in agile practices. Bioengineering, 9(5), 292. DOI: 10.3390/biomimetics9050292
Prasetyo, M., Peranginangin, R., Martinovic, N., Ichsan, M., & Wicaksono, H. (2025, March). Artificial intelligence in open innovation project management: A systematic literature review on technologies, applications, and integration requirements. Lecture Notes in Networks and Systems, 1005, 200-213. DOI: 10.1016/j.joitmc.2024.100445
Rodríguez Sánchez, E., Vázquez Santacruz, E., & Cervantes Maceda, H. (2023, March). Effort and cost estimation using decision tree techniques and story points in agile software development. Mathematics, 11(6), 1477. DOI: 10.3390/math11061477
Schwaber, K., & Sutherland, J. (2020). La guía definitiva de Scrum: Las reglas del juego. Recuperado de [link]
Shameem, M., Nadeem, M., & Zamani, A. (2023, March). Genetic algorithm-based probabilistic model for agile project success in global software development. Journal of Software: Evolution and Process. DOI: 10.1002/smr.2349
Sutherland, J. (2001). Manifesto for agile software development. Recuperado el 24 de noviembre de 2024, de [link]
Tiwari, S., Phonsa, G., & Malik, N. (2024, January). Estimation approaches of machine learning in Scrum projects. Lecture Notes in Networks and Systems, 731, 103-111. DOI: 10.1007/978-981-99-4071-4_9
TUNE-UP Process. (2017). Recuperado el 01 de agosto de 2023, de [link]
Tuneupprocess. (2017). Worki. Recuperado el 18 de octubre de 2024, de [link]
Atlassian. (2024). Jira. Recuperado el 20 de octubre de 2024, de [link]
Baharom, M., Rahman, M., Sabudin, A., & Nor, M. (2023). Decision support tools: Machine learning application in smart planner. Lecture Notes in Mechanical Engineering, 753-760. DOI: 10.1007/978-981-19-1939-8_58
Pérez Castillo, Y. J., Orantes Jiménez, S. D., & Letelier Torres, P. O. (2024). Sprint management in agile approach: Progress and velocity evaluation applying machine learning. Information, 15(11), 726. DOI: 10.3390/info15110726
Dam, H., Tran, T., Grundy, J., Ghose, A., & Kamei, Y. (2019). Towards effective AI-powered agile project management. IEEE/ACM 41st International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER), 41-44. DOI: 10.1109/ICSE-NIER.2019.00019
Digital.ai. (2024). The 17th state of agile report. Analyst Reports - Digital.ai. Recuperado de [link]
Gaona-Cuevas, M., Guerrero, V., & Vera-Rivera, F. (2024). The Smart Product Backlog: A Classification Model of User Stories. IEEE Access, 12, 150008-150019. DOI: 10.1109/ACCESS.2024.3478833
Mahdi, M., Zabil, M., Ahmad, A., Ismail, R., Yusoff, Y., Cheng, L., Naidu, H. (2021). Software Project Management Using Machine Learning Technique—A Review. Applied Sciences, 11(11), 5183. DOI: 10.3390/app11115183
Mamatha, R., & Suma, K. (2021, October). Role of machine learning in software project management. Journal of Physics: Conference Series, 2040(1), 012038. DOI: 10.1088/1742-6596/2040/1/012038
Meiliana, Daniella, G., Wijaya, N., Putra, N., & Efata, R. (2023, January). Agile software development effort estimation based on product backlog items. Procedia Computer Science, 227, 186-193. DOI: 10.1016/j.procs.2023.10.516
Olivares, R., Noel, R., Guzmán, S., Miranda, D., & Munoz, R. (2024, May). Intelligent learning-based methods for determining the ideal team size in agile practices. Bioengineering, 9(5), 292. DOI: 10.3390/biomimetics9050292
Prasetyo, M., Peranginangin, R., Martinovic, N., Ichsan, M., & Wicaksono, H. (2025, March). Artificial intelligence in open innovation project management: A systematic literature review on technologies, applications, and integration requirements. Lecture Notes in Networks and Systems, 1005, 200-213. DOI: 10.1016/j.joitmc.2024.100445
Rodríguez Sánchez, E., Vázquez Santacruz, E., & Cervantes Maceda, H. (2023, March). Effort and cost estimation using decision tree techniques and story points in agile software development. Mathematics, 11(6), 1477. DOI: 10.3390/math11061477
Schwaber, K., & Sutherland, J. (2020). La guía definitiva de Scrum: Las reglas del juego. Recuperado de [link]
Shameem, M., Nadeem, M., & Zamani, A. (2023, March). Genetic algorithm-based probabilistic model for agile project success in global software development. Journal of Software: Evolution and Process. DOI: 10.1002/smr.2349
Sutherland, J. (2001). Manifesto for agile software development. Recuperado el 24 de noviembre de 2024, de [link]
Tiwari, S., Phonsa, G., & Malik, N. (2024, January). Estimation approaches of machine learning in Scrum projects. Lecture Notes in Networks and Systems, 731, 103-111. DOI: 10.1007/978-981-99-4071-4_9
TUNE-UP Process. (2017). Recuperado el 01 de agosto de 2023, de [link]
Tuneupprocess. (2017). Worki. Recuperado el 18 de octubre de 2024, de [link]
Publicado
12/05/2025
Como Citar
CASTILLO, Yadira Jazmín Pérez; JIMÉNEZ, Sandra Dinora Orantes; TORRES, Patricio Orlando Letelier; MOSQUED, María Elena Acevedo.
Integración de herramienta para el seguimiento ágil mediante el uso de Machine Learning. In: CONGRESSO IBERO-AMERICANO EM ENGENHARIA DE SOFTWARE (CIBSE), 28. , 2025, Ciudad Real/Espanha.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 16-29.
DOI: https://doi.org/10.5753/cibse.2025.35289.
