Integration of a tool for agile monitoring through the use of Machine Learning
Abstract
The agile approach has driven the need for advanced tools in project management, especially in multi-project environments. This work presents the integration of a Machine Learning (ML) model into Worki, an agile tracking platform, to analyze historical Sprint data and evaluate performance. The model classifies and assesses progress and work pace, displaying the results directly within the platform. This enables a data-driven perspective, facilitating early anomaly detection and improving project management efficiency.
Keywords:
Machine learning, Agile management, Sprint progress, Multi-project optimization
References
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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
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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
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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
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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]
Published
2025-05-12
How to Cite
CASTILLO, Yadira Jazmín Pérez; JIMÉNEZ, Sandra Dinora Orantes; TORRES, Patricio Orlando Letelier; MOSQUED, María Elena Acevedo.
Integration of a tool for agile monitoring through the use of Machine Learning. In: IBERO-AMERICAN CONFERENCE ON SOFTWARE ENGINEERING (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.
