Sprint Management in Agile Approach: Progress and Velocity Evaluation Applying Machine Learning
Nowadays, technology plays a fundamental role in data collection and analysis, which are essential for decision-making in various fields. Agile methodologies have transformed project management by focusing on continuous delivery and adaptation to change. In multiple project management, assessing the...
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| Format: | Article |
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MDPI AG
2024-11-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/15/11/726 |
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| author | Yadira Jazmín Pérez Castillo Sandra Dinora Orantes Jiménez Patricio Orlando Letelier Torres |
| author_facet | Yadira Jazmín Pérez Castillo Sandra Dinora Orantes Jiménez Patricio Orlando Letelier Torres |
| author_sort | Yadira Jazmín Pérez Castillo |
| collection | DOAJ |
| description | Nowadays, technology plays a fundamental role in data collection and analysis, which are essential for decision-making in various fields. Agile methodologies have transformed project management by focusing on continuous delivery and adaptation to change. In multiple project management, assessing the progress and pace of work in Sprints is particularly important. In this work, a data model was developed to evaluate the progress and pace of work, based on the visual interpretation of numerical data from certain graphs that allow tracking, such as the Burndown chart. Additionally, experiments with machine learning algorithms were carried out to validate the effectiveness and potential improvements facilitated by this dataset development. |
| format | Article |
| id | doaj-art-4acf83e9f7da4f4cadfb516d6aee9ac1 |
| institution | Kabale University |
| issn | 2078-2489 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj-art-4acf83e9f7da4f4cadfb516d6aee9ac12024-11-26T18:06:43ZengMDPI AGInformation2078-24892024-11-01151172610.3390/info15110726Sprint Management in Agile Approach: Progress and Velocity Evaluation Applying Machine LearningYadira Jazmín Pérez Castillo0Sandra Dinora Orantes Jiménez1Patricio Orlando Letelier Torres2Centro de Investigación en Computación, Instituto Politécnico Nacional, Gustavo A. Madero, Mexico City 07738, MexicoCentro de Investigación en Computación, Instituto Politécnico Nacional, Gustavo A. Madero, Mexico City 07738, MexicoDepartament de Sistemes Informàtics i Computació, Universitat Politècnica de València, 46022 Valencia, SpainNowadays, technology plays a fundamental role in data collection and analysis, which are essential for decision-making in various fields. Agile methodologies have transformed project management by focusing on continuous delivery and adaptation to change. In multiple project management, assessing the progress and pace of work in Sprints is particularly important. In this work, a data model was developed to evaluate the progress and pace of work, based on the visual interpretation of numerical data from certain graphs that allow tracking, such as the Burndown chart. Additionally, experiments with machine learning algorithms were carried out to validate the effectiveness and potential improvements facilitated by this dataset development.https://www.mdpi.com/2078-2489/15/11/726sprint performance evaluationprogress monitoringagile project managementmachine learning |
| spellingShingle | Yadira Jazmín Pérez Castillo Sandra Dinora Orantes Jiménez Patricio Orlando Letelier Torres Sprint Management in Agile Approach: Progress and Velocity Evaluation Applying Machine Learning Information sprint performance evaluation progress monitoring agile project management machine learning |
| title | Sprint Management in Agile Approach: Progress and Velocity Evaluation Applying Machine Learning |
| title_full | Sprint Management in Agile Approach: Progress and Velocity Evaluation Applying Machine Learning |
| title_fullStr | Sprint Management in Agile Approach: Progress and Velocity Evaluation Applying Machine Learning |
| title_full_unstemmed | Sprint Management in Agile Approach: Progress and Velocity Evaluation Applying Machine Learning |
| title_short | Sprint Management in Agile Approach: Progress and Velocity Evaluation Applying Machine Learning |
| title_sort | sprint management in agile approach progress and velocity evaluation applying machine learning |
| topic | sprint performance evaluation progress monitoring agile project management machine learning |
| url | https://www.mdpi.com/2078-2489/15/11/726 |
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