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Desarrollo y Validación de Modelos de Redes Neuronales Para Evaluar la Estimación de Esfuerzos en Proyectos de Desarrollo de Software

Develop and Validation of Neural Network Models to Evaluate Effort Estimation in Software Development Projects

dc.contributorAvilés Sánchez, Oscar Fernando
dc.creatorMercado Espinosa, John Jairo
dc.date2025-12-10T17:01:51Z
dc.date2025-12-10T17:01:51Z
dc.date2025-06-05
dc.date.accessioned2026-03-25T19:27:43Z
dc.date.available2026-03-25T19:27:43Z
dc.identifierhttps://hdl.handle.net/10654/48063
dc.identifierinstname:Universidad Militar Nueva Granada
dc.identifierreponame:Repositorio Institucional Universidad Militar Nueva Granada
dc.identifierrepourl:https://repository.umng.edu.co
dc.identifier.urihttp://test.repositoriodigital.com:8080/handle/123456789/201289
dc.descriptionEste estudio desarrolló y validó modelos de redes neuronales artificiales para estimar esfuerzos en proyectos de desarrollo de software, realizando la comparación entre arquitecturas MLP optimizadas con algoritmos evolutivos y arquitecturas híbridas CNN+GRU. Utilizando ocho conjuntos de datos públicos (ISBSG, NASA93, China, entre otros), se procesaron 1287 proyectos históricos con métricas como líneas de código, complejidad técnica y horas invertidas. Los modelos mostraron una precisión superior a los métodos tradicionales (COCOMO, puntos de función), reduciendo la magnitud de error relativo medio (MMRE) en un 68-82% y logrando que el 93% de las estimaciones tuvieran menos del 25% de desviación (PRED25). La arquitectura CNN+GRU mostró ventajas en conjuntos de datos con patrones secuenciales (RMSE 0.56 en Maxwell), mientras que el modelo MLP evolutivo se destacó en conjuntos heterogéneos como ISBSG (MAE 0.12) Los resultados sugieren que la implementación de estos modelos en etapas tempranas de la planificación de proyectos, pueden reducir los sobrecostos y mejorar la asignación de recursos. El estudio también evidencia que la unión entre la inteligencia artificial y el criterio experto ofrece un marco prometedor para gestionar la incertidumbre inherente en los procesos de desarrollo de software.
dc.descriptionThis study developed and validated artificial neural network models to estimate efforts in software development projects, comparing MLP architectures optimized with evolutionary algorithms and hybrid CNN+GRU architectures. Using eight public data sets (ISBSG, NASA93, China, among others), 1287 historical projects were processed with metrics such as lines of code, technical complexity, and hours invested. The models showed superior accuracy to traditional methods (COCOMO, function points), reducing the mean relative error (MMRE) by 68-82% and achieving that 93% of the estimates had less than 25% deviation (PRED25). The CNN+GRU architecture showed advantages on datasets with sequential patterns (RMSE 0.56 in Maxwell), while the evolutionary MLP model excelled on heterogeneous sets such as ISBSG (MAE 0.12). The results also suggest that implementing these models in early stages of project planning can reduce cost overruns and improve resource allocation. The study shows that the union between artificial intelligence and expert judgment offers a promising framework for managing the uncertainty inherent in software development processes.
dc.descriptionTabla de contenido Resumen .......................................................................................................................................... 6 Abstract ........................................................................................................................................... 7 Tabla de contenido .......................................................................................................................... 8 Índice de tablas ............................................................................................................................. 11 Índice de figuras ............................................................................................................................ 12 Lista de abreviaturas y acrónimos................................................................................................. 13 Capítulo 1. Introducción ............................................................................................................... 15 1.1. Planteamiento del problema ........................................................................................... 15 1.2. Justificación .................................................................................................................... 19 1.3. Objetivos ........................................................................................................................ 23 1.3.1. Objetivo General ..................................................................................................... 23 1.3.2. Objetivos específicos .............................................................................................. 23 1.4. Alcance y limitaciones ................................................................................................... 24 Capítulo 2. Marco Teórico ............................................................................................................ 26 2.1. Gestión de proyectos de software....................................................................................... 26 2.1.1. Conceptos fundamentales ............................................................................................ 26 2.2. Estimación de esfuerzos en proyectos de software ............................................................ 33 2.2.1. Métodos tradicionales de estimación ........................................................................... 33 2.2.2. Desafíos en la estimación de esfuerzos ....................................................................... 36 2.2.3. Limitaciones de los métodos actuales ......................................................................... 38 2.3. Redes neuronales artificiales .............................................................................................. 40 2.3.1. Fundamentos teóricos .................................................................................................. 40 2.3.2. Tipos de redes neuronales............................................................................................ 41 2.3.3 Inteligencia Artificial Generativa ................................................................................. 46 2.4. Inteligencia artificial en la gestión de proyectos ................................................................ 47 2.4.1. Estado actual de la IA en proyectos de software ......................................................... 47 2.4.2. Herramientas y aplicaciones existentes ....................................................................... 48 2.4.3. Factores que influyen en la adopción de la IA ............................................................ 50 2.4.4. Impacto en la productividad y precisión de estimaciones ........................................... 51 Capítulo 3. Metodología ............................................................................................................... 53 3.1. Diseño de la investigación ................................................................................................. 53 3.1.1. Enfoque ........................................................................................................................ 53 3.1.2. Alcance y limitaciones ................................................................................................. 53 3.2. Desarrollo de modelos de redes neuronales ....................................................................... 55 3.2.1. Selección y preparación de datos ................................................................................. 55 3.2.2. Arquitectura de los modelos ........................................................................................ 69 3.2.3. Proceso de entrenamiento ............................................................................................ 82 3.3. Validación y evaluación ..................................................................................................... 86 3.3.1. Métricas de evaluación ................................................................................................ 86 Capítulo 4. Resultados .................................................................................................................. 88 4.1. Modelos de redes neuronales desarrollados ....................................................................... 88 4.1.1. Rendimiento y precisión .............................................................................................. 88 4.1.2. Análisis por conjunto de datos ..................................................................................... 89 4.2. Análisis comparativo .......................................................................................................... 91 4.2.1. Comparación con los métodos tradicionales ............................................................... 92 4.2.2. Impacto en la precisión de estimaciones ..................................................................... 93 4.2.3 Objetivos y resultados .................................................................................................. 94 Capítulo 5. Discusión .................................................................................................................... 96 5.1. Interpretación de resultados ............................................................................................... 96 5.1.1. Desempeño de los modelos ......................................................................................... 97 5.2. Implicaciones prácticas ...................................................................................................... 99 5.2.1. Implicaciones en la gestión de proyectos .................................................................. 100 5.3. Limitaciones del estudio................................................................................................... 102 5.4. Contribuciones de la investigación .................................................................................. 103 Capítulo 6. Conclusiones y recomendaciones ............................................................................ 105 6.1. Conclusiones .................................................................................................................... 105 6.1.1. Sobre los modelos desarrollados ............................................................................... 105 6.1.3. Sobre la integración en entornos organizacionales .................................................... 107 6.2. Recomendaciones ............................................................................................................. 108 6.2.1. Para la práctica profesional........................................................................................ 108 6.2.2. Para futuras investigaciones ...................................................................................... 110 Bibliografía ................................................................................................................................. 113 Anexo 1: Trazabilidad de fragmentos de código ........................................................................ 123
dc.descriptionMaestría
dc.formatapplicaction/pdf
dc.formatapplication/pdf
dc.languagespa
dc.publisherMaestría en Gerencia de Proyectos
dc.publisherFacultad de Ingeniería
dc.publisherUniversidad Militar Nueva Granada
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dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rightsAcceso abierto
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.subjectEstimación de esfuerzos
dc.subjectSoftware
dc.subjectRedes Neuronales Artificiales
dc.subjectRedes Neuronales Convolucionales
dc.subjectUnidad Recurrente Cerrada
dc.subjectPerceptrón Multicapa
dc.subjectGestión de Proyectos de Software
dc.subjectEffort Estimation
dc.subjectSoftware
dc.subjectArtificial Neural Networks
dc.subjectConvolutional Neural Networks
dc.subjectGated Recurrent Unit
dc.subjectMulti-layer Perceptron
dc.subjectSoftware Project Management
dc.titleDesarrollo y Validación de Modelos de Redes Neuronales Para Evaluar la Estimación de Esfuerzos en Proyectos de Desarrollo de Software
dc.titleDevelop and Validation of Neural Network Models to Evaluate Effort Estimation in Software Development Projects
dc.typeTesis/Trabajo de grado - Monografía - Maestría
dc.typeinfo:eu-repo/semantics/masterThesis
dc.typehttp://purl.org/coar/resource_type/c_bdcc
dc.typeinfo:eu-repo/semantics/acceptedVersion
dc.coverageBogotá
dc.coverage2025
dc.coverageCalle 100


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