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A Protocol for Computer-Based Protein Structure and Function Prediction
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Técnicas inteligentes para análisis predictivo en el desarrollo ágil de software

Sahana P Shankar1,2, Shilpa Shashikant Chaudhari3, Vinaytosh Mishra4,5

  • 1Department of Computer Science and Engineering, M.S. Ramaiah Institute of Technology (Affiliated to Visvesvaraya Technological University, Belgaum), Bengaluru, Karnataka, 560054, India. sahanaprabhushankar@gmail.com.

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Resumen
Este resumen es generado por máquina.

Los modelos de aprendizaje automático predicen los tiempos de resolución de problemas de software utilizando el conjunto de datos Agile Effort Estimation Software. XGBoost demostró un rendimiento superior en varias métricas de error, mejorando la gestión de proyectos y la asignación de recursos.

Palabras clave:
conjunto de datos AgESÁgilAprendizaje ProfundoEstimación de esfuerzoGitHubAprendizaje Automático

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Área de la Ciencia:

  • Ingeniería de Software
  • Aprendizaje Automático
  • Ciencia de Datos

Sus antecedentes:

  • La complejidad del desarrollo de software requiere herramientas avanzadas de gestión de proyectos.
  • El análisis predictivo para la estimación del tiempo de resolución de problemas mejora la toma de decisiones y la asignación de recursos.
  • El conjunto de datos Agile Effort Estimation Software (AgES) de GitHub proporciona características ricas para el análisis.

Objetivo del estudio:

  • Analizar modelos de aprendizaje automático para predecir los tiempos de resolución de problemas de software.
  • Evaluar el rendimiento del modelo utilizando métricas como MAE, MSE, RMSE y MdAE.
  • Identificar las metodologías más efectivas para la predicción del tiempo de resolución de problemas.

Principales métodos:

  • Se aplicaron modelos de aprendizaje automático tradicionales y avanzados (redes neuronales, bosques aleatorios, regresión lineal).
  • Se utilizó el conjunto de datos AgES con características como la experiencia del contribuidor, las categorías de problemas y los componentes.
  • Se evaluaron los modelos utilizando el Error Absoluto Medio (MAE), el Error Cuadrático Medio (MSE), el Error Cuadrático Medio (RMSE) y el Error Absoluto Mediano (MdAE).

Principales resultados:

  • El algoritmo XGBoost generalmente tuvo el mejor rendimiento en las métricas de error consideradas.
  • El análisis comparativo incluyó el conjunto de datos AgES frente a conjuntos de datos Ágiles existentes (TAWOS, Choet et al.).
  • La evaluación del modelo destacó las implicaciones prácticas para la gestión de proyectos de software del mundo real.

Conclusiones:

  • El aprendizaje automático ofrece una poderosa herramienta predictiva para la gestión de proyectos de software.
  • Las previsiones precisas del tiempo de resolución de problemas permiten una mejor planificación y gestión de recursos.
  • El estudio detalla el entrenamiento del modelo, la importancia de las características y el potencial transformador del ML en el desarrollo de software.