FRCU - GIICIS: Grupo de Investigación en Inteligencia Computacional e Ingeniería de Software - Artículos

Permanent URI for this collectionhttp://48.217.138.120/handle/20.500.12272/4095

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Subject: search.filters.subject.Fuzzy logic

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    Multi-criteria and multi-expert requirement prioritization using fuzzy linguistic labels
    (ParadigmPlus, 2022-02-08) Rottoli, Giovani Daian; Casanova Pietroboni, Carlos Antonio
    Requirement prioritization in Software Engineering is the activity that helps to select and or-der for the requirements to be implemented in each software development process iteration. Thus, requirement prioritization assists the decision-making process during iteration management. This work presents a method for requirement prioritization that considers many experts’ opinions on multiple decision criteria provided using fuzzy linguistic labels, a tool that allows capturing the imprecision of each experts’ judgment. These opinions are then aggregated using the fuzzy ag-gregation operator MLIOWA considering different weights for each expert. Then, an order for the requirements is given considering the aggregated opinions and different weights for each evaluated dimension or criteria. The method proposed in this work has been implemented and demonstrated using a synthetic dataset. A statistical evaluation of the results obtained using different t-norms was also carried out.
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    Optimización multiobjetivo difusa mediante enjambre de partículas aplicada al problema del próximo lanzamiento
    (2019-05-02) Casanova Pietroboni, Carlos Antonio; Rottoli, Giovanni Daián; Schab, Esteban Alejandro; De Battista, Anabella Cecilia; Tournoud, Adrián Alberto; Bracco, Luciano Joaquín; Pereyra Rausch, Fernando Nahuel
    En este trabajo se presenta un método novedoso basado en Enjambres de Partículas y Lógica Difusa para optimización multiobjetivo: el FMOPSO (Fuzzy Multi-Objective Particle Swarm Optimization). Este método se presenta en el contexto de la resolución de un problema clásico de la Ingeniería de Software Basada en Búsqueda: el Problema del Próximo Lanzamiento (Next Release Problem). Se realiza una prueba de concepto aplicando este algoritmo a una instancia bi-objetivo del problema mencionado anteriormente, y se lo compara con otra metaheurística del estado del arte. Finalmente, se concluye resaltando los resultados más importantes.