FRCU - GIICIS: Grupo de Investigación en Inteligencia Computacional e Ingeniería de Software - Comunicaciones a congresos

Permanent URI for this collectionhttp://48.217.138.120/handle/20.500.12272/4096

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    Fuzzy bi-objective particle swarm optimization for next release poblem
    (2019-07-10) Casanova Pietroboni, Carlos Antonio; Rottoli, Giovanni Daián; Schab, Esteban Alejandro; Bracco, Luciano Joaquín; Pereyra Rausch, Fernando Nahuel; De Battista, Anabella Cecilia
    In search-based software engineering (SBSE), software engineers usually have to select one among many quasi-optimal solutions with different values for the objectives of interest for a particular problem domain. Because of this, a metaheuristic algorithm is needed to explore a larger extension of the Pareto optimal front to provide a bigger set of possible solutions. In this regard the Fuzzy Multi-Objective Particle Swarm Optimization (FMOPSO), a novel a posteriori algorithm, is proposed in this paper and compared with other state-of-the-art algorithms. The results show that FMOPSO is adequate for finding very detailed Pareto Fronts.
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    Multi-criteria group requirement prioritization in software engineering using fuzzy linguistic labels
    (2021-10-30) Casanova Pietroboni, Carlos Antonio; Rottoli, Giovani Daian
    Requirement prioritization is a Software Engineering task that helps to choose which and in what order requirements will be implemented in each software development process iteration. In the same way, requirement prioritization is extremely useful to make decisions during iteration management. In this work a method for requirement prioritization is proposed. This method considers many experts’ opin-ions on multiple decision criteria provided using fuzzy linguistic labels, which allows to capture the imprecision of each experts’ judgment. The opinions are aggregated using a majority-guided linguistic IOWA operator considering different weights for each expert and then the requirements are prioritized considering the aggregated opinions and different weights for each evaluated dimension. The proposed method has been implemented and demonstrated using a test dataset.