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

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Author: search.filters.author.Rottoli, Giovani DaianSubject: 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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    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.