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A decision-making support system for Enterprise Architecture Modelling

  • Autores: Ricardo Pérez Castillo, Francisco Ruiz González, Mario G. Piattini Velthuis Árbol académico
  • Localización: Actas de las XXV Jornadas de Ingeniería del Software y Bases de Datos (JISBD 2021): [Málaga, 22 al 24 de septiembre de 2021] / coord. por Rafael Capilla Sevilla Árbol académico, Maider Azanza Sese Árbol académico, Miguel Rodríguez Luaces Árbol académico, M. M. Roldán García Árbol académico, Dolores Burgueño Caballero, José Raúl Romero Salguero Árbol académico, José Antonio Parejo Maestre Árbol académico, José Francisco Chicano García Árbol académico, Marcela Genero Árbol académico, Óscar Díaz García Árbol académico, José González Enríquez Árbol académico, María Carmen Penades Gramage Árbol académico; Silvia Mara Abrahao Gonzales (col.) Árbol académico, 2021
  • Idioma: español
  • Texto completo no disponible (Saber más ...)
  • Resumen
    • Companies are increasingly conscious of the importance of Enterprise Architecture (EA) to represent and manage IT and business in a holistic way. EA modelling has become decisive to achieve models that accurately represents behaviour and assets of companies and lead them to make appropriate business decisions. Although EA representations can be manually modelled by experts, automatic EA modelling methods have been proposed to deal with drawbacks of manual modelling, such as error-proneness, time-consumption, slow and poor re-adaptation, and cost. However, automatic modelling is not effective for the most abstract concepts in EA like strategy or motivational aspects. Thus, companies are demanding hybrid approaches that combines automatic with manual modelling. In this context there are no clear relationships between the input artefacts (and mining techniques) and the target EA viewpoints to be automatically modelled, as well as relationships between the experts' roles and the viewpoints to which they might contribute in manual modelling. Consequently, companies cannot make informed decisions regarding expert assignments in EA modelling projects, nor can they choose appropriate mining techniques and their respective input artefacts. This research proposes a decision support system whose core is a genetic algorithm. The proposal first establishes (based on a previous literature review) the mentioned missing relationships and EA model specifications. Such information is then employed using a genetic algorithm to decide about automatic, manual or hybrid modelling by selecting the most appropriate input artefacts, mining techniques and experts. The genetic algorithm has been optimized so that the system aids EA architects to maximize the accurateness and completeness of EA models while cost (derived from expert assignments and unnecessary automatic generations) are kept under control.


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