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                <identifier>oai:bibliotecas.ucasal.edu.ar:61343</identifier>
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<oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:identifier>https://bibliotecas.ucasal.edu.ar/opac_css/index.php?lvl=cmspage&amp;pageid=24&amp;id_notice=61343</dc:identifier>
  <dc:identifier>https://bibliotecas.ucasal.edu.ar/ark:/49597/ntxtcdfjbm</dc:identifier>
  <dc:identifier>https://n2t.net/ark:/49597/ntxtcdfjbm</dc:identifier>
  <dc:identifier>61343</dc:identifier>
  <dc:identifier>20170516u        u  u0frey0103    ba</dc:identifier>
  <dc:language>spa</dc:language>
  <dc:title>An Intelligent Approach to Support Software Architecture Decision-making in the Context of Software Architecture Evaluation</dc:title>
  <dc:publisher>Universidad Católica de Salta. Facultad de Ingeniería (Salta)</dc:publisher>
  <dc:date>2016-12-30</dc:date>
  <dc:type>info:ar-repo/semantics/documento de conferencia</dc:type>
  <dc:type>info:eu-repo/semantics/conferenceObject</dc:type>
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  <dc:coverage>1001514</dc:coverage>
  <dc:coverage>Salta (province)</dc:coverage>
  <dc:description>Software Engineering needs novel tools to pursue further the goals of achieving software quality, facing the changing role of software. In this context, Software Architecture plays a key role because it directly affects the final quality. Software Architecture Evaluation validates if the architecture achieves the quality requirements, and triggers a set of design decisions. The decision-making is a very complex process driven by several human factors. It is argued that Artificial Intelligence-based practices can assist this process. In this work, an Artificial Intelligence-based approach for assisting architects in the design decision-making process driven by quality attributes is proposed. This first version combines quality-attribute models and an intelligent agent to support software architecture evaluation. It applies Reinforcement Learning tools to obtain a sequential architectural pattern application policy by simulation. A case study and a set of experiments illustrate the proposal with patterns commonly used in software industry. </dc:description>
  <dc:description>Fil: Leone, Horacio. Universidad Tecnológica Nacional. Facultad Regional Santa Fe; Argentina.</dc:description>
  <dc:description>Fil: Gonnet, Silvio. Universidad Tecnológica Nacional. Facultad Regional Santa Fe; Argentina.</dc:description>
  <dc:description>Fil: Villarreal Guzmán, Eva. Universidad Tecnológica Nacional. Facultad Regional Villa María; Argentina.</dc:description>
  <dc:description>Fil: Bogado, Verónica. Universidad Tecnológica Nacional. Facultad Regional Villa María; Argentina.</dc:description>
  <dc:creator>Bogado, Verónica</dc:creator>
  <dc:creator>Villarreal Guzmán, Eva</dc:creator>
  <dc:creator>Gonnet, Silvio</dc:creator>
  <dc:creator>Leone, Horacio</dc:creator>
  <dc:contributor>Congreso Nacional de Ingeniería en Informática / Sistemas de información (4° : 2016 nov. 17-18 : Salta)</dc:contributor>
  <dc:subject>Diseño de sistemas</dc:subject>
  <dc:subject>Informática</dc:subject>
  <dc:subject>Software</dc:subject>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by-nc-sa/4.0/</dc:rights>
  <dc:rights>Creative Commons Atribución/Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0)</dc:rights>
  <dc:audience>Researchers</dc:audience>
  <dc:audience>Students</dc:audience>
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