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Generically Dependent Continuant
RDF description of Classifying design-level requirements using machine learning for a recommender of interaction design patterns - http://orbis.uaslp.mx/vivo/individual/scopus6389
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Information Content Entity
Consejo Nacional de Ciencia y Tecnología, CONACYT: 246970
Decision making; Human computer interaction; Learning algorithms; Learning systems; Software engineering; Support vector machines; User interfaces; Classification algorithm; Interaction design patterns; Interactive system; Linear Support Vector Machines; Requirements analysis; Requirements classifications; Supervised machine learning; User interface designs; Design
Software Engineering is a discipline that encompasses processes associated with the development of interactive systems. The perceived quality of an interactive system is heavily influenced by the user interface design, which may result in many challenges. One such challenge is design-level requirements analysis. The success of the software system is mostly dependent on how well users%27 requirements have been understood and translated into appropriate functionalities. During the interactive system design process, it is common to find recurring problems in human-computer interactions, for which reusing solutions is highly feasible. Interaction design patterns seek to support designers in decision making during the design of interactive systems. Due to the design task tends to be subjective and prone to errors. This work aims at presenting and evaluating an interaction design patterns recommendation model based on design-level requirements classification, through the application of supervised machine learning algorithms. To compare the performance of four classification algorithms, a study was carried out, in which the linear support vector machine was the most suitable to this problem. The results of this work can be used for implementing frameworks that can better support designers%27 decision making when designing user interfaces. © The Institution of Engineering and Technology 2020
Classifying design-level requirements using machine learning for a recommender of interaction design patterns
Software Engineering is a discipline that encompasses processes associated with the development of interactive systems. The perceived quality of an interactive system is heavily influenced by the user interface design, which may result in many challenges. One such challenge is design-level requirements analysis. The success of the software system is mostly dependent on how well users' requirements have been understood and translated into appropriate functionalities. During the interactive system design process, it is common to find recurring problems in human-computer interactions, for which reusing solutions is highly feasible. Interaction design patterns seek to support designers in decision making during the design of interactive systems. Due to the design task tends to be subjective and prone to errors. This work aims at presenting and evaluating an interaction design patterns recommendation model based on design-level requirements classification, through the application of supervised machine learning algorithms. To compare the performance of four classification algorithms, a study was carried out, in which the linear support vector machine was the most suitable to this problem. The results of this work can be used for implementing frameworks that can better support designers' decision making when designing user interfaces. © The Institution of Engineering and Technology 2020