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IET Software
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publication venue for: Classifying design-level requirements using machine learning for a recommender of interaction design patterns
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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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date: 2026-08-27T12:50:19
publisher: vivo
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Article
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Consejo Nacional de Ciencia y Tecnología, CONACYT: 246970
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funding vehicle for: Classifying design-level requirements using machine learning for a recommender of interaction design patterns

Classifying design-level requirements using machine learning for a recommender of interaction design patterns
freetext keyword: 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
page end: 552
type: Document
type: Article
abstract: 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
most specific type: Document
issue: 5
doi: 10.1049/iet-sen.2019.0291
abstract: 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
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has funding vehicle: Consejo Nacional de Ciencia y Tecnología, CONACYT: 246970
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