A Deep Learning Model for the Assessment of the Visual Aesthetics of Mobile User Interfaces

Adriano Luiz de Souza Lima, Christiane Gresse von Wangenheim, O. P. H. R. Martins, A. von Wangenheim, J. Hauck, A. Borgatto
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引用次数: 0

Abstract

Visual aesthetics is one of the first aspects that users experience when looking at graphical user interfaces (GUIs), contributing to the perceived usability and credibility of a software system. It can also be an essential success factor in contexts where graphical elements play an important role in attracting users, such as choosing a mobile app from an app store. Therefore, visual aesthetics assessments are crucial in interface design, but traditional methods, involving target user representatives assessing each GUI individually, are costly and time-consuming. In this context, machine learning models have been demonstrated to be promising in automating the assessment of GUIs of web-based software systems. Yet, solutions for the assessment of mobile GUIs using machine learning are still unknown. Here we introduce a deep learning model to assess the visual aesthetics of mobile Android applications designed with App Inventor. We used a supervised learning approach to train and compare models using three different architectures. The highest performing model, a Resnet50, achieved a mean squared error of .022. The assessments of new GUIs showed an excellent correlation with human ratings (ρ = .9), and the Bland Altman plot analysis revealed 95% agreement with their labels. These results indicate the model’s effectiveness in automating the visual aesthetics assessment of GUIs of mobile apps.
用于评估移动用户界面视觉美感的深度学习模型
视觉美感是用户在观察图形用户界面(GUI)时首先体验到的一个方面,它有助于提高软件系统的可用性和可信度。在图形元素对吸引用户起着重要作用的情况下,例如从应用程序商店选择移动应用程序,视觉美感也是一个重要的成功因素。因此,视觉美学评估在界面设计中至关重要,但传统方法需要目标用户代表对每个图形用户界面进行单独评估,成本高且耗时。在这种情况下,机器学习模型在自动评估基于网络的软件系统的图形用户界面方面大有可为。然而,使用机器学习评估移动图形用户界面的解决方案仍是未知数。在此,我们介绍一种深度学习模型,用于评估使用 App Inventor 设计的安卓移动应用程序的视觉美感。我们采用监督学习方法,使用三种不同的架构对模型进行训练和比较。性能最高的模型(Resnet50)的均方误差为 0.022。对新图形用户界面的评估显示出与人类评分极佳的相关性(ρ = .9),布兰德-阿特曼图分析显示与人类标签的一致性达到 95%。这些结果表明,该模型在自动评估移动应用程序图形用户界面的视觉美感方面非常有效。
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来源期刊
Journal of the Brazilian Computer Society
Journal of the Brazilian Computer Society Computer Science-Computer Science (all)
CiteScore
2.40
自引率
0.00%
发文量
2
期刊介绍: JBCS is a formal quarterly publication of the Brazilian Computer Society. It is a peer-reviewed international journal which aims to serve as a forum to disseminate innovative research in all fields of computer science and related subjects. Theoretical, practical and experimental papers reporting original research contributions are welcome, as well as high quality survey papers. The journal is open to contributions in all computer science topics, computer systems development or in formal and theoretical aspects of computing, as the list of topics below is not exhaustive. Contributions will be considered for publication in JBCS if they have not been published previously and are not under consideration for publication elsewhere.
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