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On the Detection of Structural Aesthetic Defects of Android Mobile User Interfaces with a Metrics-based Tool

机译:基于指标的工具检测Android移动用户界面结构审美缺陷

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Smartphone users are striving for easy-to-learn and use mobile apps user interfaces. Accomplishing these qualities demands an iterative evaluation of the Mobile User Interface (MUI). Several studies stress the value of providing a MUI with a pleasing look and feel to engaging end-users. The MUI, therefore, needs to be free from all kinds of structural aesthetic defects. Such defects are indicators of poor design decisions interfering with the consistency of a MUI and making it more difficult to use. To this end, we are proposing a tool (Aesthetic Defects DEtection Tool (ADDET)) to determine the structural aesthetic dimension of MUIs. Automating this process is useful to designers in evaluating the quality of their designs. Our approach is composed of two modules. (1) Metrics assessment is based on the static analysis of a tree-structured layout of the MUI. We used 15 geometric metrics (also known as structural or aesthetic metrics) to check various structural properties before a defect is triggered. (2) Defects detection: The manual combination of metrics and defects are time-consuming and user-dependent when determining a detection rule. Thus, we perceive the process of identification of defects as an optimization problem. We aim to automatically combine the metrics related to a particular defect and optimize the accuracy of the rules created by assigning a weight, representing the metric importance in detecting a defect. We conducted a quantitative and qualitative analysis to evaluate the accuracy of the proposed tool in computing metrics and detecting defects. The findings affirm the tool's reliability when assessing a MUI's structural design problems with 71% accuracy.
机译:智能手机用户正在努力易于学习和使用移动应用程序用户界面。完成这些品质要求对移动用户界面(MUI)进行迭代评估。几项研究压力了提供一个令人愉悦的外观和感觉到最终用户的价值。因此,MUI需要摆脱各种结构审美缺陷。这种缺陷是有差的设计决策的指标干扰了MUI的一致性并使其更难以使用。为此,我们正在提出一种工具(美学缺陷检测工具(addet)),以确定Muis的结构美学维度。自动化此过程对设计人员评估其设计的质量。我们的方法由两个模块组成。 (1)指标评估是基于对MUI树结构布局的静态分析。我们使用了15个几何度量(也称为结构或美学度量)来检查触发缺陷之前的各种结构性。 (2)缺陷检测:在确定检测规则时,指标和缺陷的手动组合是耗时和用户依赖性。因此,我们认为将缺陷识别作为优化问题的过程。我们的目标是自动将与特定缺陷相关的指标组合,并优化通过分配权重创建的规则的准确性,表示检测缺陷的度量标准重要性。我们进行了定量和定性分析,以评估所提出的工具在计算度量和检测缺陷中的准确性。调查结果确认了在评估MUI的结构设计问题时,申明了该工具的可靠性,以71%的精度为71%。

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