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.

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使用基于度量的工具检测 Android 移动用户界面的结构美学缺陷

智能手机用户正在努力打造易于学习和使用的移动应用程序用户界面。实现这些品质需要对移动用户界面 (MUI) 进行迭代评估。几项研究强调了为吸引最终用户提供令人愉悦的外观和感觉的 MUI 的价值。因此，MUI 需要避免各种结构美学缺陷。这些缺陷表明设计决策不佳，会干扰 MUI 的一致性并使其更难使用。为此，我们提出了一种工具（美学缺陷检测工具（ADDET））来确定 MUI 的结构美学维度。自动化这个过程对设计师评估他们的设计质量很有用。我们的方法由两个模块组成。(1)指标评估是基于对 MUI 的树状结构布局的静态分析。我们使用 15 个几何指标（也称为结构或美学指标）在缺陷触发之前检查各种结构特性。(2)缺陷检测：在确定检测规则时，手动组合指标和缺陷非常耗时且取决于用户。因此，我们将缺陷识别过程视为一个优化问题。我们的目标是自动组合与特定缺陷相关的指标，并优化通过分配权重创建的规则的准确性，代表检测缺陷的指标重要性。我们进行了定量和定性分析，以评估所提出的工具在计算指标和检测缺陷方面的准确性。在以 71% 的准确率评估 MUI 的结构设计问题时，这些发现证实了该工具的可靠性。