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相关概念视频

Design Example: Resistive Touchscreen01:14

Design Example: Resistive Touchscreen

305
A device engineer plays a crucial role in designing user interfaces for mobile devices. One such interface is the resistive touchscreen, which fundamentally consists of two metallic layers: a flexible upper layer and a rigid lower layer, separated by a narrow gap. The high resistance between these two layers is a key characteristic of this design.
When a user touches the screen, the two layers make contact at a specific point known as the touchpoint. This contact reduces the resistance between...
305

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移动UI修复:基于深度学习的UI嗅觉检测技术,用于移动用户界面.

Asif Ali1, Yuanqing Xia1,2, Qamar Navid1

  • 1School of Automation, Beijing Institute of Technology, Beijing, China.

PeerJ. Computer science
|June 10, 2024
PubMed
概括

移动UI修复 (M-UI-R) 识别和定位移动应用程序中的图形用户界面 (GUI) 错误. 这种自动化方法提高了检测UI设计气味和显示问题的效率和准确性.

关键词:
深度学习是一种深度学习.机器学习 机器学习移动应用程序评价 移动应用程序评价移动应用程序 移动应用程序嗅觉检测 嗅觉检测 嗅觉检测软件工程 软件工程 软件工程用户界面错误的错误用户界面的美学.用户界面嗅觉检测 嗅觉检测用户反是用户反.

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科学领域:

  • 计算机科学 计算机科学
  • 软件工程 软件工程 软件工程
  • 人与计算机的交互

背景情况:

  • 移动应用程序的图形用户界面 (GUIs) 对用户交互至关重要.
  • 手动识别UI设计的臭味和错误是耗时和低效的.
  • 现有的自动化方法缺乏性能,并与设计准则和语义信息作斗争.

研究的目的:

  • 提出一种自动化的方法来识别和定位移动应用程序中的UI错误.
  • 解决手动测试和现有的自动化方法的局限性.
  • 提高移动应用质量保证的效率和准确性.

主要方法:

  • 开发了移动UI修复 (M-UI-R),一种用于识别GUI显示问题和确定错误位置的方法.
  • 在历史数据上训练并测试M-UI-R,并在实时数据上验证它.
  • 在检测和定位任务中使用精度和回忆指标评估性能.

主要成果:

  • 在检测UI显示问题时,M-UI-R实现了87.7%的平均精度和86.5%的平均回忆.
  • 在定位UI设计气味方面,M-UI-R实现了71.5%的平均精度和70.7%的平均回忆.
  • 开发者调查证实了M-UI-R在支持UI增强和修复错误方面的价值.

结论:

  • M-UI-R有效地识别和定位移动应用程序中的UI错误.
  • 拟议的方法比手动测试和现有方法提供了显著的改进.
  • M-UI-R帮助开发人员提高移动应用程序用户界面和有效修复错误.