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Design Example: Resistive Touchscreen01:14

Design Example: Resistive Touchscreen

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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...
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Mobile-UI-Repair: a deep learning based UI smell detection technique for mobile user interface.

Asif Ali1, Yuanqing Xia1,2, Qamar Navid1

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

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|June 10, 2024
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Summary

Mobile-UI-Repair (M-UI-R) identifies and locates graphical user interface (GUI) bugs in mobile apps. This automated approach improves efficiency and accuracy in detecting UI design smells and display issues.

Keywords:
Deep learningMachine learningMobile app reviewsMobile applicationSmell detectionSoftware engineeringUI bugsUI estheticsUI smell detectionUser feedback

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Area of Science:

  • Computer Science
  • Software Engineering
  • Human-Computer Interaction

Background:

  • Mobile application graphical user interfaces (GUIs) are critical for user interaction.
  • Identifying UI design smells and bugs manually is time-consuming and inefficient.
  • Existing automated approaches lack performance and struggle with design guidelines and semantic information.

Purpose of the Study:

  • To propose an automated approach for identifying and localizing UI bugs in mobile applications.
  • To address the limitations of manual testing and existing automated methods.
  • To improve the efficiency and accuracy of mobile app quality assurance.

Main Methods:

  • Developed Mobile-UI-Repair (M-UI-R), an approach for recognizing GUI display issues and pinpointing bug locations.
  • Trained and tested M-UI-R on historical data and validated it on real-time data.
  • Evaluated performance using precision and recall metrics for both detection and localization tasks.

Main Results:

  • M-UI-R achieved 87.7% average precision and 86.5% average recall in detecting UI display issues.
  • M-UI-R achieved 71.5% average precision and 70.7% average recall in localizing UI design smells.
  • Developer surveys confirmed M-UI-R's value in supporting UI enhancement and bug fixing.

Conclusions:

  • M-UI-R effectively identifies and localizes UI bugs in mobile applications.
  • The proposed approach offers significant improvements over manual testing and existing methods.
  • M-UI-R aids developers in enhancing mobile app user interfaces and fixing bugs efficiently.