research-article

Unblind your apps: predicting natural-language labels for mobile GUI components by deep learning

Authors:

Zhenchang Xing,

Jinshui WangAuthors Info & Claims

ICSE '20: Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering

Pages 322 - 334

https://doi.org/10.1145/3377811.3380327

Published: 01 October 2020 Publication History

Abstract

According to the World Health Organization(WHO), it is estimated that approximately 1.3 billion people live with some forms of vision impairment globally, of whom 36 million are blind. Due to their disability, engaging these minority into the society is a challenging problem. The recent rise of smart mobile phones provides a new solution by enabling blind users' convenient access to the information and service for understanding the world. Users with vision impairment can adopt the screen reader embedded in the mobile operating systems to read the content of each screen within the app, and use gestures to interact with the phone. However, the prerequisite of using screen readers is that developers have to add natural-language labels to the image-based components when they are developing the app. Unfortunately, more than 77% apps have issues of missing labels, according to our analysis of 10,408 Android apps. Most of these issues are caused by developers' lack of awareness and knowledge in considering the minority. And even if developers want to add the labels to UI components, they may not come up with concise and clear description as most of them are of no visual issues. To overcome these challenges, we develop a deep-learning based model, called LabelDroid, to automatically predict the labels of image-based buttons by learning from large-scale commercial apps in Google Play. The experimental results show that our model can make accurate predictions and the generated labels are of higher quality than that from real Android developers.

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Cited By

Swearngin AWu JZhang XGomez ECoughenour JStukenborg RGarg BHughes GHilliard ABigham JNichols J(2024)Towards Automated Accessibility Report Generation for Mobile AppsACM Transactions on Computer-Human Interaction10.1145/3674967Online publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1145/3674967
Yu SFang CLi XLing YChen ZSu Z(2024)Effective, Platform-Independent GUI Testing via Image Embedding and Reinforcement LearningACM Transactions on Software Engineering and Methodology10.1145/3674728Online publication date: 21-Jun-2024
https://dl.acm.org/doi/10.1145/3674728
Yu SFang CZhang QDu MLiu JChen Z(2024)Semi-supervised Crowdsourced Test Report Clustering via Screenshot-Text Binding RulesProceedings of the ACM on Software Engineering10.1145/36607761:FSE(1540-1563)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3660776
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Index Terms

Unblind your apps: predicting natural-language labels for mobile GUI components by deep learning
1. Human-centered computing
  1. Accessibility
    1. Accessibility systems and tools
    2. Empirical studies in accessibility
2. Software and its engineering
  1. Software organization and properties
    1. Extra-functional properties
      1. Software usability

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cover image ACM Conferences

ICSE '20: Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering

June 2020

1640 pages

ISBN:9781450371216

DOI:10.1145/3377811

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Gregg Rothermel
North Carolina State University
,
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KAIST, South Korea

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https://dl.acm.org/doi/10.1145/3674967
Yu SFang CLi XLing YChen ZSu Z(2024)Effective, Platform-Independent GUI Testing via Image Embedding and Reinforcement LearningACM Transactions on Software Engineering and Methodology10.1145/3674728Online publication date: 21-Jun-2024
https://dl.acm.org/doi/10.1145/3674728
Yu SFang CZhang QDu MLiu JChen Z(2024)Semi-supervised Crowdsourced Test Report Clustering via Screenshot-Text Binding RulesProceedings of the ACM on Software Engineering10.1145/36607761:FSE(1540-1563)Online publication date: 12-Jul-2024
https://dl.acm.org/doi/10.1145/3660776
Acosta-Salgado LDaviet JJeanson L(2024)Improving Web Accessibility through Artificial Intelligence: A Focus on Image Description Generation: Améliorer l'Accessibilité des Sites Web grâce à l'Intelligence Artificielle : Focus sur la Génération de Descriptions d'ImagesProceedings of the 35th International Francophone Conference on Human-Computer Interaction10.1145/3650104.3652908(1-13)Online publication date: 25-Mar-2024
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Liu ZChen CWang JChen MWu BHuang YHu JWang Q(2024)Unblind Text Inputs: Predicting Hint-text of Text Input in Mobile Apps via LLMProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642939(1-20)Online publication date: 11-May-2024
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Krishna Vajjala AMansur SJose JMoran KRoychoudhury APaiva AAbreu RStorey M(2024)MotorEase: Automated Detection of Motor Impairment Accessibility Issues in Mobile App UIsProceedings of the IEEE/ACM 46th International Conference on Software Engineering10.1145/3597503.3639167(1-13)Online publication date: 20-May-2024
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Johnson BMenzies T(2024)The Power of Positionality—Why Accessibility? An Interview With Kevin Moran and Arun KrishnavajjalaIEEE Software10.1109/MS.2024.336065041:3(91-94)Online publication date: May-2024
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