Groundhog: An Automated Accessibility Crawler for Mobile Apps

Salehnamadi, Navid; Mehralian, Forough; Malek, Sam

doi:10.1145/3551349.3556905

Cited by 10 publications

(7 citation statements)

References 23 publications

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“…While there is some recent work on Keyboard Accessibility failures in web applications that could affect Motorimpaired users [34], this work does not explicitly target any of the guidelines targeted by MotorEase. The Groundhog [81] tool is also inadvertently able to detect some expanding section closures. The lack of exploration of these guidelines is largely due to the fact that implementing tools that detect when such guidelines are not followed requires new types of automated UI screen understanding.…”

Section: Accessibility Guideline Literature Reviewmentioning

confidence: 99%

“…While the Accessibility issues that MotorEase targets have not been explicitly targeted by past tools, there are two tools which are capable of detecting a subset of the accessibility violations identified by MotorEase. These two baselines are Groundhog [81] and Google Accessibility Scanner [2]. We ran these two tools on the same MotorCheck benchmark used to evaluate MotorEase to keep the comparison fair and consistent, and we report the same metrics for both MotorEase and the baseline techniques.…”

Section: Comparison To Baseline Techniquesmentioning

confidence: 99%

“…The second baseline tool that we compared MotorEase to is the Groundhog Accessibility tool [81]. We worked directly with the authors of the Groundhog tool, who were quite helpful after some initial issues initializing the tool, in order to apply Groundhog to the MotorCheck benchmark.…”

Section: Comparison To Baseline Techniquesmentioning

confidence: 99%

“…Software testing for accessibility aids developers in identifying violations of guidelines set forth by companies and governments [1,13,69,73]. A wide range of research has been carried out to automate this process [25,29,38,69,77,80,81]. We discuss the most closely related approaches below.…”

Section: Accessibility Testingmentioning

confidence: 99%

“…Finally, recently Salehnamadi et.al introduced the Groundhog approach [81], which is an accessibility crawler for mobile apps. Groundhog implements an automated UI crawler that explores an app both with and without assistive services, such as TalkBack, and notes any cases where an action can be performed through traditional touches, but cannot be performed via an assistive service.…”

Section: Accessibility Testingmentioning

confidence: 99%

See 4 more Smart Citations

MotorEase: Automated Detection of Motor Impairment Accessibility Issues in Mobile App UIs

Krishna Vajjala,

Mansur,

Jose

et al. 2024

Proceedings of the IEEE/ACM 46th International Conference on Software Engineering

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Recent research has begun to examine the potential of automatically finding and fixing accessibility issues that manifest in software. However, while recent work makes important progress, it has generally been skewed toward identifying issues that affect users with certain disabilities, such as those with visual or hearing impairments. However there are other groups of users with different types of disabilities that also need software tooling support to improve their experience. As such, this paper aims to automatically identify accessibility issues that affect users with motor-impairments.To move toward this goal, this paper introduces a novel approach, called MotorEase, capable of identifying accessibility issues in mobile app UIs that impact motor-impaired users. Motor-impaired users often have limited ability to interact with touch-based devices, and instead may make use of a switch or other assistive mechanismhence UIs must be designed to support both limited touch gestures and the use of assistive devices. MotorEase adapts computer vision and text processing techniques to enable a semantic understanding of app UI screens, enabling the detection of violations related to four popular, previously unexplored UI design guidelines that support motor-impaired users, including: (i) visual touch target size, (ii) expanding sections, (iii) persisting elements, and (iv) adjacent icon visual distance. We evaluate MotorEase on a newly derived benchmark, called MotorCheck, that contains 555 manually annotated examples of violations to the above accessibility guidelines, across 1599 screens collected from 70 applications via a mobile app testing tool. Our experiments illustrate that MotorEase is able to identify violations with an average accuracy of ≈90%, and a false positive rate of less than 9%, outperforming baseline techniques. CCS CONCEPTS• Software and its engineering → Software testing and debugging; Software design engineering.

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Section: Accessibility Guideline Literature Reviewmentioning

confidence: 99%

Section: Comparison To Baseline Techniquesmentioning

confidence: 99%

Section: Comparison To Baseline Techniquesmentioning

confidence: 99%