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An Epidemiology-inspired Large-scale Analysis of Android App Accessibility

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Published:21 April 2020Publication History
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Abstract

Accessibility barriers in mobile applications (apps) can make it challenging for people who have impairments or use assistive technology to use those apps. Ross et al.’s epidemiology-inspired framework emphasizes that a wide variety of factors may influence an app's accessibility and presents large-scale analysis as a powerful tool for understanding the prevalence of accessibility barriers (i.e., inaccessibility diseases). Drawing on this framework, we performed a large-scale analysis of free Android apps, exploring the frequency of accessibility barriers and factors that may have contributed to barrier prevalence. We tested a population of 9,999 apps for seven accessibility barriers: few TalkBack-focusable elements, missing labels, duplicate labels, uninformative labels, editable TextViews with contentDescription, fully overlapping clickable elements, and undersized elements. We began by measuring the prevalence of each accessibility barrier across all relevant element classes and apps. Missing labels and undersized elements were the most prevalent barriers. As a measure of the spread of barriers across apps, we assessed the five most reused classes of elements for missing labels and undersized elements. The Image Button class was among the most barrier-prone of the high reuse element classes; 53% of Image Button elements were missing labels and 40% were undersized. We also investigated factors that may have contributed to the high barrier prevalence in certain classes of elements, selecting examples based on prior knowledge, our analyses, and metrics of reuse and barrier-proneness. These case studies explore: (1) how the few TalkBack-focusable elements accessibility barrier relates to app category (e.g., Education, Entertainment) and the tools used to implement an app, (2) the prevalence of label-based barriers in image-based buttons, (3) design patterns that affect the labeling and size of Radio Buttons and Checkboxes, and (4) accessibility implications of the sizing of third-party plug-in elements. Our work characterizes the current state of Android accessibility, suggests improvements to the app ecosystem, and demonstrates analysis techniques that can be applied in further app accessibility assessments.

References

  1. Adobe. Adobe AIR. Retrieved on 17 April, 2019 from https://get.adobe.com/air.Google ScholarGoogle Scholar
  2. Kevin Allix, Tegawendé F. Bissyandé, Jacques Klein, and Yves Le Traon. 2016. AndroZoo: Collecting millions of Android apps for the research community. In Proceedings of the 13th International Workshop on Mining Software Repositories (MSR’16). 468--471. DOI:http://doi.org/10.1145/2901739.2903508Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Android Open Source. Making Applications Accessible. Retrieved on 17 April, 2019 from http://developer.android.com/guide/topics/ui/accessibility/apps.html.Google ScholarGoogle Scholar
  4. Android Open Source Project. Accessibility Developer Checklist. Retrieved on 17 April, 2019 from http://developer.android.com/guide/topics/ui/accessibility/checklist.html#requirements.Google ScholarGoogle Scholar
  5. “Android Open Source Project.” FloatingActionButtonBasic | Android Developers. Retrieved on 17 April, 2019 from https://developer.android.com/samples/FloatingActionButtonBasic/res/layout/fab_layout.html.Google ScholarGoogle Scholar
  6. “Android Open Source Project.” ImageButton | Android Developers. Retrieved on 17 April, 2019 from https://developer.android.com/reference/android/widget/ImageButton.html.Google ScholarGoogle Scholar
  7. “Android Open Source Project.” ImageView | Android Developers. Retrieved on 17 April, 2019 from https://developer.android.com/reference/android/widget/ImageView.html.Google ScholarGoogle Scholar
  8. Android Open Source Project. Improve Your Code with Lint. Retrieved on 17 April, 2019 from https://developer.android.com/studio/write/lint.html.Google ScholarGoogle Scholar
  9. “Android Open Source Project.” Making Apps More Accessible | Android Developers. Retrieved on 17 April, 2019 from https://developer.android.com/guide/topics/ui/accessibility/apps.html.Google ScholarGoogle Scholar
  10. Apache Cordova. Retrieved on 3 April, 2019 from https://cordova.apache.org/.Google ScholarGoogle Scholar
  11. AppCompatImageView | Android Developers. Retrieved on 30 April, 2019 from https://developer.android.com/reference/android/support/v7/widget/AppCompatImageView.Google ScholarGoogle Scholar
  12. Apple Accessibility Scanner. Retrieved on 17 April, 2018 from https://developer.apple.com/library/content/documentation/Accsssibility/Conceptual/AccessibilityMacOSX/OSXAXTestingApps.html.Google ScholarGoogle Scholar
  13. Apple Inc. 2012. Accessibility Programming Guide for iOS. Retrieved on 14 July, 2018 from https://developer.apple.com/library/ios/documentation/UserExperience/Conceptual/iPhoneAccessibility/Introduction/Introduction.html.Google ScholarGoogle Scholar
  14. Lucas Pedroso Carvalho, Bruno Piovesan Melchiori Peruzza, Flávia Santos, Lucas Pereira Ferreira, and André Pimenta Freire. 2016. Accessible smart cities?: Inspecting the accessibility of Brazilian municipalities’ mobile applications. In Proceedings of the 15th Brazilian Symposium on Human Factors in Computer Systems (IHC’16). DOI:http://doi.org/10.1145/3033701.3033718Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Raphael Clegg-Vinell, Christopher Bailey, and Voula Gkatzidou. 2014. Investigating the Appropriateness and Relevance of Mobile Web Accessibility Guidelines. In Proceedings of the International Cross-disciplinary Conference on Web Accessibility (W4A’14). 1--4. DOI:http://doi.org/10.1145/2596695.2596717Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Cocos2d-x - World's #1 Open-Source Game Development Platform. Retrieved on 30 April, 2019 from https://cocos2d-x.org.Google ScholarGoogle Scholar
  17. Michael Cooper, Peter Korn, Andi Snow-Weaver, Gregg Vanderheiden, Loïc Martínez Normand, and Mike Pluke. 2013. Guidance on Applying WCAG 2.0 to Non-Web Information and Communications Technologies (WCAG2ICT). Retrieved from http://www.w3.org/TR/wcag2ict/.Google ScholarGoogle Scholar
  18. “Data Driven Design Group.” Rico: A Mobile App Dataset of Building Data-Driven Design Applications. Retrieved on 17 April, 2019 from http://interactionmining.org/rico.Google ScholarGoogle Scholar
  19. Biplab Deka, Zifeng Huang, Chad Franzen, Joshua Hibschman, Daniel Afergan, Yang Li, Jeffrey Nichols, and Ranjitha Kumar. 2017. Rico: A mobile app dataset for building data-driven design applications. In Proceedings of the 30th ACM Symposium on User Interface Software and Technology (UIST’17). 845--854. DOI:http://doi.org/10.1145/3126594.3126651Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Biplab Deka, Zifeng Huang, and Ranjitha Kumar. 2016. ERICA: Interaction mining mobile apps. In Proceedings of the ACM Symposium on User Interface Software and Technology (UIST’16). 767--776. DOI:http://doi.org/10.1145/2984511.2984581Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Trinh-Minh-Tri Do and Daniel Gatica-Perez. 2010. By their apps you shall understand them: Mining large-scale patterns of mobile phone usage. In Proceedings of the 9th International Conference on Mobile and Ubiquitous Multimedia (MUM’10) 1--10. DOI:http://doi.org/10.1145/1899475.1899502Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Google. 2016. Accessibility Scanner. Retrieved from https://play.google.com/store/apps/details?id=com.google.android.apps.accessibility.auditor.Google ScholarGoogle Scholar
  23. Google. 2015. Accessibility test framework for android. Retrieved from https://github.com/google/Accessibility-Test-Framework-for-Android.Google ScholarGoogle Scholar
  24. Google. Android Accessibility Developer Guidelines. Retrieved on 28 August, 2015 from https://developer.android.com/guide/topics/ui/accessibility.Google ScholarGoogle Scholar
  25. Darren Guinness, Edward Cutrell, and Meredith Ringel Morris. 2018. Caption crawler: Enabling reusable alternative text descriptions using reverse image search. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI’18). 1--11. DOI:http://doi.org/10.1145/3173574.3174092Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Vicki L. Hanson and John T. Richards. 2013. Progress on website accessibility? ACM Trans. Web 7, 1 (2013), 1--30. DOI:http://doi.org/10.1145/2435215.2435217Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Shuai Hao, Bin Liu, Suman Nath, William G. J. Halfond, and Ramesh Govindan. 2014. PUMA: Programmable UI-Automation for Large-Scale Dynamic Analysis of Mobile Apps *. DOI:http://doi.org/10.1145/2594368.2594390Google ScholarGoogle Scholar
  28. Shaun K. Kane, Jessie A. Shulman, Timothy J. Shockley, and Richard E. Ladner. 2007. A web accessibility report card for top international university web sites. In Proceedings of the International Cross-disciplinary Conference on Web Accessibility (W4A’07). 148. DOI:http://doi.org/10.1145/1243441.1243472Google ScholarGoogle Scholar
  29. Lauren R. Milne, Cynthia L. Bennett, and Richard E. Ladner. 2014. The accessibility of mobile health sensors for blind users. In Proceedings of the International Technology and Persons with Disabilities Conference (CSUN’14). 166--175. DOI:http://doi.org/10211.3/133384Google ScholarGoogle Scholar
  30. Israel J. Mojica, Bram Adams, Meiyappan Nagappan, Steffen Dienst, Thorsten Berger, and Ahmed E. Hassan. 2014. A large-scale empirical study on software reuse in mobile apps. IEEE Softw 31, 2 (2014), 78--86. DOI:http://doi.org/10.1109/MS.2013.142Google ScholarGoogle ScholarCross RefCross Ref
  31. Kyudong Park, Taedong Goh, Hyo-Jeong So, Hyo-Jeong Association for Computing Machinery., HCI Society of Korea, and Hanbit Media (Firm). 2014. Toward accessible mobile application design: Developing mobile application accessibility guidelines for people with visual impairment. In Proceedings of the International Conference on Human-Computer Interaction (HCI Korea’14). 478. Retrieved from https://dl.acm.org/citation.cfm?id=2729491.Google ScholarGoogle Scholar
  32. John T. Richards, Kyle Montague, and Vicki L. Hanson. 2012. Web accessibility as a side effect. In Proceedings of the International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS’12). 79. DOI:http://doi.org/10.1145/2384916.2384931Google ScholarGoogle Scholar
  33. Anne Spencer Ross, Xiaoyi Zhang, James Fogarty, and Jacob O. Wobbrock. 2017. Epidemiology as a framework for large-scale mobile application accessibility assessment. In Proceedings of the 19th International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS’17). 2--11. DOI:http://doi.org/10.1145/3132525.3132547Google ScholarGoogle Scholar
  34. Anne Spencer Ross, Xiaoyi Zhang, Jacob O. Wobbrock, and James Fogarty. 2018. Examining image-based button labeling for accessibility in android apps through large-scale analysis. In Proceedings of the ACM SIGACCESS Conference on Computers and Accessibility (ASSETS’18).Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Leandro Coelho Serra, Lucas Pedroso Carvalho, Lucas Pereira Ferreira, Jorge Belimar Silva Vaz, and André Pimenta Freire. 2015. Accessibility evaluation of e-government mobile applications in brazil. Procedia Comput. Sci. 67, 348--357. DOI:http://doi.org/10.1016/J.PROCS.2015.09.279Google ScholarGoogle ScholarCross RefCross Ref
  36. Android Developers. Shrink, Obfuscate, and Optimize Your App. Retrieved on 29 April, 2019 from https://developer.android.com/studio/build/shrink-code.html.Google ScholarGoogle Scholar
  37. Clauirton Siebra, Tatiana Gouveia, Jefte Macedo, Walter Correia, Marcelo Penha, Fabio Silva, Andre Santos, Marcelo Anjos, and Fabiana Florentin. 2015. Usability requirements for mobile accessibility. In Proceedings of the 14th International Conference on Mobile and Ubiquitous Multimedia (MUM’15). 384--389. DOI:http://doi.org/10.1145/2836041.2841213Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Android Developers. Support Different Pixel Densities. Retrieved on 23 April, 2019 from https://developer.android.com/training/multiscreen/screendensities.html#TaskUseDP.Google ScholarGoogle Scholar
  39. The Crosswalk Project. Retrieved on 30 April, 2019 from crosswalk-project.org/.Google ScholarGoogle Scholar
  40. Unity. Retrieved on 30 April, 2019 from https://unity.com/.Google ScholarGoogle Scholar
  41. Shunguo Yan and P. G. Ramachandran. 2019. The current status of accessibility in mobile apps. ACM Trans. Access. Comput. 12, 1 (2019), 1--31. DOI:http://doi.org/10.1145/3300176Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. Xiaoyi Zhang, Anne Spencer Ross, Anat Caspi, James Fogarty, and Jacob O. Wobbrock. 2017. Interaction proxies for runtime repair and enhancement of mobile application accessibility. In Proceedings of the International Conference on Human Factors in Computing Systems (CHI’17). 6024--6037. DOI:https://doi.org/10.1145/3025453.3025846Google ScholarGoogle Scholar

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      • Published in

        cover image ACM Transactions on Accessible Computing
        ACM Transactions on Accessible Computing  Volume 13, Issue 1
        March 2020
        121 pages
        ISSN:1936-7228
        EISSN:1936-7236
        DOI:10.1145/3396729
        Issue’s Table of Contents

        Copyright © 2020 ACM

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        Publication History

        • Published: 21 April 2020
        • Accepted: 1 December 2019
        • Revised: 1 November 2019
        • Received: 1 May 2019
        Published in taccess Volume 13, Issue 1

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