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https://hdl.handle.net/2440/136638
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Type: | Conference paper |
Title: | Better Addressing Diverse Accessibility Issues in Emerging Apps: A Case Study using COVID-19 Apps |
Author: | Haggag, O. Grundy, J. Abdelrazek, M. Haggag, S. |
Citation: | Proceedings of the 9th IEEE/ACM International Conference on Mobile Software Engineering and Systems (MobileSoft 2022), 2022, pp.50-61 |
Publisher: | IEEE |
Publisher Place: | Online |
Issue Date: | 2022 |
ISBN: | 9781665490184 |
Conference Name: | IEEE/ACM International Conference on Mobile Software Engineering and Systems (MobileSoft) (17 May 2022 - 18 May 2022 : Pittsburg, USA) |
Statement of Responsibility: | Omar Haggag, John Grundy, Mohamed Abdelrazek, Sherif Haggag |
Abstract: | The COVID-19 pandemic resulted in introducing a large number of “emerging apps" to the mobile app market. These apps were developed and deployed quickly to address the urgency of the situation. This gave us an indication that the cycle of having new emerging apps will likely reoccur in every upcoming emergency in the future e.g. for advice and guidance during bush fires, floods, other pandemics, etc. We carried out an in-depth analysis of user reviews and version history release notes for 30 COVID-19 apps that were developed in a great hurry in 2020. We identified many diverse accessibility issues that exist, not just related to conventional challenged end-user accessibility issues, but including the ability to register, access, download, and use from different app stores in different countries and for different end-users. From this large-scale analysis, we developed a new advisory tool for software developers of emerging apps to avoid many of the wide accessibility issues presented in these COVID-19 apps. A user evaluation of our prototype tool with 13 real-world app developers indicates it will assist developers to address many of these issues prior to initial emerging app deployment. |
Keywords: | Emerging Apps; COVID-19 Apps; Accessibility; Mobile Apps; User Reviews; Version History; Analysis; Evaluation; Recommendations, Guidelines |
Rights: | © 2022 Association for Computing Machinery. |
DOI: | 10.1145/3524613.3527817 |
Grant ID: | http://purl.org/au-research/grants/arc/FL190100035 http://purl.org/au-research/grants/arc/DP200100020 |
Published version: | https://ieeexplore.ieee.org/document/9797313 |
Appears in Collections: | Computer Science publications |
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