  Mobile apps provide new opportunities to people with disabilities to act
independently in the world. Motivated by this trend, researchers have conducted
empirical studies by using the inaccessibility issue rate of each page (i.e.,
screen level) to represent the characteristics of mobile app accessibility.
However, there still lacks an empirical investigation directly focusing on the
issues themselves (i.e., issue level) to unveil more fine-grained findings, due
to the lack of an effective issue detection method and a relatively
comprehensive dataset of issues.
  To fill in this literature gap, we first propose an automated app page
exploration tool, named Xbot, to facilitate app accessibility testing and
automatically collect accessibility issues by leveraging the instrumentation
technique and static program analysis. Owing to the relatively high activity
coverage (around 80%) achieved by Xbot when exploring apps, Xbot achieves
better performance on accessibility issue collection than existing testing
tools such as Google Monkey. With Xbot, we are able to collect a relatively
comprehensive accessibility issue dataset and finally collect 86,767 issues
from 2,270 unique apps including both closed-source and open-source apps, based
on which we further carry out an empirical study from the perspective of
accessibility issues themselves to investigate novel characteristics of
accessibility issues. Specifically, we extensively investigate these issues by
checking 1) the overall severity of issues with multiple criteria, 2) the
in-depth relation between issue types and app categories, GUI component types,
3) the frequent issue patterns quantitatively, and 4) the fixing status of
accessibility issues.
