It is crucial to make software, with its ever-growing influence on everyday lives, accessible to all, including people with disabilities. Despite promoting software accessibility through government regulations, development guidelines, tools and frameworks, investigations reveal a marketplace of inaccessible web and mobile applications. To better understand the limitations of contemporary software industry in adopting accessibility practices, it is necessary to construct a holistic view that combines the perspectives of software practitioners, stakeholders and end users. In this paper, we collect 637 conversations from Twitter to synthesize and qualitatively analyze discussions posted about software accessibility. Our findings observe an active community that provides feedback on inaccessible software, shares personal accounts of development practices and advocates for inclusivity. By perceiving software accessibility from process, profession and people viewpoints, we present current conventions, challenges and possible resolutions with four emergent themes: cost and incentives, awareness and advocacy, technology and resources, and integration and inclusion.

For 15% of the world population with disabilities, accessibility is arguably the most critical software quality attribute. The growing reliance of users with disability on mobile apps to complete their day-to-day tasks further stresses the need for accessible software. Mobile operating systems, such as iOS and Android, provide various integrated assistive services to help individuals with disabilities perform tasks that could otherwise be difficult or not possible. However, for these assistive services to work correctly, developers have to support them in their app by following a set of best practices and accessibility guidelines. Text Scaling Assistive Service (TSAS) is utilized by people with low vision, to increase the text size and make apps accessible to them. However, the use of TSAS with incompatible apps can result in unexpected behavior introducing accessibility barriers to users. This paper presents approach, an automated testing technique for text accessibility issues arising from incompatibility between apps and TSAS. As a first step, we identify five different types of text accessibility by analyzing more than 600 candidate issues reported by users in (i) app reviews for Android and iOS, and (ii) Twitter data collected from public Twitter accounts. To automatically detect such issues, approach utilizes the UI screenshots and various metadata information extracted using dynamic analysis, and then applies various heuristics informed by the different types of text accessibility issues identified earlier. Evaluation of approach on 30 real-world Android apps corroborates its effectiveness by achieving 88.27% precision and 95.76% recall on average in detecting text accessibility issues.

For 15% of the world population with disabilities, accessibility is arguably the most critical software quality attribute. The ever-growing reliance of users with disability on mobile apps further underscores the need for accessible software in this domain. Existing automated accessibility assessment techniques primarily aim to detect violations of predefined guidelines, thereby produce a massive amount of accessibility warnings that often overlook the way software is actually used by users with disability. This paper presents a novel, high-fidelity form of accessibility testing for Android apps, called Latte, that automatically reuses tests written to evaluate an app’s functional correctness to assess its accessibility as well. Latte first extracts the use case corresponding to each test, and then executes each use case in the way disabled users would, i.e., using assistive services. Our empirical evaluation on real-world Android apps demonstrates Latte’s effectiveness in detecting substantially more useful defects than prior techniques.

Mobile apps are an integral component of our daily life. Ability to use mobile apps is important for everyone, but arguably even more so for approximately 15% of the world population with disabilities. This paper presents the results of a large-scale empirical study aimed at understanding accessibility of Android apps from three complementary perspectives. First, we analyze the prevalence of accessibility issues in over 1, 000 Android apps. We find that almost all apps are riddled with accessibility issues, hindering their use by disabled people. We then investigate the developer sentiments through a survey aimed at understanding the root causes of so many accessibility issues. We find that in large part developers are unaware of accessibility design principles and analysis tools, and the organizations in which they are employed do not place a premium on accessibility. We finally investigate user ratings and comments on app stores. We find that due to the disproportionately small number of users with disabilities, user ratings and app popularity are not indicative of the extent of accessibility issues in apps. We conclude the paper with several observations that form the foundation for future research and development.

Android platform provisions a number of sophisticated concurrency mechanisms for the development of apps. The concurrency mechanisms, while powerful, are quite difficult to properly master by mobile developers. In fact, prior studies have shown concurrency issues, such as event-race defects, to be prevalent among real-world Android apps. In this paper, we propose a flow-, context-, and thread-sensitive static analysis framework, called ER Catcher, for detection of event-race defects in Android apps. ER Catcher introduces a new type of summary function aimed at modeling the concurrent behavior of methods in both Android apps and libraries. In addition, it leverages a novel, statically constructed Vector Clock for rapid analysis of happens-before relations. Altogether, these design choices enable ER Catcher to not only detect event-race defects with a substantially higher degree of accuracy, but also in a fraction of time compared to the existing state-of-the-art technique.

Over the past few years, researchers have proposed various program analysis tools for automated detection of event-race conditions in Android. However, to this date, it is not clear how these tools compare to one another, as they have been evaluated on arbitrary, disjointed set of Android apps, for which there is no ground truth, i.e., verified set of event races. To fill this gap and support future research in this area, we introduce BenchERoid, a set of 34 Android apps with injected event-race bugs. The current version of benchmark contains 36 types of event-race bugs that were identified by analyzing Android concurrency literature and publicly available issue repositories. We believe that our framework is a valuable resource for both developers and researchers interested in concurrency bug analysis in Android. BenchERoid is publicly available at: https://github.com/seal-hub/bencheroid.