Many web applications today use responsive design to adjust the view of web pages to match the screen size of end users. People with disabilities often use an alternative view either due to zooming on a desktop device to enlarge text or viewing within a smaller viewport when using assistive technologies. When web pages are not implemented to correctly adjust the page's content across different screen sizes, it can lead to both a loss of content and functionalities between the different versions. Recent studies show that these reflow accessibility issues are among the most prevalent modern web accessibility issues. In this paper, we present a novel automated technique to automatically detect reflow accessibility issues in web pages for keyboard users. The evaluation of our approach on real-world web pages demonstrated its effectiveness in detecting reflow accessibility issues, outperforming state-of-the-art techniques.

The Web has become an essential part of many people’s daily lives, enabling them to complete everyday and essential tasks online and access important information resources. The ability to navigate the Web via the keyboard interface is critical to people with various types of disabilities. However, modern websites often violate web accessibility guidelines for keyboard navigability. In this paper, we present a novel approach for automatically detecting web accessibility barriers that prevent or hinder keyboard users’ ability to navigate web pages. An extensive evaluation of our technique on real-world subjects showed that our technique was able to detect navigation-based keyboard accessibility barriers in web applications with high precision and recall.

The ability to navigate the Web via the keyboard interface is critical to people with various types of disabilities. However, modern websites often violate web accessibility guidelines for keyboard navigability with respect to web dialogs. In this paper, we present a novel approach for automatically detecting web accessibility bugs that prevent or hinder keyboard users' ability to navigate dialogs in web pages. An extensive evaluation of our technique on real-world subjects showed that our technique is effective in detecting these dialog-related keyboard navigation failures.

Many people with disabilities often struggle to interact with small UI content or comprehend small text displayed on mobile app user interfaces (UIs). To overcome these difficulties, they rely on scaling assistive services to adjust and increase the size of UI content. Unfortunately, recent studies have shown that a large number of mobile apps are not compatible with these services, leading to inconsistencies that can distort the layout of the UIs of these apps. These distortions make it even more troublesome for people with disabilities to use these mobile apps, which defeats the purpose of these scaling assistive services. Existing techniques are limited in terms of repairing these issues. In this paper, we present ScaleFix, a novel approach to automatically repair UI scaling accessibility issues in mobile apps. ScaleFix is the first-ever technique to repair such issues. The evaluation of ScaleFix on real-world Android apps demonstrated its ability to effectively repair UI scaling accessibility issues. Additionally, in a user study with individuals affected by these issues, the results demonstrated that ScaleFix was able to improve the accessibility of mobile UIs without negatively impacting the readability or aesthetics of the UI.

TalkBack is one of the most popular screen readers for the 304 million visually impaired users worldwide to access Android devices. Yet, many popular Android apps lack a proper TalkBack accessible mechanism, which could cause failures that make apps unusable. Existing accessibility related techniques are limited in terms of detecting these issues. In this paper, we present a novel approach for automatically detecting TalkBack interactive accessibility failures that prevent TalkBack users from interacting with core functionalities in Android apps. Our evaluation of our technique on real-world Android apps showed that it is able to detect these TalkBack accessibility failures with high precision and recall.

An increasing number of people are dependent on mobile devices to access data and complete essential tasks. For people with disabilities, mobile apps that violate accessibility guidelines can prevent them from carrying out these activities. Size-Based Inaccessibility is one of the top accessibility issues in mobile applications. These issues make apps difficult to use, especially for older people and people with motor disabilities. Existing accessibility related techniques are limited in terms of helping developers to resolve these issues. In this paper, we present our novel automated approach for repairing Size-Based Inaccessibility issues in mobile applications. Our empirical evaluation showed that our approach was able to successfully resolve 99% of the reported Size-Based Inaccessibility issues and received a high approval rating in a user study of the appearance of the repaired user interfaces.

The keyboard is the most universally supported input method operable by people with disabilities. Yet, many popular websites lack keyboard accessible mechanism, which could cause failures that make the website unusable. In this paper, we present a novel approach for automatically detecting and localizing keyboard accessibility failures in web applications. Our extensive evaluation of our technique on real world web pages showed that our technique was able to detect keyboard failures in web applications with high precision and recall and was able to accurately identify the underlying elements in the web pages that led to the observed problems.

Work in Mobile App Energy Consumption (MAEC) has drawn participants from a broad range of communities such as systems, networking, hardware, testing, analysis and design. This diversity has enriched and informed the research from many different angles. For example, knowledge about the physical properties of batteries (e.g., that their performance is temperature dependent) is necessary to control for con-founding variables during experiments. However, it has also led to a confusing and conflicting mix of terms, names, and expressions as researchers from different domains each attempt to apply their existing terminology or invent new terms to describe various kinds of energy related issues.

In mobile applications, local databases have become an important component, providing mobile users with a responsive and secure service for data access and management. However, using local databases comes with a cost. Studies have shown that they are one of the most resource consuming components on mobile devices. Improper usage of the local database can even severely impact the responsiveness of an application. In this paper, we conducted a literature review and a benchmark study to investigate problematic programming practices with respect to database usage. Our results present a comprehensive overview of the current knowledge about these practices, and introduce new knowledge about the impact of these practices on the resource consumption of mobile applications.

Mobile app developers are able to design sophisticated user interfaces (UIs) that can improve a user's experience and contribute to an app's success. Developers invest in automated UI testing techniques, such as crawlers, to ensure that their app's UIs have a high level of quality. However, UI implementation mechanisms have changed significantly due to the availability of new APIs and mechanisms, such as fragments. In this paper, we study a large set of real-world apps to identify whether the mechanisms developers use to implement app UIs cause problems for those automated techniques. In addition, we examined the changes in these practices over time. Our results indicate that dynamic analyses face challenges in terms of completeness and changing development practices motivate the use of additional analyses, such as static analyses. We also discuss the implications of our results for current testing techniques and the design of new analyses.