Accessible user interface support for multi-device ubiquitous applications: architectural modifiability considerations

The market for personal computing devices is rapidly expanding from PC, to mobile, home entertainment systems, and even the automotive industry. When developing software targeting such ubiquitous devices, the balance between development costs and market coverage has turned out to be a challenging issue. With the rise of Web technology and the Internet of things, ubiquitous applications have become a reality. Nonetheless, the diversity of presentation and interaction modalities still drastically limit the number of targetable devices and the accessibility toward end users. This paper presents webinos, a multi-device application middleware platform founded on the Future Internet infrastructure. Hereto, the platform's architectural modifiability considerations are described and evaluated as a generic enabler for supporting applications, which are executed in ubiquitous computing environments

Leveraging Program Analysis to Reduce User-Perceived Latency in Mobile Applications

Reducing network latency in mobile applications is an effective way of improving the mobile user experience and has tangible economic benefits. This paper presents PALOMA, a novel client-centric technique for reducing the network latency by prefetching HTTP requests in Android apps. Our work leverages string analysis and callback control-flow analysis to automatically instrument apps using PALOMA's rigorous formulation of scenarios that address "what" and "when" to prefetch. PALOMA has been shown to incur significant runtime savings (several hundred milliseconds per prefetchable HTTP request), both when applied on a reusable evaluation benchmark we have developed and on real applicationsComment: ICSE 201

The Feasibility of Dynamically Granted Permissions: Aligning Mobile Privacy with User Preferences

Current smartphone operating systems regulate application permissions by prompting users on an ask-on-first-use basis. Prior research has shown that this method is ineffective because it fails to account for context: the circumstances under which an application first requests access to data may be vastly different than the circumstances under which it subsequently requests access. We performed a longitudinal 131-person field study to analyze the contextuality behind user privacy decisions to regulate access to sensitive resources. We built a classifier to make privacy decisions on the user's behalf by detecting when context has changed and, when necessary, inferring privacy preferences based on the user's past decisions and behavior. Our goal is to automatically grant appropriate resource requests without further user intervention, deny inappropriate requests, and only prompt the user when the system is uncertain of the user's preferences. We show that our approach can accurately predict users' privacy decisions 96.8% of the time, which is a four-fold reduction in error rate compared to current systems.Comment: 17 pages, 4 figure

Generating Predicate Callback Summaries for the Android Framework

One of the challenges of analyzing, testing and debugging Android apps is that the potential execution orders of callbacks are missing from the apps' source code. However, bugs, vulnerabilities and refactoring transformations have been found to be related to callback sequences. Existing work on control flow analysis of Android apps have mainly focused on analyzing GUI events. GUI events, although being a key part of determining control flow of Android apps, do not offer a complete picture. Our observation is that orthogonal to GUI events, the Android API calls also play an important role in determining the order of callbacks. In the past, such control flow information has been modeled manually. This paper presents a complementary solution of constructing program paths for Android apps. We proposed a specification technique, called Predicate Callback Summary (PCS), that represents the callback control flow information (including callback sequences as well as the conditions under which the callbacks are invoked) in Android API methods and developed static analysis techniques to automatically compute and apply such summaries to construct apps' callback sequences. Our experiments show that by applying PCSs, we are able to construct Android apps' control flow graphs, including inter-callback relations, and also to detect infeasible paths involving multiple callbacks. Such control flow information can help program analysis and testing tools to report more precise results. Our detailed experimental data is available at: http://goo.gl/NBPrKsComment: 11 page