Christopher Peplin

Informed mobile prefetching

Prefetching is a double-edged sword. It can hide the latency of data transfers over poor and intermittently connected wireless networks, but the costs of prefetching in terms of increased energy and cellular data usage are potentially substantial, particularly for data prefetched incorrectly. Weighing the costs and benefits of prefetching is complex, and consequently most mobile applications employ simple but sub-optimal strategies. Rather than leave the job to applications, we argue that the underlying mobile system should provide explicit prefetching support. Our prototype, IMP, presents a simple interface that hides the complexity of the prefetching decision. IMP uses a cost-benefit analysis to decide when to prefetch data. It employs goal-directed adaptation to try to minimize application response time while meeting budgets for battery lifetime and cellular data usage. IMP opportunistically uses available networks while ensuring that prefetches do not degrade network performance for foreground activity. It tracks hit rates for past prefetches and accounts for network-specific costs in order to dynamically adapt its prefetching strategy to both the network conditions and the accuracy of application prefetch disclosures. Experiments with email and news reader applications show that IMP provides predictable usage of budgeted resources, while lowering application response time compared to the oblivious strategies used by current applications.

AMC: verifying user interface properties for vehicular applications

Vehicular environments require continuous awareness of the road ahead. It is critical that mobile applications used in such environments (e.g., GPS route planners and location-based search) do not distract drivers from the primary task of operating the vehicle. Fortunately, a large body of research on vehicular interfaces provides best practices that mobile application developers can follow. However, when we studied the most popular vehicular applications in the Android marketplace, no application followed these guidelines. In fact, vehicular applications were not substantially better at meeting best practice guidelines than non-vehicular applications. To remedy this problem, we have developed a tool called AMC that uses model checking to automatically explore the graphical user interface (GUI) of Android applications and detect violations of vehicular design guidelines. AMC is designed to give developers early feedback on their application GUI and reduce the amount of time required by a human expert to assess an applications suitability for vehicular usage. We have evaluated AMC by comparing the violations that it reports with those reported by an industry expert for 12 applications. AMC generated a definitive assessment for 85% of the guidelines checked; for these cases, it had no false positives and a false negative rate of under 2%. For the remaining 15% of cases, AMC reduced the number of application screens that required manual verification by 95%.