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One of the reasons programming mobile systems is so hard is the wide variety of environments a typical app encounters at runtime. As a result, in many cases only post-deployment user testing can determine the right algorithm to use, the rate at which something should happen, or when an app should attempt to conserve energy. Programmers should not be forced to make these choices at development time. Unfortunately, languages leave no way for programmers to express and structure uncertainty about runtime conditions, forcing them to adopt ineffective or fragile ad-hoc solutions.We introduce a new approach based on structured uncertainty through a new language construct: the maybe statement. maybe statements allow programmers to defer choices about app behavior that cannot be made at development time, while providing enough structure to allow a system to later adaptively choose from multiple alternatives. Eliminating the uncertainty introduced by maybe statements can be done in a large variety of ways: through simulation, split testing, user configuration, temporal adaptation, or machine learning techniques, depending on the type of adaptation appropriate for each situation. Our paper motivates the maybe statement, presents its syntax, and describes a complete system for testing and choosing from maybe alternatives.

Smartphones represent the most serious threat to user privacy of any widely-deployed computing technology because these devices are always on and always connected, making them the perfect candidate to know most about the owner. Unfortunately, existing permission models provide smartphone users with limited protection, in part due to the difficulty to users in distinguishing between legitimate and illegitimate use of their data; for example, a mapping app may upload the same location information it uses to download maps (legitimate) to a marketing agency interested in delivering location-based ads (illegitimate). As a result, smartphone users find themselves forced to make burdensome and error-prone trade-offs between app functionality and privacy. To combat this, we propose a new approach called PocketMocker. By allowing substitution of real data streams with artificial or mocked data, PocketMocker allows users to manipulate impressions of their behavior in well-defined ways, such as appearing more fit, more social, or more on-time than they actually are. Instead of focusing on privacy, we explore providing users with better management of their smartphonederived digital identities. We discuss the design of PocketMocker, which uses user-initiated context trace recording and replay to enable objective-driven context mocking. Our evaluation shows that users want to use PocketMocker, that PocketMocker can mock popular smartphone apps, and that PocketMocker is usable.