Several types of apps require accessing user location, including map navigation, food ordering, and fitness tracking apps. To access user location, app developers use frameworks that the underlying platform provides to them. For the iOS platform, the Core Location framework enables developers to configure various services to obtain user location information. But how does a particular configuration affect the energy consumption of an app? The available Core Location framework documentation is insufficient to help developers reason about the tradeoff between choosing a particular configuration and energy consumption. In this paper, we present a set of guidelines that will help developers make an energy-efficient design choice while configuring the Core Location framework for their app. To achieve that, we have created microbenchmark configurations of the various services that the Core Location framework provides. We have then run several test-scenarios on these configurations to extract their energy profiles. To extract energy-efficient guidelines for developers, we have carefully examined those energy profile results. The guidelines show several configurations that not only reduce energy consumption but also access locations more frequently than other configurations. To evaluate those guidelines, we analyzed three real-world apps and a location service sample app provided by Apple. Our results show that the guidelines help reduce energy: 0.42% for a property search app, 10.59% for a weather app, 26.91% for a location utility app, and 11.37% for Apple's sample app. Additionally, our empirical evaluation shows that choosing an energy-hungry configuration can increase the energy consumption by up to a maximum of 23.97%. Our guidelines are effective on 3 real-world apps, and our methodology may be used to extract energy-efficient guidelines for frameworks other than the Core Location framework.

Swift is an open-source programming language and Apple's recommended choice for app development. Given the global widespread use of Apple devices, the ability to analyze Swift programs has significant impact on millions of users. Although static analysis frameworks exist for various computing platforms, there is a lack of comparable tools for Swift. While LLVM and Clang support some analyses for Swift, they are either primarily dynamic analyses or not suitable for deeper analyses of Swift programs such as taint tracking. Moreover, other existing tools for Swift only help enforce code styles and best practices.