Abstract—As energy efficiency has become a key consideration in the engineering of mobile applications, an increasing number of perfective maintenance tasks are concerned with optimizing energy consumption. However, optimizing a mobile application to reduce its energy consumption is non-trivial due to the highly volatile nature of mobile execution environments. Mobile applications commonly run on a variety of mobile devices over mobile networks with divergent characteristics. Therefore, no single, static energy consumption optimization is likely to yield across-the-board benefits, and may even turn to be detrimental in some scenarios. In this paper, we present a novel approach to perfective maintenance of mobile applications to reduce their energy consumption. The maintenance programmer declaratively specifies the suspected energy consumption hotspots in a mobile application. Based on this input, our approach then automatically transforms the application to enable it to offload parts of its functionality to the cloud. The offloading is highly adaptive, being driven by a runtime system that dynamically determines both the state-to-offload and its transfer mechanism based on the execution environment in place. In addition, the runtime system continuously improves its effectiveness due to a feedback-loop mechanism. Thus, our approach flexibly reduces the energy con-sumption of mobile applications behind the scenes. Applying our approach to third-party Android applications has shown that it can effectively reduce the overall amount of energy consumed by these applications, with the actual numbers ranging between 25% and 50%. These results indicate that our approach represents a promising direction in developing pragmatic and systematic tools for the perfective maintenance of mobile applications. I.

Abstract. Unexpected power consumption of smartphone applications is a nuisance because the battery capacity is limited. Such energy bugs (ebugs) are currently detected only at runtime. Some ebugs, however, are desirable to detect at the early stage of the development because they are design faults. This paper proposes a formal model, the power consumption automaton, to account for the power consumption, and discusses how the tool-assisted analysis is conducted.

Abstract—Cloud offloading—leveraging remote cloud-based computing resources to execute energy-intensive functionality— has become a widely-used optimization technique for mobile applications. However, implementing cloud offloading techniques remains a delicate and complex task, reserved for expert pro-grammers. If cloud computing is to realize its promise as a generally applicable, powerful optimization technique for mobile applications, its implementation barrier must be lowered. As we have discovered, reusable system building blocks exposed via a convenient programming model can facilitate the im-plementation of complex cloud offloading optimizations. This paper describes a system architecture for implementing adaptive cloud offloading optimizations. In particular, the architecture features parameterizable building blocks for monitoring and estimating energy consumption and performance efficiency as well as state synchronization across address spaces, which the mobile programmer can use a ̀ la carte. These blocks streamline the implementation procedure for a wide array of adaptive of-floading optimizations. Applying this system architecture to third-party mobile applications has optimized their energy efficiency, depending on the execution environment in place. Index Terms—mobile applications; energy optimization; cloud offloading; programming model; adaptation I.