Cyan Droid: stable and effective energy inefficiency diagnosis for Android apps

• Published in: 中国科学：信息科学（英文版） 2017 (1), p.51-68
• Main Author: Qiwei LI Chang XU Yepang LIU Chun CAO Xiaoxing MA Jian LU
• Format: Article
• Language: English
• ISSN: 1674-733X; 1869-1919

Smartphones are an indispensable part of people’s daily lives. Smartphone apps often use phone sensors to probe their users’ physical environmental conditions to provide services. However, sensing operations can be energy-consumptive, and thus the obtained sensory data should be effectively utilized by apps for their users’ benefits. Existing studies disclosed that many real-world smartphone apps have poor utilization of sensory data, and this causes serious energy waste. To diagnose such energy bugs, a recent technique GreenD roid automatically generates sensory data, tracks their propagation and analyzes their utilization in an app. However,we observe that GreenD roid’s sensory data generation is random and this can negatively affect its stability and effectiveness. Our study reported that GreenD roid might miss energy bugs that require specific sensory data to manifest. To address this problem, we propose a novel approach to systematically generating multi-dimensional sensory data. For effective diagnosis, we also propose to consider app state changes at a finer granularity. We implemented our approach as a prototype tool CyanD roid, and evaluated it using four real-world Android apps and hundreds of their mutants. Our results confirmed that CyanD roid is more stable and effective in energy inefficiency diagnosis for sensory data underutilization issues.