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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.
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 resulting sensory data should be effectively utilized by apps for their users’ benefits. Existing studies described 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 generated sensory data, tracks their propagation and Analyze 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. 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 prepared our approach as a prototype tool CyanD roid, and evaluate 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.