The first challenge to develop an energy efficient application is to measure the application's energy consumption, which requires sophisticated hardware infrastructure and sig nificant amounts of developers' time. Models and tools that estimate software energy consumption can save developers time, as application profiling is much easier and more widely available than hardware instrumentation for measuring software energy consumption. Our work focuses on modelling software energy consumption by using system calls and machine learning tech niques. This system call based model is validated against actual energy measurements from five different Android applications.These results demonstrate that system call counts can successfully model software energy consumption if the idle energy consump tion of an application is estimated or known. In the absence of any knowledge of an application's idle energy consumption, our system call based approach is still useful to compare the energy consumption among different versions of the same application.

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