Nowadays, the use of IoT devices is essential in most sectors along with a rising concern regarding their energy consumption. Therefore, an accurate estimation of the energy consumption of such devices is required for energy-efficient improvements. This paper presents Energy Measurement Model (EMM), an energy estimation tool for Raspberry Pi 3 Model B+. It is a software-based model used to estimate the local energy consumption of a device by taking into consideration CPU utilization. The error rate of our model averages at 1.25%. Using this model, we study the energy impact of a set of algorithms, programming languages and compilers.
Software testing plays an important role in building quality software and improving maintainability. However, there are no research studies to analyze its impact on energy efficiency. In this paper, we our hypothesis and research questions on the impact of software tests (in particular through coverage metrics) on the energy consumption of software. We also present our experimental methodology and our initial results.
Software testing plays an important role in building quality software and improving maintainability. However, there are no research studies to analyze its impact on energy efficiency. In this paper, we conduct a preliminary study on the impact of unit tests and code coverage on the energy consumption of software. Our empirical study analyzes the energy consumption of multiple JSON libraries and the relation of their energy efficiency to test metrics. Although our study has limitations in the size of the data set, we found that there are hints for a positive correlation between line coverage and energy consumption.
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