Estimating Software Energy Consumption with Machine Learning Approach by Software Performance Feature

Fu, Cuijiao; Qian, Depei; Luan, Zhongzhi

doi:10.1109/cybermatics_2018.2018.00106

Cited by 4 publications

References 12 publications

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Estimating the energy consumption of model-view-controller applications

2023

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For information and communication technology to reach its goal of zero emissions in 2050, power consumption must be reduced, including the energy consumed by software. To develop sustainability-aware software, green metrics have been implemented to estimate the energy consumed by the execution of an application. However, they have a rebound energy consumption effect because they require an application to be executed to estimate the energy consumed after each change. To address this problem, it is necessary to construct energy estimation models that do not require the execution of applications. This work addresses this problem by constructing a green model based on size, complexity and duplicated lines to estimate the energy consumed by model-view-controller applications without their execution. This article defines a model constructed based on 52 applications. The results were accurate in twelve applications, which showed that the joule estimation was very close to reality, avoiding the energy consumed by the execution of applications.

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Estimating the energy consumption of model-view-controller applications

2023

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FPowerTool: A Function-Level Power Profiling Tool

et al. 2019

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A Literature Review of Using Machine Learning in Software Development Life Cycle Stages

Shafiq¹,

Mashkoor

Mayr-Dorn

et al. 2021

IEEE Access

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The software engineering community is rapidly adopting machine learning for transitioning modern-day software towards highly intelligent and self-learning systems. However, the software engineering community is still discovering new ways how machine learning can offer help for various software development life cycle stages. In this article, we present a study on the use of machine learning across various software development life cycle stages. The overall aim of this article is to investigate the relationship between software development life cycle stages, and machine learning tools, techniques, and types. We attempt a holistic investigation in part to answer the question of whether machine learning favors certain stages and/or certain techniques.

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Estimating Software Energy Consumption with Machine Learning Approach by Software Performance Feature

Cited by 4 publications

References 12 publications

Estimating the energy consumption of model-view-controller applications

Estimating the energy consumption of model-view-controller applications

FPowerTool: A Function-Level Power Profiling Tool

A Literature Review of Using Machine Learning in Software Development Life Cycle Stages

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