As mobile applications remain to drive user engagement and digital services, optimizing energy consumption has become an indispensable software engineering goal. Extreme battery drain caused by inefficient application behavior not only degrades user knowledge but also contributes to device overheating and shorter hardware lifecycle. This exploration displayed an incorporated framework for energy-efficient software development, merging static code analysis, dynamic energy profiling, and experimental testing across four Android applications. Using tools such as Android Studio Profiler, Trepn Profiler, Battery Historian, and Jupyter Notebook, the examination recognized and improved important energy anti-patterns, including unreleased wake locks, excessive CPU activity, and recurrent background polling. Post-optimization investigation displayed a 30 - 40% reduction in energy usage, a 20% drop in average CPU load, and a 60 - 70% decrease in wake lock counts. These outcomes were statistically authenticated using paired t-tests, all of which yielded p-values < 0.05, confirming significant improvements. The discoveries reinforce the prominence of participating energy diagnostics into the development lifecycle and provide a practical, reproducible model for building greener mobile applications. The planned procedure authorizes developers to make informed coding decisions, improves runtime efficiency, and aligns software design with global sustainability goals.
| Published in | Journal of Chemical, Environmental and Biological Engineering (Volume 9, Issue 2) |
| DOI | 10.11648/j.jcebe.20250902.15 |
| Page(s) | 83-94 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2025. Published by Science Publishing Group |
Mobile App Optimization, Energy Efficiency, Wake Locks, Trepn Profiler, Android Studio, Dynamic Analysis, Green Software Engineering, CPU Usage, Energy Profiling, Software Sustainability
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APA Style
Adewumi, I. O., Arikeuyo, A. O., Amuda, H., Alao, K. O. (2025). Energy Efficient Software Development for Mobile Applications. Journal of Chemical, Environmental and Biological Engineering, 9(2), 83-94. https://doi.org/10.11648/j.jcebe.20250902.15
ACS Style
Adewumi, I. O.; Arikeuyo, A. O.; Amuda, H.; Alao, K. O. Energy Efficient Software Development for Mobile Applications. J. Chem. Environ. Biol. Eng. 2025, 9(2), 83-94. doi: 10.11648/j.jcebe.20250902.15
AMA Style
Adewumi IO, Arikeuyo AO, Amuda H, Alao KO. Energy Efficient Software Development for Mobile Applications. J Chem Environ Biol Eng. 2025;9(2):83-94. doi: 10.11648/j.jcebe.20250902.15
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Download@article{10.11648/j.jcebe.20250902.15,
author = {Idowu Olugbenga Adewumi and Akeem Olamide Arikeuyo and Hafeez Amuda and Kehinde Oluwaremilekun Alao},
title = {Energy Efficient Software Development for Mobile Applications},
journal = {Journal of Chemical, Environmental and Biological Engineering},
volume = {9},
number = {2},
pages = {83-94},
doi = {10.11648/j.jcebe.20250902.15},
url = {https://doi.org/10.11648/j.jcebe.20250902.15},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jcebe.20250902.15},
abstract = {As mobile applications remain to drive user engagement and digital services, optimizing energy consumption has become an indispensable software engineering goal. Extreme battery drain caused by inefficient application behavior not only degrades user knowledge but also contributes to device overheating and shorter hardware lifecycle. This exploration displayed an incorporated framework for energy-efficient software development, merging static code analysis, dynamic energy profiling, and experimental testing across four Android applications. Using tools such as Android Studio Profiler, Trepn Profiler, Battery Historian, and Jupyter Notebook, the examination recognized and improved important energy anti-patterns, including unreleased wake locks, excessive CPU activity, and recurrent background polling. Post-optimization investigation displayed a 30 - 40% reduction in energy usage, a 20% drop in average CPU load, and a 60 - 70% decrease in wake lock counts. These outcomes were statistically authenticated using paired t-tests, all of which yielded p-values < 0.05, confirming significant improvements. The discoveries reinforce the prominence of participating energy diagnostics into the development lifecycle and provide a practical, reproducible model for building greener mobile applications. The planned procedure authorizes developers to make informed coding decisions, improves runtime efficiency, and aligns software design with global sustainability goals.},
year = {2025}
}
TY - JOUR T1 - Energy Efficient Software Development for Mobile Applications AU - Idowu Olugbenga Adewumi AU - Akeem Olamide Arikeuyo AU - Hafeez Amuda AU - Kehinde Oluwaremilekun Alao Y1 - 2025/12/08 PY - 2025 N1 - https://doi.org/10.11648/j.jcebe.20250902.15 DO - 10.11648/j.jcebe.20250902.15 T2 - Journal of Chemical, Environmental and Biological Engineering JF - Journal of Chemical, Environmental and Biological Engineering JO - Journal of Chemical, Environmental and Biological Engineering SP - 83 EP - 94 PB - Science Publishing Group SN - 2640-267X UR - https://doi.org/10.11648/j.jcebe.20250902.15 AB - As mobile applications remain to drive user engagement and digital services, optimizing energy consumption has become an indispensable software engineering goal. Extreme battery drain caused by inefficient application behavior not only degrades user knowledge but also contributes to device overheating and shorter hardware lifecycle. This exploration displayed an incorporated framework for energy-efficient software development, merging static code analysis, dynamic energy profiling, and experimental testing across four Android applications. Using tools such as Android Studio Profiler, Trepn Profiler, Battery Historian, and Jupyter Notebook, the examination recognized and improved important energy anti-patterns, including unreleased wake locks, excessive CPU activity, and recurrent background polling. Post-optimization investigation displayed a 30 - 40% reduction in energy usage, a 20% drop in average CPU load, and a 60 - 70% decrease in wake lock counts. These outcomes were statistically authenticated using paired t-tests, all of which yielded p-values < 0.05, confirming significant improvements. The discoveries reinforce the prominence of participating energy diagnostics into the development lifecycle and provide a practical, reproducible model for building greener mobile applications. The planned procedure authorizes developers to make informed coding decisions, improves runtime efficiency, and aligns software design with global sustainability goals. VL - 9 IS - 2 ER -
Department of Software Engineering Program, Lead City University, Ibadan, Nigeria
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