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Code smells analysis for android applications and a solution for less battery consumption.

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  • 1Department of Computer Science and Engineering, Amity University Uttar Pradesh, Noida, India.

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Summary
This summary is machine-generated.

Refactoring Android code smells like No Low Memory Resolver and Slow Loop significantly reduces battery consumption. This study offers developers a method to improve efficiency and contribute to green mobile energy.

Keywords:
Android code smellsGreen energyMachine-learningMulti-linear regressionRefactoringRobust statisticsSoftware energy model

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Area of Science:

  • Software Engineering
  • Mobile Computing
  • Energy Efficiency

Background:

  • Battery consumption is critical for Android devices, impacting performance and maintainability.
  • Android-specific code smells can negatively affect battery life.
  • Optimizing code is essential for sustainable mobile technology.

Purpose of the Study:

  • Analyze Android code smells and their effect on battery consumption.
  • Develop a mathematical model linking static code metrics, code smells, and battery usage.
  • Provide guidelines for developers to minimize battery drain in Android applications.

Main Methods:

  • Studied three code smells: No Low Memory Resolver (NLMR), Slow Loop (SL), and Unclosed Closable.
  • Analyzed 4,165 classes from 16 Android applications.
  • Employed rule-based classification and multi-linear regression (MLR) modeling.

Main Results:

  • Refactoring identified code smells demonstrably reduces battery consumption.
  • The refactoring method achieved a cumulative accuracy of 87.47%.
  • MLR models showed R-square values of 0.76 for NLMR and 0.668 for SL.

Conclusions:

  • Refactoring specific Android code smells is effective in minimizing battery usage.
  • The study provides a framework for developers to enhance Android code efficiency.
  • Findings support the development of energy-efficient mobile applications and contribute to green energy initiatives.