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Software Defect Prediction Based on Hybrid Swarm Intelligence and Deep Learning.

Zhen Li1,2, Tong Li1,2, YuMei Wu2,3

  • 1School of Electronic and Information, Jiangsu University of Science and Technology, Zhenjiang 212100, China.

Computational Intelligence and Neuroscience
|January 7, 2022
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Summary
This summary is machine-generated.

This study enhances software defect prediction using a novel hybrid deep learning approach. Combining particle swarm and wolf swarm algorithms optimizes model hyperparameters, significantly improving prediction accuracy and testing efficiency.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Software Engineering

Background:

  • Software quality and testing efficiency are critical challenges in software development.
  • Traditional defect prediction methods often struggle with complex models and hyperparameter tuning.
  • Deep learning offers potential but requires effective optimization strategies.

Purpose of the Study:

  • To improve software quality and testing efficiency through advanced defect prediction.
  • To develop a novel hybrid swarm intelligence algorithm for deep learning model optimization.
  • To enhance the performance of deep learning-based software defect prediction models.

Main Methods:

  • Implemented a deep learning model for software defect prediction.
  • Developed a hybrid algorithm by combining particle swarm optimization (PSO) and wolf swarm optimization (WSO).
  • Utilized the hybrid algorithm for hyperparameter optimization, employing the model's loss function as the fitness function.
  • Applied an autoencoder for data preprocessing and performance enhancement.

Main Results:

  • The hybrid algorithm demonstrated superior performance in hyperparameter optimization compared to traditional methods and single swarm intelligence algorithms.
  • Experimental results across six datasets showed significant improvements in prediction accuracy and other key indicators.
  • Autoencoder processing further boosted the overall performance of the defect prediction model.

Conclusions:

  • The proposed hybrid swarm intelligence algorithm effectively optimizes deep learning models for software defect prediction.
  • This approach offers a promising solution for enhancing software quality and testing efficiency.
  • The integration of autoencoders provides an additional performance boost, underscoring the model's robustness.