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This study introduces a novel firefly algorithm enhanced with deep learning for automatic structural test data generation. The improved algorithm achieves better performance in software quality assurance, increasing coverage rates and solution diversity.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Software Engineering

Background:

  • Software testing is crucial for quality assurance.
  • Intelligent optimization algorithms aid automatic test data generation.
  • The standard firefly algorithm has limitations in convergence and accuracy.

Purpose of the Study:

  • To propose a novel firefly algorithm integrated with deep learning for structural test data generation.
  • To enhance the convergence rate and accuracy of the firefly algorithm.
  • To improve software quality assurance through effective test data generation.

Main Methods:

  • Dividing the firefly population into male and female subgroups.
  • Implementing a global search for male fireflies attracted to random female fireflies.
  • Utilizing deep learning for a central firefly to guide local search for female fireflies.
  • Incorporating chaos search near the best firefly in the final stage.

Main Results:

  • The proposed algorithm demonstrates superior performance compared to standard methods.
  • Achieved higher success coverage rates.
  • Reduced coverage time.
  • Increased the diversity of generated solutions.

Conclusions:

  • The deep learning-enhanced firefly algorithm is effective for structural test data generation.
  • This approach offers significant improvements in software testing efficiency and effectiveness.
  • The method provides a promising direction for advanced software quality assurance techniques.