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Genetic algorithm-based test data generation for multiple paths via individual sharing.

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  • 1College of Science, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China.

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Summary
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Genetic algorithms enhance automated test data generation for software path testing. A novel multi-population approach significantly improves efficiency in achieving multiple path coverage.

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Area of Science:

  • Computer Science
  • Software Engineering

Background:

  • Genetic algorithms (GAs) are increasingly used for automated test data generation.
  • Current GA-based methods face efficiency challenges in achieving comprehensive path testing.

Purpose of the Study:

  • To improve the efficiency of test data generation for multiple path coverage.
  • To address the limitations of existing genetic algorithm approaches in software testing.

Main Methods:

  • A mathematical model for generating test data targeting multiple path coverage was developed.
  • A multi-population genetic algorithm incorporating individual sharing was designed to solve the model.

Main Results:

  • Theoretical analysis confirmed the performance of the proposed method.
  • Experimental application to various programs demonstrated significant efficiency gains.
  • The method effectively improves test data generation for achieving extensive path coverage.

Conclusions:

  • The proposed multi-population genetic algorithm with individual sharing offers a more efficient solution for test data generation.
  • This approach enhances the capability of achieving multiple path coverage in software testing.
  • The findings suggest a promising direction for optimizing automated software testing processes.