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This study introduces an improved adaptive genetic algorithm (IAGA) to enhance automatic test case generation for software structural testing. The IAGA method effectively improves path coverage by dynamically adjusting genetic algorithm parameters to maintain population diversity.

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Area of Science:

  • Software Engineering
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Automatic test case generation is crucial for effective software structural testing.
  • Search-based optimization methods, like genetic algorithms (GAs), are used to address challenges in test case generation.
  • Maintaining population diversity is key for the efficiency of genetic algorithms.

Purpose of the Study:

  • To propose an improved adaptive genetic algorithm (IAGA) for effective automatic test case generation.
  • To enhance the process of generating test cases for achieving path coverage in software.
  • To improve the global optimum searching capability of genetic algorithms in software testing.

Main Methods:

  • Developed an Improved Adaptive Genetic Algorithm (IAGA).
  • Implemented dynamic adjustment of crossover and mutation rates based on individual similarity and fitness values.
  • Maintained population diversity throughout the optimization process.

Main Results:

  • The IAGA demonstrated efficiency in generating test cases for path coverage.
  • Experimental results on benchmark and industrial programs validated the proposed method's effectiveness.
  • The adaptive strategy enhanced the exploitation of searching for the global optimum.

Conclusions:

  • The proposed IAGA is an efficient method for automatic test case generation in software structural testing.
  • Maintaining population diversity through adaptive parameter adjustment is beneficial for optimization.
  • The IAGA approach contributes to improved path coverage in software testing.