Research On Automatic Generation Of Test Cases Based On Chaotic Genetic Algorithm

The growing demand for software has led to an increase in the scale of software,and the importance of software testing for health software systems has become more apparent.At the same time,the cost of software testing is very high,usually consuming half of the total financial resources of the entire project development.The current software testing process is mainly manual,but experiments show that software testing automation can significantly reduce the total cost of software testing and software development,thus has become a hot field for scientific research,and the automatic generation of test cases is an important part of software testing.A number of automated test case generation methods have been explored as an effective way to generate automated test cases that can be searched using a series of intelligent optimization algorithms.In this paper,the most commonly genetic algorithm is used,and correspondingly improve the defects of the number of iterations caused by the wave-understanding phenomenon in the late stage of genetic algorithm and easy to fall into the local optimal solution,it has been expected to improve the accuracy of the genetic algorithm and the efficiency of testing software.In the background of automatic generation of software test cases,this paper studies the application of genetic algorithm in this aspect,focuses on the analysis of the automatic generation of test cases for software test path-oriented,and proposes a test case generation method of chaotic genetic algorithm.The method of this paper has a certain dependence on the selection of the initial population for the traditional genetic algorithm.The initial population is closer to the optimal solution and can get better results.The initial population generation strategy based on reverse learning is used to optimize the initial population.The initial population is closer to the optimal solution.At the same time,the design of the fitness function of the genetic algorithm does not fully consider the parallel ability of the algorithm.The individual evaluation function for all target paths is designed to improve the efficiency of the genetic algorithm.Then the chaotic sequence is used to operate.The crossover and mutation process of genetic algorithm improves the global optimization ability of the algorithm and avoids falling into local convergence(premature).Finally,a comparative experiment is designed to prove the effectiveness of the initial population generation strategy based on reverse learning,the improved fitness function and the chaotic mutation(cross)method.The method is applied to specific engineering experiments and compared with basic genetic algorithms and other improved genetic algorithms in recent years.The experimental results show that the improved chaotic genetic algorithm improves the efficiency of the algorithm by 10.8% while improving the coverage of the code and more adaptive genetic algorithm increased by 8.49%.This method is superior to the traditional algorithm in the generation of test cases,which improves the efficiency of the algorithm and the coverage of the target path.,has a role in the field of test case generation.