The Application Research Of Artificial Intelligence In Automatic Test Case Generation

The problem of automatic test case generation is the basic problem in software testing.It is very important to solve the problem of test case generation for coverage test in software testing.In addition,artificial intelligence technology,as one of the most advanced technologies,can be applied to various fields.It is very feasible and important to apply the artificial intelligence technology to test case generation in software test,and the application of artificial intelligence is to improve the efficiency of testing.In order to solve the problems mentioned above,the technology of machine learning and heuristic search in artificial intelligence is applied to the problem of test case generation.The research contents of this paper are mainly divided into three parts.The first part is based on the technology of artificial neural network in machine learning,and realizes the function of predicting test case generation time.The research firstly determines the factors that influence test case generation are related to code metrics,and then statistics the index through automated testing tools to generate data sets.Finally,the parameters in the model are corrected and confirmed by experiments.The experimental results show that the prediction accuracy of the model for the generation time of test cases is higher than 80%.This strategy can help the tester get the progress of the test ahead of time in the code coverage testing,and understand the needed time for each file and project,so that the tester can grasp the workload of the test.The second part applies the technology of reinforce learning in machine learning,and realizes the automatic generation of test cases by simulating its ability of human making decision by experience.In this method,the process of variable operation in constraint solving is modeled in an abstract way.The model realizes the intelligent solution of constraint set through selecting the appropriate variables and actions to perform operations.The experiment proves the feasibility of the strategy and confirms that the method can generate the correct test case.In addition,the experiment compares the number of variables in test case generation method without reinforcement learning strategy,which proves the feasibility of this strategy in reducing the number of variables operations.The third part is to improve the test case generation framework based on branch and bound.Branch and bound test case generation framework is the application of artificial intelligence search algorithm.In this part,a processing strategy for equality constraint condition in test case generation is proposed.By testing the program containing equality and inequality constraints,we can find that this strategy can reduce the number of interval operations,and significantly improve the efficiency of test case generation.In addition,this strategy can be used to detect the unreachable path caused by the unsolvable equation.The author develops and tests the three strategies based on the code test system(CTS).The purpose of application research is to improve the testing efficiency of CTS.The experiment can verify the feasibility of these three strategies in improving test case generation.