Research On Automatic Softwaretest Case Generation Based On Genetic Algorithms

Software testing has become more complex and difficult than ever. Being an important measure to assure quality and reliability of software,software testing becomes one of the most important aspects in domestic and abroad software researches.Studies of automatic test case generation can reduce the high cost of manual software testing,free tester from heavy labor and at the same time increase its reliability.So studies of automatic test case generation is of great significance to increase the realization of automatic software testing.The paper takes the research and design for the automated generation of test cases deeply.First,this paper introduces the basic theory of software testing, The genetic algorithm is used as the core algorithm of automatic test case generation based on analysis and comparison of the methods such as random algorithm,symbol executing algorithm, target oriented algorithm,path oriented algorithm and genetic algorithm.Second,based on the study of basic principle of genetic algorithms,this paper focuses on several fitness function for path testing,accesses the performance of fitness functions and improves the fitness function and selection strategy of fitness value. New selection strategy divides population into several groups,uses roulette options in each group unit and generate new individual by the combined effect of individual in the selected group.At last,as an example,we generate testing case for the Program of Triangle Classifier. The results demonstrate that some fitness functions provide better results than others,generating fewer test cases to exercise a given program path.In these studies,the branch predicate and inverse path probability approaches were the best performers.