GATS Algorithm Of Test Cases Automatic Generating For An Object-oriented Class

In software testing, it is critical and difficult for software testing to automatically generate test case. Although some methods are brought out to automatically generate test cases, there are no perfect solutions because of their localizations in the practical application. Test cases can only be gained by experiences.Object-oriented software test is an important step in guaranteeing the quality and reliability of object-oriented software. With the fast development of object-oriented software techniques, the traditional means of software testing techniques are highly urgent. In the object-oriented testing, the class testing is indispensable. Based on this, this paper proposes a GATS algorithm for automatic test cases generation for classes.This paper firstly analyzes the object-oriented features to the impact of software testing, compares the object-oriented testing and traditional testing and automatically generates test cases for classes. We compare genetic algorithm and the other search algorithms, analyzes the advantages of genetic algorithm applied to automatic generation of test cases for object-oriented classes. We explain the basic principle and process of genetic algorithm and analyze its advantages and disadvantages. By introducing tabu search algorithm, we describe the basic principle and process of tabu search algorithm and analyze its advantages and disadvantages. And we analyze GATS algorithm.Class object is different from the basic data type, therefore, the paper design a new code of class object and construct the fitness function for the test cases automatic generating of class objects required. Comparison of time complexity, the improved method takes less time and promotes faster convergence of generic algorithms.This paper proposes a GATS algorithm for automatic test cases generation for classes. This algorithm combines optimization of genetic groups and strong hiking ability which tabu search obtains to implement global optimization. We analyze the performance of GATS algorithm and genetic algorithm. The experiment shows GATS algorithm can better meet the function coverage for test cases. This paper focuses on the core technology. The core technology has the encoding of class object, selection operator, crossover operator, mutation operator and tabu operator.Finally, we summarize the full paper and put forward the prospect of the next step.