Study In Automation Generation Of Test Data Based On Simulated Annealing Genetic Algorithm

Software testing is an important measure to assure quality and reliability of software. Software must be tested to ensure the practical application of its normal work. Testing should be able to find any defects in software, at the same time,it should also be efficient and low-cost. However, software testing is a complex process, which need to consume 1/3 time and take 40% cost of entire software development approximately. Therefore, raising the automation level of software testing is very important method to ensure software development quality and reduction software development cost.This paper focuses on automatic test data generation for path testing using genetic algorithms. That is a Program P and a target Path W, D is the set of all possible inputs, the goal is to find input values x (?) D, that will traverse the target path W. It is also an important method to test the structure of programs. Software testing data are some input data which are designed elaborately based on specifications in the software development stages and inner logic of software. The software defects can be detected though inputting test data to run program.Firstly, the paper has introduced the basic theory of software testing and automatic test case generation technology, and presented these method's merits and shortcomings. The paper has explained with emphasis measures the automatic test data generation for test path technology and some realization methods. Studies of automatic test data generation technology can reduce the high manual test cost and the same time decrease huge manpower and enhance the reliability of testing process. The paper summed up the test data automatic generation technologies in recent years, and according to the systematical sum-up of a variety of methods and techniques, extracted the methods and techniques used in this paper.Subsequently, this paper introduces the basic principle of genetic algorithm and simulated annealing genetic algorithm respectively, and analyzes the advantages and shortcomings of each one. Because genetic algorithm runs short of variety and has the problem of precocity, this paper proposes a new algorithm-- Simulated Annealing Genetic Algorithm (SAGA) as the core of the automatic test data generation. This algorithm can speed up the convergence rate and improve the efficiency of the search. The article also introduces the Program Instrumentation based Branch-Cover test cases generating model for path testing.At last,as an example, we generate testing data for the program of software testing. The system can generate the test data for the appointed path accurately, and has better convergence. And by the experiment that is designed in this paper, the advantage of the system is proved.