Test Data Generation Using Annealing Immune Genetic Algorithm

With the development of software technology and the increase of software project scale, the effect of software testing becomes more and more important. In testing, the selection of test data is a nodus to structure testing. Whether the errors of programs can be detected or not are directly related to whether the test data is right or not. Although some methods are brought out to automatically generate test data, in the practical application there are no perfect solutions because of their localizations. The test data can be gained only by the experiences. This paper focuses on the contrast of several Genetic Algorithms, and the automatically test data generation using Annealing Immune Genetic Algorithm (AIGA).At the beginning, this paper introduces the software testing technology and the method of testing data generation. We introduce the concepts, purpose, principle, classes and testing process, and emphasizes on the various of data generation methods which we have used now, such as random algorithm, Korel algorithm, probing algorithm and so on. Finally, the genetic algorithm is used as the core algorithm of automatic test data generation.Secondly, this paper introduces the basic principle of genetic algorithm simulated annealing genetic algorithm and immune 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 adopts the AIGA as the core of the automatic test data generation. This algorithm uses the expectation of reproduction instead of fitness function, and use annealing temperature to adjust the expectation of reproduction.At last, as an example, we generate testing data for the Program of Triangle Classifier, and do the analyses and compare on the data that get from the experimentation.