Research On Applying Genetic Algorithm For Automatically Generating Test Case

Software testing is an expensive component of software development and maintenance. A particularly labor-intensive component of this testing process is the generation of test data to satisfy testing requirements. Given a testing requirement, such as an input that will cause execution of a particular statement, test-data generation techniques attempts to find a program input that will satisfy the testing requirement. The automation of test-data generation is an important step in reducing the cost of software development and maintenance.How to generate adequate test cases based on a specification to cover all of the paths in its implementation is a challenge in software testing. In this paper, we describe a novel method for effectively selecting test cases generated from a model-based formal specification. Applying the method, a set of test cases is first generated from the specification, and then a genetic algorithm is repeatedly applied to derive more test cases with stronger capability in terms of achieving better path coverage on the basis of the existing test cases. The principle of genetic algorithm is discussed and an improvement of the genetic algorithm through integration with Tabu list is presented. An experiment is conducted to study how the improved genetic algorithm can be applied and to evaluate its effectiveness. The result shows that the improved genetic algorithm performs better than the standard genetic algorithm.The compile environment in this experiment is Eclipse. And the operation system in this experiment is Windows7, the CPU is Corei7 as well. The different methods compared in these experiment based on the cost of time.