Hybrid Approach for Effective Testdata Trade-Off for Software Testing 12+

📗 Testing is a process used to identify quality of developed computer software.One of the important activity in testing environment is automatic test case generation, independent of the way a given software system is designed. This project presents a fusion approach in functional testing for generating test cases using genetic neural networks. The system is aimed to carry out the test data generation process for functional testing. The paper explains how the method can be used to produce a set of test cases covering the most common functional existing in software automatically. Test case inputs are generated randomly and the valid inputs are selected for the proper output. The association rule mining techniques are used to validate the generated data sets. The genetic algorithm is used to generate data values for the test cases. The testing process should be done in a way to scrutinize the faults still. The generated test data is validated arithmetically. This approach is applicable for systems in which large amount of input/output data is available.