Automatic Test Cases Generation using Multistage-Based Genetic Algorithm for Object Oriented Testing

Software testing is a major phase that takes place under the construction of software designing. Basically, testing is a process that assists in the determination of work that it reached to the desired output or not. It generally depends on the validation and verification procedure, whereas in simple terms a software testing process is to discover the bugs, errors, faults of the developed software and manage it. It is also considered as the risk based activity. The testing criterion is different at each level and it is completed in various steps. The life cycle of software testing is composed of various steps as the feasibility study, data gathering and specification, design or framework, unit testing, integration and system testing. At last the maintenance is occurring to finalize the software application. In software engineering several kinds of testing strategies are utilized as black box, white box, regression testing, static, dynamic and so on. There are enormous advantages of software testing. The common advantages are to investigate software quality, access the huge pool for verification, deducted the construction cost, improve the reusability, aimed at the basic competencies, increase the demand of the product, balance the time period for the development of software and boost the competitiveness. But there are also certain vulnerabilities related to the large investments, software tools, training, need of more manpower, most time consuming of test preparations, need of more testing space, hidden errors impact on the entire code and cost. In the proposed work, the performance is reliant on the better way. Test case generation is a procedure to generate software corresponding various test case generations and validate various test cases. So that research work identifies the quality of software. This process also declined the maintenance cost (MC) of a software system. In the proposed architecture design, Multi-stage Genetic algorithm has various benefits as it is highly effective in higher dimensional spaces, more memory efficient and versatile. Basically, Multi-stage GA is applied in several real-time applications as in the text categorization, classification of test cases and regression related issues. In the research work, mutants compare various existing techniques and performance parameters are like as mutants, accuracy rate, time consumption and number of events. The planned approach is best in terms to enhance the accuracy rate and achieved it in a reduced time period. Several techniques are used to compare the number of events fire. So that, the architecture accuracy rate has achieved this based on the number of events. The multistage GA test case is an intelligent approach and supportive to various languages like .Net, Java, C++ and Project Management used in an automatic test case. It helps to improve the quality of software