Abstract: Software testing is accounted to be an important phases in software development life cycle in terms of cost and manpower. Consequently, many studies have been conducted to minimize the associated cost and human effort to fix bugs and errors and to improve the testing processs quality by generating test cases at early stages. However, most of them considered only one type of behavioural diagrams with a lot of human intervention. In this study, an optimized automated approach for generic test case generation was proposed. It is considered as generic in terms of it can be applied on different types of behavioural diagrams (i.e. activity diagram, state diagram, uses case diagram, etc.) for multi-disciplinary domains. While the automation process is used to generate test case with minimum human intervention which will consequently help to minimize total cost. Testing process is considered the key to success of any software. An optimized test case generation approach therefore will be very useful. As a result an optimization technique has been applied to optimize the generated test cases to ensure the quality of results. Accordingly, the proposed approach merges model-based testing with search-based testing to automatically generate test cases from different behavioural diagrams, i.e. use case, activity, etc. Moreover, the proposed approach uses text mining and symbolic execution methodology for test data generation and validation where a knowledge base is developed for multi-disciplinary domains.
Roaa Elghondakly, Sherin Moussa and Nagwa Badr, 2016. An Optimized Approach for Automated Test Case Generation and Validation for Uml Diagrams. Asian Journal of Information Technology, 15: 4276-4290.