Automated regression test suite prioritization using system models

During regression testing, a modified system is retested using the existing test suite. Since test suites may be very large, developers are interested in detecting faults in the system as early as possible. Test prioritization tries to order test cases for execution so the chances of early detection of faults during retesting are increased. The existing prioritization methods are based on the code of the system. System modeling is a widely used technique to model state-based systems. In this dissertation, we present methods of test prioritization based on state-based models after changes to the model and the system. The model is executed for the test suite and information about model execution is used to prioritize tests. We have performed an experimental study in which we compared different test prioritization methods. The results of the experimental study suggest that system models may improve the effectiveness of test prioritization with respect to early fault detection. In addition, we propose an analytical framework for evaluation of test prioritization methods. This framework may reduce the cost of evaluation as compared to the existing evaluation framework that is based on experimentation (observation).