Model-based test prioritization heuristic methods and their evaluation
Proceedings of the 3rd international workshop on Advances in model-based testing
Time-aware test-case prioritization using integer linear programming
Proceedings of the eighteenth international symposium on Software testing and analysis
Regression testing with UML software designs: A survey
Journal of Software Maintenance and Evolution: Research and Practice
An effective fault aware test case prioritization by incorporating a fault localization technique
Proceedings of the 2010 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement
Model-based regression test case prioritization
ACM SIGSOFT Software Engineering Notes
Controversy Corner: Improving test efficiency through system test prioritization
Journal of Systems and Software
Regression testing minimization, selection and prioritization: a survey
Software Testing, Verification & Reliability
Regression test suite prioritization using system models
Software Testing, Verification & Reliability
Bridging the gap between the total and additional test-case prioritization strategies
Proceedings of the 2013 International Conference on Software Engineering
| Hi-index | 0.00 |
During regression testing, a modified system is retested using the existing test suite. Because the size of the test suite may be very large, testers are interested in detecting faults in the system as early as possible during the retesting process. 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 paper, 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. Execution of the model is inexpensive as compared to execution of the system, therefore the overhead associated with test prioritization is relatively small. In addition, we present 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). 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.