Risk-driven model tabanlı test

Software is becoming larger and more complex in consumer electronics products. As a result, testing these products for reliability is becoming a major challenge. Traditional and manual testing activities are not effective and efficient in pinpointing faults. Consequently, manual testing activities are being replaced with automated techniques. Modelbased testing is one of these techniques. It uses test models as input and automates test case generation. However, these models are very large for industry-scale systems. Hence, the number of generated test cases can be very large as well. However, it is not feasible to test every functionality of the system exhaustively due to extremely limited resources in the consumer electronics domain. Only those system usage scenarios that are associated with a high likelihood of failures should be tested. Therefore, we propose a risk-driven model-based testing approach in this thesis. Hereby, test models are augmented with information regarding failure risk. Markov chains are used for expressing these models, which are basically composed of states and transitions. Each state transition is annotated with a probability. Probability values are used for generating test cases that cover transitions with the highest probability values. The proposed approach updates transition probability values based on three types of analysis for risk estimation. First, usage profile is used for determining the mostly used features of the system. Second, static analysis is used for estimating fault potential at each state. Third, dynamic analysis is used for estimating error likelihood at each state. Test models are updated based on these analyses and estimations iteratively. The approach is evaluated with three industrial case studies for testing digital TVs, smart phones and washing machines. Results show that the approach increases test efficiency by revealing more faults in less testing time.