A model-based testing framework with reduced set of test cases for programmable controllers

In testing of programmable controllers, manual selection of test cases is still the most common method in practice. This is however tailor-made, time consuming and error-prone. Traditional model-based methods can hardly handle industrial scale systems which usually possess a significant number of states, and signals of sensors and actuators. In this paper, we propose a model-based testing framework that utilizes simplified plant features to reduce the number of test cases, and at the same time also guarantees a full coverage of nominal behavior of system under test. The proposed framework has been illustrated on a case study.     «

In testing of programmable controllers, manual selection of test cases is still the most common method in practice. This is however tailor-made, time consuming and error-prone. Traditional model-based methods can hardly handle industrial scale systems which usually possess a significant number of states, and signals of sensors and actuators. In this paper, we propose a model-based testing framework that utilizes simplified plant features to reduce the number of test cases, and at the same tim...     »