An Automated Testing Framework For Smart TV apps Based on Model Separation
This work addresses the problem of efficient and effective testing for smart TV app developers, but it is incremental as it builds on existing model-based testing methods.
The paper tackles the lack of systematic testing for smart TV apps by proposing a model-based approach using model separation to generate test cases based on user preferences, resulting in more comprehensive models with less time and effective fault detection compared to manual testing.
Smart TV application (app) is a new technological software app that can deal with smart TV devices to add more functionality and features. Despite its importance nowadays, far too little attention has been paid to present a systematic approach to test this kind of app so far. In this paper, we present a systematic model-based testing approach for smart TV app. We used our new notion of model separation to use sub-models based on the user preference instead of the exhaustive testing to generate the test cases. Based on the constructed model, we generated a set of test cases to assess the selected paths to the chosen destination in the app. We also defined new mutation operators for smart TV app to assess our testing approach. The evaluation results showed that our approach can generate more comprehensive models of smart TV apps with less time as compared to manual exploratory testing. The results also showed that our approach can generate effective test cases in term of fault detection.