AdapTV+: enhancing model-based test adaptation for smart TVs through icon recognition

Official URL: https://dx.doi.org/10.1109/PRDC59308.2023.00033

Our previous approach to test adaptation in smart TVs had a limitation in handling UI elements that lack associated text. To address this issue, we created a dataset and trained a classifier to label frequently used icons found in smart TVs, which can be recognized without an associated text. In cases where a UI element lacks associated text, we utilize the classifier to label the element, enabling the identification of "equivalent states". This paper presents the development, training process, and potential impact of this classifier on our test adaptation technique. Evaluation results show that our classifier significantly improves metrics like success rate, adaptation rate, and test length overhead. Our proposed methodology, combined with the trained classifier, offers practical solutions to enhance test adaptation processes in smart TVs.