Adapting RICO Dataset for Boosting Graphical User Interface Component Classification for Automated Android Testing

User Interface testing is one of the most used routines for Android feature quality verification. Regarding this, several industrial and academic solutions are available for Graphical User Interface component identification. However, the state-of-art works are still limited respecting the variety of widgets that can recognize. Also, most solutions use application metadata that can vary across different releases and device models, affecting the portability of automations. Research that approach widget classification using Machine Learning based solutions commonly use a large Android screen capture dataset called RICO. However, several annotations problems have been recurrently pointed on this dataset. In this work, we propose a selection of data cleaning and balancing techniques for removing noisy samples and leveling the number of samples per class on RICO dataset. We used a custom dataset with an extended number of classes (106) when comparing with the state-of-art works that approach widget classification with 15 classes on average. Finally, we showed that using these techniques to improve the quality of data can improve the accuracy when training Machine Learning models like Convolutional Neural Networks and eXtreme Gradient Boosting.