Automatic Anomaly Detection in Graphical User Interfaces Using Deep Neural Networks
Matilda Nobliá · KTH Publication Database DiVA (KTH Royal Institute of Technology) · 2019
The automatic detection of code errors is a ubiquitous part of the quality assurance process performed during software development. However, graphical errors that may occur in user interfaces are often detected manually. This report examines if deep neural networks (DNNs), may be used to automatically detect two common types of anomalies present in a graphical user interface. The results point towards this being the case for the particular dataset used in this report.