Search for: All records

In software development, many documents (e.g., tutorials for tools and mobile application websites) contain screenshots of graphical user interfaces (GUIs) to illustrate functionalities. Although screenshots are critical in such documents, screenshots can become outdated, especially if document developers forget to update them. Outdated screenshots can mislead users and diminish the credibility of documentation. Identifying screenshots manually is tedious and error-prone, especially when documents are numerous. However, no existing tools are proposed to detect outdated screenshots in GUI documents. To mitigate manual efforts, we propose DOSUD, a novel approach for detecting outdated screenshots. It is challenging to identify outdated screenshots since the differences are subtle and only specific areas are useful to identify such screenshots. To address the challenges, DOSUD automatically extracts and labels screenshots and trains a classification model to identify outdated screenshots. As the first exploration, we focus on Android applications and the most popular IDE, VS Code. We evaluated DOSUD on a benchmark comprising 10 popular applications, achieving high F1-scores. When applied in the wild, DOSUD identified 20 outdated screenshots across 50 Android application websites and 17 outdated screenshots in VS Code documentation. VS Code developers have confirmed and fixed all our bug reports. 

Existing methods for pedestrian motion trajectory prediction are learning and predicting the trajectories in the 2D image space. In this work, we observe that it is much more efficient to learn and predict pedestrian trajectories in the 3D space since the human motion occurs in the 3D physical world and and their behavior patterns are better represented in the 3D space. To this end, we use a stereo camera system to detect and track the human pose with deep neural networks. During pose estimation, these twin deep neural networks satisfy the stereo consistence constraint. We adapt the existing SocialGAN method to perform pedestrian motion trajectory prediction from the 2D to the 3D space. Our extensive experimental results demonstrate that our proposed method significantly improves the pedestrian trajectory prediction performance, outperforming existing state-of-the-art methods.