AS400-DET: Detection using Deep Learning Model for IBM i (AS/400)
This work addresses automated testing for IBM i GUI systems, which is an incremental improvement in a domain-specific context.
The paper tackles automatic GUI component detection for IBM i systems by introducing a human-annotated dataset of 1,050 screen images and developing a detection system based on deep learning models, demonstrating its effectiveness for component detection.
This paper proposes a method for automatic GUI component detection for the IBM i system (formerly and still more commonly known as AS/400). We introduce a human-annotated dataset consisting of 1,050 system screen images, in which 381 images are screenshots of IBM i system screens in Japanese. Each image contains multiple components, including text labels, text boxes, options, tables, instructions, keyboards, and command lines. We then develop a detection system based on state-of-the-art deep learning models and evaluate different approaches using our dataset. The experimental results demonstrate the effectiveness of our dataset in constructing a system for component detection from GUI screens. By automatically detecting GUI components from the screen, AS400-DET has the potential to perform automated testing on systems that operate via GUI screens.