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AS400-DET: Detection using Deep Learning Model for IBM i (AS/400)

Created by
  • Haebom

Author

Thanh Tran, Son T. Luu, Quan Bui, Shoshin Nomura

Outline

This paper proposes an automatic GUI component detection method for IBM i systems (AS/400). We present a human-annotated dataset consisting of 1,050 system screen images (including 381 Japanese IBM i system screen screenshots). Each image contains multiple components, including text labels, text boxes, options, tables, instructions, keyboards, and command lines. We developed a detection system based on a state-of-the-art deep learning model and evaluated various approaches using our own dataset. Experimental results demonstrate the effectiveness of the dataset in building a system for detecting components on GUI screens. AS400-DET has the potential to automatically detect GUI components on screens and perform automated testing on systems operating through GUI screens.

Takeaways, Limitations

Takeaways:
Proposing an automatic detection method for IBM i system GUI components and building a dataset.
Presenting the possibility of performing automated GUI-based system testing.
Use of various images, including Japanese IBM i system screenshots.
Limitations:
Lack of information about the architecture and details of specific deep learning models.
Lack of specific information on actual automated test applications and performance of AS400-DET.
Further analysis of the bias and generalizability of the dataset is needed.
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