@inproceedings{a196dcfd0589477b834b38e4da42b485,
title = "PF-ENV: Extending Problem Frames for AI-Enabled System Requirements Environment Modeling",
abstract = "AI models learn patterns and abilities from big data rather than relying on code logic. AI models exhibit a certain tolerance to varying inputs in open environments, enabling AI components to address issues in dynamic environments. However, the complexity of objects and the ever-changing conditions in open environments often result in AI-enabled system failing to meet user expectations regarding performance. Consequently, during the early development stages of AI-enabled system, it is essential to thoroughly consider both their actual environment and the environment required for effective functioning. In response, this paper introduces the PF-ENV, a model of the actual environment, and the required environment of AI-enabled system through extended Problem Frames and model-driven engineering (MDE). The paper demonstrates its application through a case involving the AI-enabled systems in Autopilot. Finally, the practical implementation of PF-ENV demonstrates its effectiveness in modeling the actual environment and environmental requirements.",
keywords = "AI-enabled Systems, Environment Modeling, Meta-model, Problem Frames",
author = "Jian Tu and Juntao Gao and Runkun Zhang and Yilong Yang",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 33rd IEEE International Requirements Engineering Conference Workshops, REW 2025 ; Conference date: 01-09-2025 Through 05-09-2025",
year = "2025",
doi = "10.1109/REW66121.2025.00025",
language = "英语",
series = "Proceedings - 2025 IEEE 33rd International Requirements Engineering Conference Workshops, REW 2025",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "157--164",
booktitle = "Proceedings - 2025 IEEE 33rd International Requirements Engineering Conference Workshops, REW 2025",
address = "美国",
}