An LLM-Integrated Framework for Completion, Management, and Tracing of STPA
Raeisdanaei, Ali, Kim, Juho, Liao, Michael, Kochhar, Sparsh
–arXiv.org Artificial Intelligence
In many safety-critical engineering domains, hazard analysis techniques are an essential part of requirement elicitation. Of the methods proposed for this task, STPA (System-Theoretic Process Analysis) represents a relatively recent development in the field. The completion, management, and traceability of this hazard analysis technique present a time-consuming challenge to the requirements and safety engineers involved. In this paper, we introduce a free, open-source software framework to build STPA models with several automated workflows powered by large language models (LLMs). In past works, LLMs have been successfully integrated into a myriad of workflows across various fields. Here, we demonstrate that LLMs can be used to complete tasks associated with STPA with a high degree of accuracy, saving the time and effort of the human engineers involved. We experimentally validate our method on real-world STPA models built by requirement engineers and researchers. The source code of our software framework is available at the following link: https://github.com/blueskysolarracing/stpa.
arXiv.org Artificial Intelligence
Mar-15-2025
- Country:
- North America
- United States
- Massachusetts (0.04)
- Florida > Miami-Dade County
- Miami (0.04)
- Colorado > El Paso County
- Colorado Springs (0.04)
- California > Sacramento County
- Sacramento (0.04)
- Canada > Ontario
- Toronto (0.04)
- United States
- Europe
- Monaco (0.04)
- France (0.04)
- Switzerland > Geneva
- Geneva (0.04)
- Germany > Baden-Württemberg
- Stuttgart Region > Stuttgart (0.04)
- Asia > Japan
- Honshū > Kantō > Ibaraki Prefecture > Tsukuba (0.04)
- North America
- Genre:
- Research Report > New Finding (0.93)
- Workflow (0.87)
- Industry:
- Transportation > Air (0.93)
- Automobiles & Trucks (0.93)
- Technology: