@inproceedings{c61f2181ce6b432d956e72a9dccbba7e,
title = "Automatic Generation of Component Fault Trees from AADL Models for Design Failure Modes and Effects Analysis",
abstract = "Safety analysis is a crucial process in developing safety-critical systems, allowing the identification of potential design issues that may lead to hazards. Automation of this process has become the focus of research in the critical system domain due to the growing complexity of systems. This paper proposes a Component Fault Trees (CFTs) based Failure Mode and Effects Analysis approach for Architecture Analysis and Design Language (AADL) models. First, we propose a methodology for directly generating CFTs from AADL models to display the overall failure behavior of the system. Then we extend the Error Model Annex Version 2 (EMV2) with DFMEA property to express the assessment criteria of error formally, and conduct Design Failure Mode and Effects Analysis (DFMEA) whose core step is guided by CFTs. We discuss our approach with its tool support and evaluate its applicability in driving the design of safety-critical systems through a case study.",
keywords = "Component Fault Trees, EMV2, Failure Mode and Effect Analysis, Safety Analysis",
author = "Xiongpeng Hu and Jing Liu and Hui Dou and Hongtao Chen and Yuhong Zhang",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 23rd IEEE International Conference on Software Quality, Reliability, and Security, QRS 2023 ; Conference date: 22-10-2023 Through 26-10-2023",
year = "2023",
doi = "10.1109/QRS60937.2023.00060",
language = "英语",
series = "IEEE International Conference on Software Quality, Reliability and Security, QRS",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "550--561",
booktitle = "Proceedings - 2023 IEEE 23rd International Conference on Software Quality, Reliability, and Security, QRS 2023",
address = "美国",
}