TY - GEN
T1 - QuanSafe
T2 - 28th International Conference on Engineering of Complex Computer Systems, ICECCS 2024
AU - Zhu, Yiwei
AU - Liu, Jing
AU - Sun, Haiying
AU - Yin, Wei
AU - Kang, Jiexiang
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - The safety of modern safety-critical systems is increasingly receiving attention. AADL, as an effective modeling language, is widely used for architectural modeling of embedded safety-critical systems. Currently, the main challenges facing the safety analysis of AADL models are the system’s dynamic behavior, state space explosion, rare event prediction, and the lack of explanation of unsatisfied specifications. To address these issues, we propose QuanSafe, a discrete-time Bayesian network (DTBN)-based framework of quantitative safety analysis for AADL models. The dynamic behaviors and temporal features of AADL models can be described entirely using DTBN. Moreover, DTBN can effectively avoid state space explosion and poor performance when dealing with rare events. At the same time, DTBN has the ability of diagnostic analyses, which helps improve the system. QuanSafe provides a complete algorithm to transform AADL models into DTBN models. In addition, it supports multiple automated safety analysis methods with improved metrics. We conduct a case study on the Aircraft System. The experimental results show that our approach has higher efficiency and more comprehensive analysis capabilities than existing research.
AB - The safety of modern safety-critical systems is increasingly receiving attention. AADL, as an effective modeling language, is widely used for architectural modeling of embedded safety-critical systems. Currently, the main challenges facing the safety analysis of AADL models are the system’s dynamic behavior, state space explosion, rare event prediction, and the lack of explanation of unsatisfied specifications. To address these issues, we propose QuanSafe, a discrete-time Bayesian network (DTBN)-based framework of quantitative safety analysis for AADL models. The dynamic behaviors and temporal features of AADL models can be described entirely using DTBN. Moreover, DTBN can effectively avoid state space explosion and poor performance when dealing with rare events. At the same time, DTBN has the ability of diagnostic analyses, which helps improve the system. QuanSafe provides a complete algorithm to transform AADL models into DTBN models. In addition, it supports multiple automated safety analysis methods with improved metrics. We conduct a case study on the Aircraft System. The experimental results show that our approach has higher efficiency and more comprehensive analysis capabilities than existing research.
KW - AADL
KW - Discrete-time Bayesian Network
KW - Model-based Safety Analysis
KW - Safety Analysis
UR - https://www.scopus.com/pages/publications/85206171987
U2 - 10.1007/978-3-031-66456-4_11
DO - 10.1007/978-3-031-66456-4_11
M3 - 会议稿件
AN - SCOPUS:85206171987
SN - 9783031664557
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 201
EP - 222
BT - Engineering of Complex Computer Systems - 28th International Conference, ICECCS 2024, Proceedings
A2 - Bai, Guangdong
A2 - Ishikawa, Fuyuki
A2 - Ait-Ameur, Yamine
A2 - Papadopoulos, George A.
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 19 June 2024 through 21 June 2024
ER -