TY - GEN
T1 - An Ontological Analysis of Safety-Critical Software and Its Anomalies
AU - Liu, Hezhen
AU - Jin, Zhi
AU - Zheng, Zheng
AU - Huang, Chengqiang
AU - Zhang, Xun
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The progressively dominant role of software in safety-critical systems raise concerns about the software dependability. There are limited mature practices and guides for assessing software dependability and analyzing system-level hazards triggered by software anomalies. A problem is that faults, errors, and failures that represent software anomalies, albeit with different natures, are usually used indistinctly to predict software dependability, leading to unsolid results. The lack of such consensual conceptualization also leads to poor interoperability between supporting tools, and, consequently, difficulties in anomaly management and software maintenance. Anomaly analysis and management is more tough for safety-critical software due to its higher complexity and the safety-critical nature. The complex context of safety-critical software causes difficulties in determining the evolution/propagation path of software anomalies and the impact on system safety. To capture the nature of safety-critical software and support an understanding of mechanisms of software anomalies and associated hazards, we propose three reference ontologies: Safety-critical Software Ontology, Software Fault Ontology and Software-failure-induced Hazard Ontology, which are built based on international standards, guides, and relevant conceptual models. We also discuss the relationships among them. That will facilitate a better understanding of the software anomaly mechanisms and the design of intervening/mitigation solutions. We demonstrate how these ontologies can help analyze software problems of real-world safety-critical systems.
AB - The progressively dominant role of software in safety-critical systems raise concerns about the software dependability. There are limited mature practices and guides for assessing software dependability and analyzing system-level hazards triggered by software anomalies. A problem is that faults, errors, and failures that represent software anomalies, albeit with different natures, are usually used indistinctly to predict software dependability, leading to unsolid results. The lack of such consensual conceptualization also leads to poor interoperability between supporting tools, and, consequently, difficulties in anomaly management and software maintenance. Anomaly analysis and management is more tough for safety-critical software due to its higher complexity and the safety-critical nature. The complex context of safety-critical software causes difficulties in determining the evolution/propagation path of software anomalies and the impact on system safety. To capture the nature of safety-critical software and support an understanding of mechanisms of software anomalies and associated hazards, we propose three reference ontologies: Safety-critical Software Ontology, Software Fault Ontology and Software-failure-induced Hazard Ontology, which are built based on international standards, guides, and relevant conceptual models. We also discuss the relationships among them. That will facilitate a better understanding of the software anomaly mechanisms and the design of intervening/mitigation solutions. We demonstrate how these ontologies can help analyze software problems of real-world safety-critical systems.
KW - dependability
KW - safety-critical software
UR - https://www.scopus.com/pages/publications/85151443648
U2 - 10.1109/QRS57517.2022.00040
DO - 10.1109/QRS57517.2022.00040
M3 - 会议稿件
AN - SCOPUS:85151443648
T3 - IEEE International Conference on Software Quality, Reliability and Security, QRS
SP - 311
EP - 320
BT - Proceedings - 2022 IEEE 22nd International Conference on Software Quality, Reliability and Security, QRS 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 22nd IEEE International Conference on Software Quality, Reliability and Security, QRS 2022
Y2 - 5 December 2022 through 9 December 2022
ER -