TY - JOUR
T1 - Automated Identification of Application-Dependent Safe Faults in Automotive Systems-on-a-Chips
AU - Bagbaba, Ahmet Cagri
AU - Augusto da Silva, F.
AU - Sonza Reorda, Matteo
AU - Hamdioui, S.
AU - Jenihhin, Maksim
AU - Sauer, Christian
PY - 2022
Y1 - 2022
N2 - ISO 26262 requires classifying random hardware faults based on their effects (safe, detected, or undetected) within integrated circuits used in automobiles. In general, this classification is addressed using expert judgment and a combination of tools. However, the growth of integrated circuit complexity creates a huge fault space; hence, this form of fault classification is error prone and time consuming. Therefore, an automated and systematic approach is needed to target hardware fault classification in automotive systems on chips (SoCs), considering the application software. This work focuses on identifying safe faults: the proposed approach utilizes coverage analysis to identify candidate safe faults considering all the constraints coming from the application. Then, the behavior of the application software is modeled so that we can resort to a formal analysis tool. The proposed technique is evaluated on the AutoSoC benchmark running a cruise control application. Resorting to our approach, we could classify 20%, 11%, and 13% of all faults in the central processing unit (CPU), universal asynchronous receiver–transmitter (UART), and controller area network (CAN) as safe faults, respectively. We also show that this classification can increase the diagnostic coverage of software test libraries targeting the CPU and CAN modules by 4% to 6%, increasing the achieved testable fault coverage.
AB - ISO 26262 requires classifying random hardware faults based on their effects (safe, detected, or undetected) within integrated circuits used in automobiles. In general, this classification is addressed using expert judgment and a combination of tools. However, the growth of integrated circuit complexity creates a huge fault space; hence, this form of fault classification is error prone and time consuming. Therefore, an automated and systematic approach is needed to target hardware fault classification in automotive systems on chips (SoCs), considering the application software. This work focuses on identifying safe faults: the proposed approach utilizes coverage analysis to identify candidate safe faults considering all the constraints coming from the application. Then, the behavior of the application software is modeled so that we can resort to a formal analysis tool. The proposed technique is evaluated on the AutoSoC benchmark running a cruise control application. Resorting to our approach, we could classify 20%, 11%, and 13% of all faults in the central processing unit (CPU), universal asynchronous receiver–transmitter (UART), and controller area network (CAN) as safe faults, respectively. We also show that this classification can increase the diagnostic coverage of software test libraries targeting the CPU and CAN modules by 4% to 6%, increasing the achieved testable fault coverage.
KW - Automotive systems
KW - Diagnostic coverage
KW - Fault classification
KW - Fault injection
KW - Formal methods
KW - Functional safety
KW - ISO 26262
KW - Safe faults
UR - http://www.scopus.com/inward/record.url?scp=85122936454&partnerID=8YFLogxK
U2 - 10.3390/electronics11030319
DO - 10.3390/electronics11030319
M3 - Article
VL - 11
JO - Electronics (Switzerland)
JF - Electronics (Switzerland)
IS - 3
M1 - 319
ER -