Automated Identification of Application-Dependent Safe Faults in Automotive Systems-on-a-Chips

Ahmet Cagri Bagbaba, F. Augusto da Silva, Matteo Sonza Reorda, S. Hamdioui, Maksim Jenihhin, Christian Sauer

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

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.
Original languageEnglish
Article number319
Number of pages26
JournalElectronics (Switzerland)
Volume11
Issue number3
DOIs
Publication statusPublished - 2022

Keywords

  • Automotive systems
  • Diagnostic coverage
  • Fault classification
  • Fault injection
  • Formal methods
  • Functional safety
  • ISO 26262
  • Safe faults

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