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Automating Pattern Selection for Assurance Case Development for Cyber-Physical Systems

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Part of the Lecture Notes in Computer Science book series (LNCS,volume 13414)


Assurance Cases are increasingly being required for regulatory acceptance of Cyber-Physical Systems. However, the ever-increasing complexity of these systems has made the assurance cases development complex, labor-intensive and time-consuming. Assurance case fragments called patterns are used to handle the complexity. The state-of-the-art approach has been to manually select generic patterns from online catalogs, instantiate them with system-specific information, and assemble them into an assurance case. While there has been some work in automating the instantiation and assembly, a less researched area is the automation of the pattern selection process, which takes a considerable amount of the assurance case development time. To close this automation gap, we have developed an automated pattern selection workflow that handles the selection problem as a coverage problem, intending to find the smallest set of patterns that can cover the available system artifacts. For this, we utilize the ontology graphs of the system artifacts and the patterns and perform graph analytics. The selected patterns are fed into an external instantiation function to develop an assurance case. Then, they are evaluated for coverage using two coverage metrics. An illustrative autonomous vehicle example is provided, demonstrating the utility of the proposed workflow in developing an assurance case with reduced efforts and time compared to the manual development alternative.


  • Cyber physical systems
  • Assurance case
  • Patterns
  • GSN
  • Optimization
  • Ontology
  • Graph isomorphism
  • Coverage metrics

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  1. 1.

    In the rest of this paper, we will refer to “AC patterns" as “patterns".

  2. 2.

    Captures the fine-grained relationships between different system artifacts.

  3. 3.

    For the CARLA AV setup, visit

  4. 4.

    Tool is being built as part of the DARPA ARCOS program. Check our GitHub for release information.

  5. 5.

    For a bird’s eye view of the “revision1" assurance case and the report, visit


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The authors would like to thank Sarah C. Helble and Dennis M. Volpano for helpful discussions and feedback. This work was supported by the DARPA ARCOS project under Contract FA8750-20-C-0515 (ACCELERATE) and the DARPA Assured Autonomy project. The views, opinions, and/or findings expressed are those of the author(s) and do not necessarily reflect the views of DARPA. We would like to thank the reviewers and editors for taking the time and effort necessary to review the manuscript. We appreciate the valuable feedback, which helped us to improve the quality of the manuscript.

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Correspondence to Shreyas Ramakrishna .

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Ramakrishna, S., Jin, H., Dubey, A., Ramamurthy, A. (2022). Automating Pattern Selection for Assurance Case Development for Cyber-Physical Systems. In: Trapp, M., Saglietti, F., Spisländer, M., Bitsch, F. (eds) Computer Safety, Reliability, and Security. SAFECOMP 2022. Lecture Notes in Computer Science, vol 13414. Springer, Cham.

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