Abstract
Assurance cases (ACs) have gained attention in the aerospace, medical, and other heavily-regulated industries as a means for providing structured arguments on why a product is dependable (i.e., safe, secure, etc.) for its intended application. Challenges in AC construction stem from the complexity and uniqueness of the designs, the heterogeneous nature of the required supporting evidence, and the need to assess the quality of an argument. We present an automated AC generation framework that facilitates the construction, validation, and confidence assessment of ACs based on dependability argument patterns and confidence patterns capturing domain knowledge. The ACs are instantiated with a system’s specification and evaluated based on the available design and verification evidence. Aerospace case studies illustrate the framework’s effectiveness, efficiency, and scalability.
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References
Holloway, C.: Understanding the overarching properties, ser. NASA technical memorandum (2019)
Rushby, J.: The interpretation and evaluation of assurance cases. Computer Science Laboratory, SRI International, Menlo Park, CA, Technical report. SRI-CSL-15-01 (2015)
TACW Group: Goal structuring notation community standard (version 3) (2021)
Hawkins, R., Habli, I., et al.: Weaving an assurance case from design: a model-based approach. In: International Symposium High Assurance Systems Engineering, pp. 110–117 (2015)
Rushby, J.: Formalism in safety cases. In: Dale, C., Anderson, T. (eds.) Making Systems Safer, pp. 3–17. Springer, London (2010). https://doi.org/10.1007/978-1-84996-086-1_1
Bloomfield, R., Rushby, J.: Assurance 2.0. arXiv preprint arXiv:2004.10474 (2020)
Denney, E., Pai, G., Pohl, J.: AdvoCATE: an assurance case automation toolset. In: Ortmeier, F., Daniel, P. (eds.) SAFECOMP 2012. LNCS, vol. 7613, pp. 8–21. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33675-1_2
Barry, M.R.: CertWare: a workbench for safety case production and analysis. In: Aerospace conference, pp. 1–10 (2011)
Matsuno, Y.: D-case editor: a typed assurance case editor. University of Tokyo (2011)
Adelard LLP. Assurance and safety case environment (ASCE) (2011). https://www.adelard.com/asce/
Sljivo, I., Gallina, B., Carlson, J., Hansson, H., Puri, S.: Tool-supported safety-relevant component reuse: from specification to argumentation. In: Casimiro, A., Ferreira, P.M. (eds.) Ada-Europe 2018. LNCS, vol. 10873, pp. 19–33. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-92432-8_2
Šljivo, I., Uriagereka, G.J., et al.: Guiding assurance of architectural design patterns for critical applications. J. Syst. Architect. 110, 101765 (2020)
de la Vara, J.L., Ruiz, A., Blondelle, G.: Assurance and certification of cyber-physical systems: the AMASS open source ecosystem. J. Syst. Softw. 171, 110812 (2021). https://www.sciencedirect.com/science/article/pii/S0164121220302120
Nešić, D., Nyberg, M., Gallina, B.: Product-line assurance cases from contract-based design. J. Syst. Softw. 176, 110922 (2021)
Neapolitan, R.: Learning Bayesian Networks, ser. Artificial Intelligence. Pearson Prentice Hall (2004)
Cârlan, C., Nigam, V., et al.: ExplicitCase: tool-support for creating and maintaining assurance arguments integrated with system models. In: International Symposium on Software Reliability Engineering Workshops (ISSREW), pp. 330–337 (2019)
Ramakrishna, S., Hartsell, C., et al.: A methodology for automating assurance case generation. arXiv preprint arXiv:2003.05388 (2020)
Graydon, P.J., Holloway, C.M.: An investigation of proposed techniques for quantifying confidence in assurance arguments. Saf. Sci. 92, 53–65 (2017)
Oh, C., Naik, N., Daw, Z., Wang, T.E., Nuzzo, P.: ARACHNE: automated validation of assurance cases with stochastic contract networks. In: Trapp, M., Saglietti, F., Spisländer, M., Bitsch, F. (eds.) SAFECOMP 2022. LNCS, vol. 13414, pp. 65–81. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-14835-4_5
Holloway, C.M.: Explicate’78: uncovering the implicit assurance case in DO-178C. In: Safety-Critical Systems Symposium (2015)
The GSN Working Group Online, Goal Structuring Notation. http://www.goalstructuringnotation.info/
Adelard LLP. Claims, Arguments and Evidence (CAE) (2019). https://www.adelard.com/asce/choosing-asce/cae.html. Accessed 23 Oct 2020
Fujita, H., Matsuno, Y., et al.: DS-Bench toolset: tools for dependability benchmarking with simulation and assurance. In: International Conference on Dependable Systems and Networks, pp. 1–8. IEEE (2012)
Benveniste, A., Caillaud, B., et al.: Contracts for system design. Found. Trends Electron. Des. Autom. 12(2–3), 124–400 (2018)
Bauer, S.S., et al.: Moving from specifications to contracts in component-based design. In: de Lara, J., Zisman, A. (eds.) FASE 2012. LNCS, vol. 7212, pp. 43–58. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-28872-2_3
Gacek, A., Backes, J., et al.: Resolute: an assurance case language for architecture models. In: SIGAda Annual Conference on High Integrity Language Technology, pp. 19–28 (2014)
Wang, T.E., Daw, Z., Nuzzo, P., Pinto, A.: Hierarchical contract-based synthesis for assurance cases. In: Deshmukh, J.V., Havelund, K., Perez, I. (eds.) NFM 2022. LNCS, vol. 13260, pp. 175–192. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-06773-0_9
Jensen, F.V.: Introduction to Bayesian Networks, 1st edn. Springer, Heidelberg (1996)
Verbert, K., Babuška, R., De Schutter, B.: Bayesian and Dempster-Shafer reasoning for knowledge-based fault diagnosis–a comparative study. Eng. Appl. Artif. Intell. 60, 136–150 (2017)
ArduPilot Dev Team. Arducopter (2023). https://ardupilot.org/copter/
Shankar, N., Bhatt, D., et al.: DesCert: Design for certification. arXiv preprint arXiv:2203.15178 (2022)
Bhatt, D., Ren, H., Murugesan, A., Biatek, J., Varadarajan, S., Shankar, N.: Requirements-driven model checking and test generation for comprehensive verification. In: Deshmukh, J.V., Havelund, K., Perez, I. (eds.) NFM 2022. LNCS, vol. 13260, pp. 576–596. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-06773-0_31
Acknowledgments
Distribution statement “A” (approved for public release, distribution unlimited). This research was developed with funding from the Defense Advanced Research Projects Agency (DARPA), contract FA875020C0508. The views, opinions, or findings expressed are those of the authors and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government. The authors wish to also acknowledge the partial support by the National Science Foundation (NSF) under Awards 1846524 and 2139982, the Office of Naval Research (ONR) under Award N00014-20-1-2258, the Defense Advanced Research Projects Agency (DARPA) under Award HR00112010003, and the Okawa Research Grant.
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Wang, T.E. et al. (2023). Computer-Aided Generation of Assurance Cases. In: Guiochet, J., Tonetta, S., Schoitsch, E., Roy, M., Bitsch, F. (eds) Computer Safety, Reliability, and Security. SAFECOMP 2023 Workshops. SAFECOMP 2023. Lecture Notes in Computer Science, vol 14182. Springer, Cham. https://doi.org/10.1007/978-3-031-40953-0_12
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