Abstract
We present ARACHNE, a framework for the automated, compositional validation of assurance cases (ACs), i.e., structured arguments about the correctness or safety of a design. ARACHNE leverages assume-guarantee contracts, expressed in a stochastic logic formalism, to formally capture AC claims (guarantees) subject to their contexts (assumptions) as well as the sources of uncertainty associated with them. Given an AC, modeled as a hierarchical network of stochastic contracts, and a library of confidence models, expressed as a set of Bayesian networks, we propose a procedure that coordinates logic and Bayesian reasoning to check that the AC argument is sound and quantify its strength in terms of a confidence measure. The effectiveness of our approach is illustrated on case studies motivated by testing and validation of airborne and automotive system software.
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- 1.
Due to randomness in the system or process, often quantified via objective measures from statistics (e.g., number of heads out of n tosses of a coin).
- 2.
Uncertainty about the system or process (e.g., whether a coin is fair or not), often captured by subjective measures of belief.
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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 1839842, 1846524, and 2139982, the Office of Naval Research (ONR) under Award N00014-20-1-2258, and the Defense Advanced Research Projects Agency (DARPA) under Award HR00112010003.
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Oh, C., Naik, N., Daw, Z., Wang, T.E., Nuzzo, P. (2022). ARACHNE: Automated Validation of Assurance Cases with Stochastic Contract Networks. 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. https://doi.org/10.1007/978-3-031-14835-4_5
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