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Proceedings of the Eighth International Conference on Probabilistic Safety Assessment & Management (PSAM)

Editor
Michael G. Stamatelatos
Michael G. Stamatelatos
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Harold S. Blackman
Harold S. Blackman
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ISBN-10:
0791802442
No. of Pages:
2576
Publisher:
ASME Press
Publication date:
2006

Although some work has been done to integrate software into the classical PRA framework, current Probability Risk Assessment (PRA) practice overwhelmingly neglects the contribution of software to the system risk. Dynamic Probabilistic Risk Assessment (DPRA) of complex systems is considered to be the next generation of PRA techniques. It is a set of methods and techniques, in which simulation models that represent the behavior of the elements of a system are exercised in order to identify risks and vulnerabilities of the system. We propose that DPRA is the most appropriate environment for incorporation of software behavior into system risk models, and this paper demonstrates the approach developed for that purpose.

The proposed methodology is developed and implemented within a new DPRA methodological framework and its software implementation known as SimPRA [1]. SimPRA is an adaptive scheduling simulation-based DPRA environment, where prior knowledge of the systems and knowledge gained during simulation is used to dynamically adjust the scenario exploration rules. In order to address the state explosion issue symptomatic of DPRA approaches, a new approach is implemented in SimPRA to bias the simulation towards “interesting” events and end states. This approach includes the use of a knowledge-driven high level Planner to guide the simulation as well as an entropy-based biasing of the scenarios through the Scheduler which controls the actual simulation.

The software representation in the SimPRA environment includes both a behavior model and a software guidance model [2]. The behavior model is an executable simulation model. It is plugged into the system simulation module of SimPRA to represent the software behavior. It is in principle able to capture all phenomena that fall within the scope of the analysis. The software behavior model is a combination of deterministic and stochastic models. The deterministic model is used to simulate the behavior of the software, as well as the interaction between the software and other parts of the system. The stochastic model is superimposed onto the deterministic model to represent the uncertain behavior of the software, e.g., software failures. Two knowledge bases, the Abstraction Knowledge Base (AKB) and Failure Injection Knowledge Base (FIKB) are automatically generated from the behavior model. The information is used in the guidance model to define the controllable variables. The software guidance model is used to guide the simulation to explore scenarios of interest instead of a wide-scale exploration. A Simulation Knowledge Base (SKB) is constructed inside the software guidance model to store the prior knowledge about the software system.

This paper focuses on the integration of the software model into the SimPRA environment. A procedure is summarized to establish an accurate software representation when code is available and objective test data can be obtained. To illustrate the methodology, an example application is provided.

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