Modeling and Verification of Safety-Critical Systems Using Safecharts

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With rapid development in science and technology, we now see the ubiquitous use of different types of safety-critical systems in our daily lives such as in avionics, consumer electronics, and medical systems. In such systems, unintentional design faults might result in injury or even death to human beings. To make sure that safety-critical systems are really safe, there is need to verify them formally. However, the verification of such systems is getting more and more difficult, because the designs are becoming very complex. To cope with high design complexity, currently model-driven architecture design is becoming a well-accepted trend. However, conventional methods of code testing and standards conformance do not fit very well with such model-based approaches. To bridge this gap, we propose a model-based formal verification technique for safety-critical systems. In this work, the model checking paradigm is applied to the Safecharts model which was used for modeling, but not yet used for verification. Our contributions are five folds. Firstly, the safety constraints in Safecharts are mapped to semantic equivalents in timed automata for verification. Secondly, the theory for safety constraint verification is proved and implemented in a compositional model checker (SGM). Thirdly, prioritized transitions are implemented in SGM to model the risk semantics in Safecharts. Fourthly, it is shown how the original Safecharts lacked synchronization semantics which could lead to safety hazards. A solution to this issue is also proposed. Finally, it is shown that priority-based approach to mutual exclusion of resource usage in the original Safecharts is unsafe and corresponding solutions are proposed here. Application examples show the feasibility and benefits of the proposed model-driven verification of safety-critical systems.KeywordsModel CheckMutual ExclusionGuard ConditionError TraceSafety ConstraintThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.