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Control systems are used in industry for process automation and to increase the reliability and dependability when executing critical tasks. Failures in the operation of such systems may cause expensive losses and can compromise the plant safety. Therefore, it is necessary to define techniques, methods and tools to increase the dependability as well as reliability. Verification and validation techniques...
A probabilistic system is useful in modeling randomized algorithms (e.g., consensus algorithms), unreliable or unpredictable behaviors (e.g., human behaviors in decision making process), etc. Markov Decision Process (MDP) is used to construct this kind of system, because it has both nondeterministic and probabilistic choices. In this work, we study probabilistic models and analyze some issues such...
The complexity of software in embedded systems has increased significantly over the last years so that software verification now plays an important role in ensuring the overall product quality. In this context, bounded model checking has been successfully applied to discover subtle errors, but for larger applications, it often suffers from the state space explosion problem. This paper describes a...
Model checking is a well-known and fully automatic technique for checking software properties, usually given as temporal logic formulae on the program variables. Most of model checkers found in the literature use exact deterministic algorithms to check the properties. These algorithms usually require huge amounts of memory if the checked model is large. We propose here the use of an algorithm based...
The next few years will see distributed real-time computer systems playing an important role in control systems of high-dependability applications, such as rail transportation. In these applications a failure in the temporal domain can be as critical as a failure in the value domain. In rail transportation, train control system has become more complex and the methods to ensure its correctness of have...
Model-based testing techniques select test cases according to test goals, which might be coverage criteria or mutation adequacy. Complex criteria and large models lead to large test suites, and a test case created for one coverage item might cover several other items as well. Therefore, test case generation is optimized in order to avoid unnecessary test cases and minimize the test generation and...
The bugbear of model checking is the explosion in the number of states as the number of processes increases. Industrial-sized problems are often intractable for model checkers. We modify the most popular model checker in use today, SPIN, by replacing its internal verification search engine by a guided, random-walk based simulator. The resulting tool is called RANSPIN. The guiding mechanism used in...
We present a component-based description language for heterogeneous systems composed of several data flow processing components and a unique event- based controller. Descriptions are used both for generating and deploying implementation code and for checking safety properties on the system. The only constraint is to specify the controller in a synchrounous reactive language. We propose an analysis...
Systems verification requires first to model the system to be verified, then to formalize the properties to be satisfied, and finally to describe the behaviour of the environment. This last point, known as the proof context, is often neglected. It could, however, be of great importance in order to reduce the complexity of the proof. The question is then how to formalize such a proof context. This...
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