TAILIEUCHUNG - Model-Based Design for Embedded Systems- P44

Model-Based Design for Embedded Systems- P44: This book contains information obtained from authentic and highly regarded sources. Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. | 406 Model-Based Design for Embedded Systems desirable. Although simulation is more time-scalable it is still not completely predictable. Running 200 instead of 100 simulations obviously does not guarantee that twice as many bugs will be found. It does not guarantee either that twice as many states will be explored. These are some of the reasons that prompted more systematic methodologies for simulation-based verification . see 108 for the case of the hardware industry and 44 for work done in a software context . In the hardware case these methodologies include specialized languages for writing testbenches . simulation environments that allow to specify input-generation policies as well as property monitors for example see 53 111 . In this context the principle of randomization is often used as a good aid to uncover corner cases and eventually bugs . see 64 75 78 92 . We discuss some applications of the randomized state-space exploration principle to embedded system models in the rest of this section. We also introduce the concept of resource-aware verification which goes beyond randomization and includes all verification methods that are explicitly aware of their memory and time resources. Finally we examine a particular randomized search algorithm RRT and its application to hybrid automata. Randomized Exploration and Resource-Aware Verification A simple technique to randomly explore a state space is random walk pick randomly an initial state s0 then pick randomly one of the successors of s0 say s1 then pick randomly one of the successors of s1 and so on. This is obviously a very inexpensive algorithm in terms of space since it needs only to store a single state at a given time plus perhaps its set of successor states. Basic random walk is limited especially when bugs lie very deep in the state space that is the paths to reach an error state are very long. Then unless the number of such paths is very large the probability to follow a path that leads

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