Verification of Synchronization-Related Properties for UML-MARTE RTES Models with a Set of Time Constraints Dedicated Formal Semantic

Critical Real-Time Embedded Systems (RTES) have strong requirement with respect to system's reliability. In Model-Driven Engineering (MDE), verification at early phases of the system lifecycle is an important issue, especially for time constraints in UML-MARTE RTES model. In order to assess that the time requirements are met by the behavior models, the key challenging problem is to transform these time constraints from the UML-MARTE model to computable formal semantics that provide time properties verification. Moreover, to allow the application of this formal semantic to real industrial use cases, the performance of verification should scale well. In this paper, we present a set of time constraint dedicated semantics under the framework for UML-MARTE RTES model's time requirement assessment. We focus on how to specify a set of synchronization-related constraints between system's tasks relying on a formal semantics and to accomplish verification by an efficient observer-based model checking method using Time Petri Nets. We analyse the method's computational complexity and demonstrate the method's scalability by illustrating some performance results

A transformation-driven approach to automate feedback verification results

International audienceThe integration of formal verification methods in modeling activities is a key issue to ensure the correctness of complex system design models. In this purpose, the most common approach consists in defining a translational semantics mapping the abstract syntax of the designer dedicated Domain-Specific Modeling Language (DSML) to a formal verification dedicated semantic domain in order to reuse the available powerful verification technologies. Formal verification is thus usually achieved using model transformations. However, the verification results are available in the formal domain which significantly impairs their use by the system designer which is usually not an expert of the formal technologies. In this paper, we introduce a novel approach based on Higher-Order transformations that analyze and instrument the transformation that expresses the semantics in order to produce traceability data to automatize the back propagation of verification results to the DSML end-user

Leveraging formal verification tools for DSML users: a process modeling case study

15 pagesIn the last decade, Model Driven Engineering (MDE) has been used to improve the development of safety critical systems by providing early Validation and Verification (V&V) tools for Domain Specific Modeling Languages (DSML). Verification of behavioral models is mainly addressed by translating domain specific models to formal verification dedicated languages in order to use the sophisticated associated tools such as model-checkers. This approach has been successfully applied in many different contexts, but it has a major draw- back: the user has to interact with the formal tools. In this paper, we present an illustrated approach that allows the designer to formally express the expected behavioral properties using a user oriented language -- a temporal extension of OCL --, that is automatically translated into the formal language; and then to get feedback from the assessment of these properties using its domain language without having to deal with the formal verification language nor with the under- lying translational semantics. This work is based on the metamodeling pattern for executable DSML that extends the DSML metamodel to integrate concerns related to execution and behavior

Online diagnosis of accidental faults for real-time embedded systems using a hidden Markov model

International audienceThis article proposes an approach for the online analysis of accidental faults for real-time embedded systems using hidden Markov models (HMMs). By introducing reasonable and appropriate abstraction of complex systems, HMMs are used to describe the healthy or faulty states of system’s hardware components. They are parametrized to statistically simulate the real system’s behavior. As it is not easy to obtain rich accidental fault data from a system, the Baum–Welch algorithm cannot be employed here to train the parameters in HMMs. Inspired by the principles of fault tree analysis and the maximum entropy in Bayesian probability theory, we propose to compute the failure propagation distribution to estimate the parameters in HMMs and to adapt the parameters using a backward algorithm. The parameterized HMMs are then used to online diagnose accidental faults using a vote algorithm integrated with a low-pass filter. We design a specific test bed to analyze the sensitivity, specificity, precision, accuracy and F1-score measures by generating a large amount of test cases. The test results show that the proposed approach is robust, efficient and accurate