Critical distributed real-time embedded componentbased systems must be dependable and thus be able to avoid unacceptable failures. To efficiently evaluate the dependability of the assembly obtained by selecting and composing components, well-integrated and tool-supported techniques are needed. Currently, no satisfying tool-supported technique fully integrated in the development life-cycle exists. To overcome this limitation, we propose CHESS-FLA, which is a modeldriven failure logic analysis method. CHESS-FLA allows designers to: model the nominal as well as the failure behaviour of their architectures; automatically perform dependability analysis through a model transformation; and, finally, ease the interpretation of the analysis results through back-propagation onto the original architectural model. CHESS-FLA is part of an industrial quality tool-set for the functional and extra-functional development of high integrity embedded component-based systems, developed within the EU-ARTEMIS funded CHESS project. Finally, we present a case study taken from the telecommunication domain to illustrate and assess the proposed method.
Business analysts and domain experts are often sketching the behaviors of a software system using high-level models that are technology-and platform-independent. The developers will refine and enrich these high-level models with technical details. As a consequence, the refined models can deviate from the original models over time, especially when the two kinds of models evolve independently. In this context, we focus on behavior models; that is, we aim to ensure that the refined, low-level behavior models conform to the corresponding high-level behavior models. Based on existing formal verification techniques, we propose containment checking as a means to assess whether the system's behaviors described by the low-level models satisfy what has been specified in the high-level counterparts. One of the major obstacles is how to lessen the burden of creating formal specifications of the behavior models as well as consistency constraints, which is a tedious and error-prone task when done manually. Our approach presented in this paper aims at alleviating the aforementioned challenges by considering the behavior models as verification inputs and devising automated mappings of behavior models onto formal properties and descriptions that can be directly used by model checkers. We discuss various challenges in our approach and show the applicability of our approach in illustrative scenarios.
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