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Existing model-based processes for embedded real-time systems support the analysis of various non-functional properties, most notably schedulability, through model checking, simulation or other means. The analysis results are then used for modifying the systems design, so that the expected properties are satisfied. A rigorous model-based design flow differs in that it aims at a system implementation derived from high-level models by applying a sequence of semantics-preserving transformations. Properties established at any design step are preserved throughout the subsequent steps including the executable implementation. We introduce such a design flow using a process network model of computation for application design at a high level, which combines streaming and reactive control processing with task parallelism. The schedulability of the so-called FPPNs (Fixed Priority Process Networks) is well-studied and various solutions have been presented. This article focuses on the design flows steps for deriving executable implementations on the BIP (Behavior - Interaction - Priority) runtime environment. FPPNs are designed using the TASTE toolset, a convenient architecture description interface. In this way, the developers do not program explicitly low-level real-time OS services and the schedulability properties are guaranteed throughout the design steps by construction. The approach has been validated on the design of a real spacecraft on-board application that has been scheduled for execution on an industrial multicore platform.
Avionics is one kind of domain where prevention prevails. Nonetheless fails occur. Sometimes due to pilot misreacting, flooded in information. Sometimes information itself would be better verified than trusted. To avoid some kind of failure, it has b
The Behavior-Interaction-Priority (BIP) framework, rooted in rigorous semantics, allows the construction of systems that are correct-by-design. BIP has been effectively used for the construction and analysis of large systems such as robot controllers
Pre-trained large-scale language models have increasingly demonstrated high accuracy on many natural language processing (NLP) tasks. However, the limited weight storage and computational speed on hardware platforms have impeded the popularity of pre
This paper describes a comprehensive prototype of large-scale fault adaptive embedded software developed for the proposed Fermilab BTeV high energy physics experiment. Lightweight self-optimizing agents embedded within Level 1 of the prototype are re
Estimating Worst-Case Execution Time (WCET) is of utmost importance for developing Cyber-Physical and Safety-Critical Systems. The systems scheduler uses the estimated WCET to schedule each task of these systems, and failure may lead to catastrophic