Safety-critical systems are widely used in different domains and lead to an increasing complexity. Such systems rely on specific services such space and time isolation as in the ARINC653 avionics standard. Their criticality requires a carefully driven design based on an appropriate development process and dedicated tools to detect and avoid problems as early as possible.Model Driven Engineering (MDE) approaches are now considered as valuable approach for building safety-critical systems. The Architecture Analysis and Design Language (AADL) proposes a component-based language suitable to operate MDE that fits with safety-critical systems needs. This paper presents an approach for the modeling, verification and implementation of ARINC653 systems using AADL. It details a modeling approach exploiting the new features of AADL version 2 for the design of ARINC653 architectures. It also proposes modeling patterns to represent other safety mechanisms such as the use of Ravenscar for critical applications. This approach is fully backed by tools with Ocarina (AADL toolsuite), POK (AADL/AR-INC653 runtime) and Cheddar (scheduling verification). Thus, it assists system engineers to simulate and validate non functional requirements such as scheduling or resources dimensioning.
In this paper, we investigate a way to promote the reuse of legacy tools (or transformations) in specific contexts (defined by specific metamodels). More precisely we suggest a model transformation approach to achieve this purpose.We first introduce a language based on a metamodel called Modif in order to specify the differences between two semantically close metamodels. We can generate automatically data migration components from a Modif specification. They enable to put data complying with the specific context under the scope of the legacy tool. But more importantly in the case of a rewriting tool, they enable to put the tool's outcome back into the original specific context.Then we propose a process and a set of helpers based on Modif to automate the reuse of legacy tools for domainspecific contexts. To illustrate this approach, we apply it to the case of simple finite state machines.
International audienceTo debug running software we need unanticipated adaptation capabilities, especially when systems cannot be stopped, updated and restarted. Adapting such programs at runtime is an extreme solution given the delicate live contexts the debugging activity takes place. We introduce the Dynamic Layer, a construct in which behavioral variations are gathered and activated as a whole set of adaptations. Dimensions of Dynamic Layers activation are reified to allow very fine definitions of layer scopes and a fine grained selection of adapted entities. This paper describes and discusses the Dynamic Layer solution to perform unanticipated runtime debugging. An experimental implementation with the Pharo language is evaluated through a runtime adaptation example
Cyber-Physical Systems (CPS) integrate sensors and actuators to collect data and control entities in the physical world. Debugging CPS systems is hard due to the time-sensitive nature of a distributed applications combined with the lack of control on the surrounding physical environment. This makes bugs in CPS systems hard to reproduce and thus to fix. In this context, on-line debugging techniques are helpful because the debugger is connected to the device when an exception or crash occurs. This paper reports on our experiences on applying two different on-line debugging techniques for a CPS system: remote debugging using the Pharo remote debugger and our IDRA debugger. In contrast to traditional remote debugging, IDRA allows to on-line debug an application locally in another client machine by reproducing the runtime context where the bug manifested. Our qualitative evaluation shows that IDRA provides almost the same interaction capabilities than Pharo's remote debugger and is less intrusive when performing hot-modifications. Our benchmarks also show that IDRA is significantly faster than the Pharo remote debugger, although it increases the amount of data transferred over the network.
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