Modern embedded systems integrate more and more complex functionalities. At the same time, the semiconductor technology advances enable to increase the amount of hardware resources on a chip for the execution. Massively parallel embedded systems specifically deal with the optimized usage of such hardware resources to efficiently execute their functionalities. The design of these systems mainly relies on the following challenging issues: first, how to deal with the parallelism in order to increase the performance; second, how to abstract their implementation details in order to manage their complexity; third, how to refine these abstract representations in order to produce efficient implementations. This article presents the Gaspard design framework for massively parallel embedded systems as a solution to the preceding issues. Gaspard uses the repetitive Model of Computation (MoC), which offers a powerful expression of the regular parallelism available in both system functionality and architecture. Embedded systems are designed at a high abstraction level with the MARTE (Modeling and Analysis of Real-time and Embedded systems) standard profile, in which our repetitive MoC is described by the so-called Repetitive Structure Modeling (RSM) package. Based on the Model-Driven Engineering (MDE) paradigm, MARTE models are refined towards lower abstraction levels, which make possible the design space exploration. By combining all these capabilities, Gaspard allows the designers to automatically generate code for formal verification, simulation and hardware synthesis from high-level specifications of high-performance embedded systems. Its effectiveness is demonstrated with the design of an embedded system for a multimedia application.

Abstract-Test prioritization techniques select test cases that maximize the confidence on the correctness of the system when the resources for quality assurance (QA) are limited. In the event of a test failing, the fault at the root of the failure has to be localized, adding an extra debugging cost that has to be taken into account as well. However, test suites that are prioritized for failure detection can reduce the amount of useful information for fault localization. This deteriorates the quality of the diagnosis provided, making the subsequent debugging phase more expensive, and defeating the purpose of the test cost minimization.In this paper we introduce a new test case prioritization approach that maximizes the improvement of the diagnostic information per test. Our approach minimizes the loss of diagnostic quality in the prioritized test suite. When considering QA cost as the combination of testing cost and debugging cost, on the Siemens set, the results of our test case prioritization approach show up to a 53% reduction of the overall QA cost, compared with the next best technique .

Runtime testing is emerging as the solution for the integration and validation of software systems where traditional development-time integration testing cannot be performed, such as Systems of Systems or Service Oriented Architectures. However, performing tests during deployment or in-service time introduces interference problems, such as undesired side-effects in the state of the system or the outside world.This paper presents a qualitative model of runtime testability that complements Binder's classical testability model, and a generic measurement framework for quantitatively assessing the degree of runtime testability of a system based on the ratio of what can be tested at runtime vs. what would have been tested during development time. A measurement is devised for the concrete case of architecture-based test coverage, by using a graph model of the system's architecture. Concretely, two testability studies are performed for two component based systems, showing how to measure the runtime testability of a system.

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