This paper studies the semantics of hierarchical finite state machines (FMS's) that are composed using various concurrency models, particularly dataflow, discrete-events, and synchronous/reactive modeling. It is argued that all three combinations are useful, and that the concurrency model can be selected independently of the decision to use hierarchical FSM's. In contrast, most formalisms that combine FSM's with concurrency models, such as Statecharts (and its variants) and hybrid systems, tightly integrate the FSM semantics with the concurrency semantics. An implementation that supports three combinations is described.

Dataflow programming models are suitable to express multi-core streaming applications. The design of highquality embedded systems in that context requires static analysis to ensure the liveness and bounded memory of the application. However, many streaming applications have a dynamic behavior. The previously proposed dataflow models for dynamic applications do not provide any static guarantees or only in exchange of significant restrictions in expressive power or automation. To overcome these restrictions, we propose the schedulable parametric dataflow (SPDF) model. We present static analyses and a quasi-static scheduling algorithm. We demonstrate our approach using a video decoder case study.

We propose a new framework for the (length,reliability) bicriteria static multiprocessor scheduling problem. Our first criterion remains the schedule's length, crucial to assess the system's realtime property. For our second criterion, we consider the global system failure rate, seen as if the whole system were a single task scheduled onto a single processor, instead of the usual reliability, because it does not depend on the schedule length like the reliability does (due to its computation in the classical exponential distribution model). Therefore, we control better the replication factor of each individual task of the dependency task graph given as a specification, with respect to the desired failure rate. To solve this bicriteria optimization problem, we take the failure rate as a constraint, and we minimize the schedule length. We are thus able to produce, for a given dependency task graph and multiprocessor architecture, a Pareto curve of non-dominated solutions, among which the user can choose the compromise that fits his requirements best. Compared to the other bicriteria (length,reliability) scheduling algorithms found in the literature, the algorithm we present here is the first able to improve significantly the reliability, by several orders of magnitude, making it suitable to safety critical systems.

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A large class of embedded systems is distinguished from general-purpose computing systems by the need to satisfy strict requirements on timing, often under constraints on available resources. Predictable system design is concerned with the challenge of building systems for which timing requirements can be guaranteed a priori. Perhaps paradoxically, this problem has become more difficult by the introduction of performanceenhancing architectural elements, such as caches, pipelines, and multithreading, which introduce a large degree of uncertainty and make guarantees harder to provide. The intention of this article is to summarize the current state of the art in research concerning how to build predictable yet performant systems. We suggest precise definitions for the concept of "predictability", and present predictability concerns at different abstraction levels in embedded system design. First, we consider timing predictability of processor instruction sets. Thereafter, we consider how programming languages can be equipped with predictable timing semantics, covering both a language-based approach using the synchronous programming paradigm, as well as an environment that provides timing semantics for a mainstream programming language (in this case C). We present techniques for achieving timing predictability on multicores. Finally, we discuss how to handle predictability at the level of networked embedded systems where randomly occurring errors must be considered.

Our goal is to automatically obtain a distributed and fault-tolerant embedded system: distributed because the system must run on a distributed architecture; fault-tolerant because the system is critical.

Software is a fundamental pillar of modern scientific research, across all fields and disciplines. However, there is a lack of adequate means to cite and reference software due to the complexity of the problem in terms of authorship, roles and credits. This complexity is further increased when it is considered over the lifetime of a software that can span up to several decades. Building upon the internal experience of Inria, the French research institute for digital sciences, we provide in this paper a contribution to the ongoing efforts in order to develop proper guidelines and recommendations for software citation and reference. Namely, we recommend: (1) a richer taxonomy for software contributions with a qualitative scale; (2) to put humans at the heart of the evaluation; and (3) to distinguish citation from reference.