Multi-paradigm modelling for cyber–physical systems: a descriptive framework

The complexity of cyber–physical systems (CPSs) is commonly addressed through complex workflows, involving models in a plethora of different formalisms, each with their own methods, techniques, and tools. Some workflow patterns, combined with particular types of formalisms and operations on models in these formalisms, are used successfully in engineering practice. To identify and reuse them, we refer to these combinations of workflow and formalism patterns as modelling paradigms. This paper proposes a unifying (Descriptive) Framework to describe these paradigms, as well as their combinations. This work is set in the context of Multi-Paradigm Modelling (MPM), which is based on the principle to model every part and aspect of a system explicitly, at the most appropriate level(s) of abstraction, using the most appropriate modelling formalism(s) and workflows. The purpose of the Descriptive Framework presented in this paper is to serve as a basis to reason about these formalisms, workflows, and their combinations. One crucial part of the framework is the ability to capture the structural essence of a paradigm through the concept of a paradigmatic structure. This is illustrated informally by means of two example paradigms commonly used in CPS: Discrete Event Dynamic Systems and Synchronous Data Flow. The presented framework also identifies the need to establish whether a paradigm candidate follows, or qualifies as, a (given) paradigm. To illustrate the ability of the framework to support combining paradigms, the paper shows examples of both workflow and formalism combinations. The presented framework is intended as a basis for characterisation and classification of paradigms, as a starting point for a rigorous formalisation of the framework (allowing formal analyses), and as a foundation for MPM tool development.

Monitoring Practitioner's Skills in Pure-Tone Audiometry

So far, there exists no standard, to evaluate a practitioner's skills in pure-tone audiometry. To narrow the gap, this article presents an artificial patient (AP) emulating various types of hearing impairment. In contrast to other solutions, the AP autonomously listens to real pure-tones in soft real-time, while taking into account elements from psycho-acoustics. The emulated patient profiles are reproducible. New profiles can be easily added. The AP is able to recover from error. In this contribution, the authors develop software requirements specifications and derive a modular system architecture. To analyze the performance, the article proposes a stochastic extension to existing synchronous data flow graphs, taking into account the unbounded nature of the tasks' worst case response time. Maximization and summation over the graph reveals the joint distribution of the response time with first and second central moments corresponding to, respectively, the expected response time and the jitter of the task. The theoretical results have finally been validated by measurements on the target.

LusRegTes: A Regression Testing Tool for Lustre Programs

Lustre is a synchronous data-flow declarative language widely used for safety-critical applications (avionics, energy, transport...). In such applications, the testing activity for detecting errors of the system plays a crucial role. During the development and maintenance processes, Lustre programs are often evolving, so regression testing should be performed to detect bugs. In this paper, we present a tool for automatic regression testing of Lustre programs. We have defined an approach to generate test cases in regression testing of Lustre programs. In this approach, a Lustre program is represented by an operator network, then the set of paths is identified and the path activation conditions are symbolically computed for each version. Regression test cases are generated by comparing paths between versions. The approach was implemented in a tool, called LusRegTes, in order to automate the test process for Lustre programs.