A Modelling Method for Embedded Systems

We suggest a systematic modelling method for embedded systems. The goal is to derive models (1) that share the relevant properties with the original system, (2) that are suitable for computer aided analysis, and (3) where the modelling process itself is transparent and efficient, which is necessary to detect modelling errors early and to produce model versions (e.g. for product families). Our aim is to find techniques to enhance the quality of the model and of the informal argument that it accurately represents the system. Our approach is to use joint decomposition of the system model and the correctness property, guided by the structure of the physical environment, following, e.g., engineering blueprints. In this short note we describe our approch to combine Jackson¿s problem frame approach [1, 2] with a stepwise refinement method to arrive at provably correct designs of embedded systems

TrainMiC® Presentations Translated in Serbian

TrainMiC® is a European programme for life-long learning about how to interpret the metrological requirements in chemistry. It is operational across many parts of Europe via national teams. These teams use shareware pedagogic tools which have been harmonized at European level by a joint effort of many experts across Europe working in an editorial board. The material has been translated into fourteen different languages. In this publication, TrainMiC® presentations translated in Serbian language by the Serbian TrainMiC® team are published.JRC.D.3-Knowledge Transfer and Standards for Securit

Classifying Assumptions Made During Requirements Verification of Embedded Systems

We are investigating ways to improve the process of modelling of embedded systems for formal verification. In the modelling process, we make a mathematical model of the system software and its environment (the plant), and we prove that the requirement holds for the model. But we also want to have an argument that increases our confidence that the model represents the system correctly (with respect to the requirement). Therefore, we document some of the modelling decisions in form of a list of the system assumptions made while modelling. Identifying the assumptions and deciding which ones are relevant is a difficult task and it cannot be formalized. To support this process, we give a classification of assumptions. We show our approach on an example

Validation of Embedded System Verification Models

The result of a model-based requirements verification shows that the model of a system satisfies (or not) formalised system requirements. The verification result is correct only if the model represents the system adequately. No matter what modelling technique we use, what precedes the model construction are non-formal activities. During these activities the modeller has to learn how the system works, what the requirements are, and to decide what is relevant to model and how to do it. Due to a partly non-formal nature of modelling steps, we do not have a formal proof that the model represents the system adequately. The most we can do is to increase the confidence in the model. In this paper we explore non-formal model validation steps while designing a formal model. On the example of a Uppaal performance model we designed in a company that produces printers, we will show what validation steps were necessary to increase the stakeholders' confidence in the model. Based on this case study, we propose more general, but non-formal model validation steps, that can structure model validation. The steps we propose deal with the same design elements and issues present in other model-based verification activities, therefore can accompany them as well

A Modelling Method for Embedded Systems

We suggest a systematic modelling method for embedded systems. The goal is to derive models (1) that share the relevant properties with the original system, (2) that are suitable for computer aided analysis, and (3) where the modelling process itself is transparent and efficient, which is necessary to detect modelling errors early and to produce model versions (e.g. for product families). Our aim is to find techniques to enhance the quality of the model and of the informal argument that it accurately represents the system. Our approach is to use joint decomposition of the system model and the correctness property, guided by the structure of the physical environment, following, e.g., engineering blueprints. In this short note we describe our approch to combine Jacksons problem frame approach with a stepwise refinement method to arrive at provably correct designs of embedded systems

Reusing knowledge in embedded system modelling

Model-based design is a promising technique to improve the quality of software and the efficiency of the software development process. We are investigating how to efficiently model embedded software and its environment to verify the requirements for the system controlled by the software. The software environment consists of mechanical, electrical and other parts; modelling it involves learning how these parts work, deciding what is relevant to model and how to model it. It is not possible to fully automate these steps. There are general guidelines, but given that every modelling problem differs, much is left to the modeller's own preference, background and experience. Still, when the next generation of a system is designed, the new system will have common elements with its previous version. Therefore, lessons learned from the current model could inform future models. We propose a framework for identifying the non-formal elements of knowledge, insights and a model itself, which can support modelling of the next system generation. We will present the application of our framework on an action research case – modelling mechanical parts of a paper-inserting machine

Practical Examples on Traceability, Measurement Uncertainty and Validation in Chemistry, Volume 2

Case studies on traceability, measurement uncertainty and validation for measurements of retinol and alfα-tocopherol in human serum, cyclamate in soft drinks, arsenic in ground water, sodium chloride in milk products, total organic carbon in waste water are presented in this book. Additionally, the idea and structure of the TrainMic® examples, which complement the TrainMic® theoretical presentations, are described in detail to give complete overview of the TrainMic® teaching material.JRC.D.4-Isotope measurement