This thesis describes a methodology for the requirements analysis of critical real-time systems. The methodology is based on formal methods, and provides a systematic way in which requirements can be analysed and specifications produced. The proposed methodology consists of a framework with distinct phases of analysis, a set oftechniques appropriate for the issues to be analysed at each phase of the framework, a hierarchical structure of the specifications obtained from the process of analysis, and techniques to perform quality assessment of the specifications. The phases of the framework, which are abstraction levels for the analysis of the requirements, follow directly from a general structure adopted for critical real-time systems. The intention is to define abstraction levels, or domains, in which the analysis of requirements can be performed in terms of specific properties of the system, thus reducing the inherent complexity of the analysis. Depending on the issues to be analysed in each domain, the choice of the appropriate formalism is determined by the set of features, related to that domain, that a formalism should possess. In this work, instead of proposing new formalisms we concentrate on identifying and enumerating those features that a formalism should have. The specifications produced at each phase of the framework are organised by means of a specification hierarchy, which facilitates our assessment of the quality of the requirements specifications, and their traceability. Such an assessment should be performed by qualitative and quantitative means in order to obtain high confidence (assurance) that the level of safety is acceptable. In order to exemplify the proposed methodology for the requirements analysis of critical real-time systems we discuss a case study based on a crossing of two rail tracks (in a model railway), which raises safety issues that are similar to those found at a traditional level crossing (i.e. rail-road).

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Embedded systems exists everywhere around us and the number of applications seems to be ever growing. They are found in electrical devices from coee machines to aircrafts. The common denominator is that they are designed for the specic purpose of the application. Some of them are used in safety critical systems where it is crucial that they operate correct and as intended in order to avoid accidents that can harm humans or properties. Meanwhile, general purpose Commercial O The Shelf (COTS) devices that can be found in the retail store, such as smartphones and tablets, has become a natural part of everyday life in the society. New applications are developed every day that improves everyday living, but numerous are also coupled to specic devices in order to control its functionality. Interaction between embedded systems and the exible devices do however not come without issues. Security, safety and ethical aspects are some of the issues that should be considered. In this thesis, a case study was performed to investigate the feasibility of using mobile COTS products in interaction with safety critical systems with respect to functional safety. Six user scenarios were identied for investigation, which could be of interest for industrial applications; The operator presented live machine data, The operator controlling the machine remotely, The service technician using mobile device in maintenance, service technician reading machine logs from the oce, the production manager analyzing machine productivity logs from the oce and the software manager uploading software. Restrictions in the functional safety standard, IEC 61508, and the characteristics of COTS devices, leads to the conclusion that real time interaction with safety systems is not allowed if the certication is to be preserved. Extracting information used to analyze the system where data is only sent from the machine would be allowed. All scenarios where the machine sends data to the user, and the data is only used as information, are hence allowed if isolation properties are guaranteed. A prototype system was designed and parts of it were implemented to show how sending and logging information can be performed using the company developed communication platform Data Engine.

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This dissertation builds on work that has applied complex systems thinking to socio-ecological systems as well as on research that explores critical and reflective approaches to planning. A broad, interdisciplinary literature review was undertaken to explore the implications of complex and critical systems thinking and critical social epistemology for environmental management, planning and policy research, governance and social learning. Building on the insights from this review, one of the key contributions of this research is a conceptual framework that explicitly integrates knowledge and learning into an understanding of socio-ecological systems. It is argued that in the highly complex and uncertain realm of environmental policy, planning and governance, we should begin to discuss such systems as socio-ecological-epistemological (SEE) systems. This research addresses the complexity, uncertainty, high decision stakes, power relations and plurality of knowledges involved in the process of social learning in environmental planning and governance. The SEE systems conceptual framework for research and intervention was developed in the form of descriptive (Co-Evolution, Reflexive Uncertainty, Cross-scalar Considerations) and prescriptive (Critical Awareness, Pluralism, Power) principles. Based upon these principles, a critical systems-based approach to planning and policy research was developed and applied to two case studies of innovative, integrated environmental planning and governance: the Oak Ridges Moraine and the Long Point World Biosphere Reserve. A conceptual model for describing and refining the contributions of environmental movement organizations to social learning in the context of environmental governance emerged. The model describes the requirements of social learning as defined along three axes: typology of knowledge; levels of critical reflection; and, a scale axis from individual agent to larger social structures (institutions). Through this work, it is evident that insights from complex and critical systems understanding have influenced thinking in environmental management, planning, governance and social learning. Through the exploratory application of the SEE systems approach to complex environmental planning and governance systems, the SEE systems principles appear to resonate strongly with the experience of environmental movement organizations. In particular, three key findings emerged from the two exploratory, empirical case studies. First, interviewees and workshop attendees in both case study contexts described the importance of various types of knowledge, especially scientific, local technical and governance knowledge in initiating policy change. Second, research participants stressed the importance of understanding the cross-scalar dynamics that affect their ability to influence policy as well as the need to develop policy and governance structures appropriate to the scale of the issue of interest. And finally, the need for individual as well as organizational critical reflection upon policy tools and implementation, policy goals as well as the power differentials embodied in certain policy and governance structures was also highlighted in the qualitative, empirical data generated through interviews and workshops. This research reaffirms the importance of the collaboration and the collective contribution of academic researchers, civil servants and volunteer members of environmental movement organizations to fostering social learning for sustainability in the context of complex SEE systems.

La validation temporelle des systèmes temps réel est nécessaire dans le cadre d’applications critiques tels l’aéronautique, le spatial ou l’automobile. Il s’agit, dans ces systèmes, de garantir les temps de réponse des tâches ainsi que le déterminisme de leurs communications. En raison de la complexité des systèmes actuels ainsi que de leur criticité, il est nécessaire de mettre en place une démarche de conception réduisant le temps de développement et ainsi le temps de mise en marché (time-to-market), tout en réduisant les risques d’erreurs de conception.Ce contexte rend l’ingénierie dirigée par les modèles particulièrement adaptée au développement de ce type de système. Les contributions de cette thèse partent des constats suivants. Premièrement, malgré la multitude des modèles d’analyses existants les modèles actuels ne peuvent retranscrire de nombreux cas rencontrés en industrie. Pour pouvoir analyser ces cas, il est nécessaire de les adapter `a l’analyse. Le deuxième constat porte sur l’adaptation qui n’est toujours pas simple surtout quand il existe une disparité sémantique entre les langages de description d’architecture et les modèles d’analyse ce qui nécessite de les rapprocher. Le dernier constat porte sur les difficultés dans la modélisation des systèmes distribués complexes car à moins de connaître le système complet, la représentation globale et sa validation temporelle reste coûteuse.Cette thèse propose des outils et méthodes pour améliorer le processus de modélisation et d’analyses temps réel. La première contribution consiste en la mise en place d’un référentiel de transformation de modèles endogène pour effectuer une adaptation conservative des modèles industriels aux modèles d’analyses. Dans l’optique de réduire l’écart sémantique entre les langages de description et les modèles d’analyse, cette thèse propose également une modélisation incrémentale des réseaux temps réel en vue de leur validation temporelle car les langages existants les considèrent de fa¸con limitée. La troisième contribution de la thèse porte sur la réduction des artéfacts de modélisation par extraction et élagage de méta-modèles afin d’obtenir les éléments nécessaires par rapport au points de vue d’analyse souhait´es.Toutes ces contributions sont implémentées dans des frameworks intégrant les processus d’analyses temps réel tels Time4Sys et MoSaRT et utilisées, dans le cadre d’un projet collaboratif, par des partenaires industriels. / The timing validation of real-time systems is mandatory for critical applications such as aeronautics,aerospace or automotive systems. The aim is to guarantee tasks response time and messages transmission time on networks. As for the criticality of these complex systems, it is necessary to implement a design process that reduces the development time therefore the time-to-market while reducing design errors risks.This context makes model-driven engineering well adapted for the development of critical real-time systems.The contributions of this thesis rely on the following observations. First of all, despite the existence of various analysis models, they often cannot represent perfectly some industrial cases. To analyze these cases,an adaptation is required to make them analyzable with existing tests. However, the adaptation is not quite easy especially in case of a semantic gap between systems description languages and analysis models. Also,several difficulties have been noticed to design and analyze an entire distributed complex system in one-shot unless knowing well the full system.In this PhD thesis, tools and methods are proposed to ease and improve the modeling and analysis processes of real-time systems. The first thesis contribution consists of implementing a rule-based endogenous transformation repository dedicated to adapt conservatively industrial models to the analysis models. The second contribution is focused on real-time networks and is dedicated to reduce the semantic gap between description languages and analysis models by proposing artefacts allowing to design networks on an incremental way. Moreover, this thesis proposes to reduce modeling artifacts using extraction and meta-models pruning inorder to retrieve useful elements referring to chosen analysis viewpoints.All these contributions are implemented in modeling frameworks integrating real-time analyses processes such as Time4Sys and MoSaRT, and used in a collaborative project by industrial partners.

This dissertation builds on work that has applied complex systems thinking to socio-ecological systems as well as on research that explores critical and reflective approaches to planning. A broad, interdisciplinary literature review was undertaken to explore the implications of complex and critical systems thinking and critical social epistemology for environmental management, planning and policy research, governance and social learning. Building on the insights from this review, one of the key contributions of this research is a conceptual framework that explicitly integrates knowledge and learning into an understanding of socio-ecological systems. It is argued that in the highly complex and uncertain realm of environmental policy, planning and governance, we should begin to discuss such systems as socio-ecological-epistemological (SEE) systems. This research addresses the complexity, uncertainty, high decision stakes, power relations and plurality of knowledges involved in the process of social learning in environmental planning and governance. The SEE systems conceptual framework for research and intervention was developed in the form of descriptive (Co-Evolution, Reflexive Uncertainty, Cross-scalar Considerations) and prescriptive (Critical Awareness, Pluralism, Power) principles. Based upon these principles, a critical systems-based approach to planning and policy research was developed and applied to two case studies of innovative, integrated environmental planning and governance: the Oak Ridges Moraine and the Long Point World Biosphere Reserve. A conceptual model for describing and refining the contributions of environmental movement organizations to social learning in the context of environmental governance emerged. The model describes the requirements of social learning as defined along three axes: typology of knowledge; levels of critical reflection; and, a scale axis from individual agent to larger social structures (institutions). Through this work, it is evident that insights from complex and critical systems understanding have influenced thinking in environmental management, planning, governance and social learning. Through the exploratory application of the SEE systems approach to complex environmental planning and governance systems, the SEE systems principles appear to resonate strongly with the experience of environmental movement organizations. In particular, three key findings emerged from the two exploratory, empirical case studies. First, interviewees and workshop attendees in both case study contexts described the importance of various types of knowledge, especially scientific, local technical and governance knowledge in initiating policy change. Second, research participants stressed the importance of understanding the cross-scalar dynamics that affect their ability to influence policy as well as the need to develop policy and governance structures appropriate to the scale of the issue of interest. And finally, the need for individual as well as organizational critical reflection upon policy tools and implementation, policy goals as well as the power differentials embodied in certain policy and governance structures was also highlighted in the qualitative, empirical data generated through interviews and workshops. This research reaffirms the importance of the collaboration and the collective contribution of academic researchers, civil servants and volunteer members of environmental movement organizations to fostering social learning for sustainability in the context of complex SEE systems.