Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.638461
Title: Model transformation for multi-objective architecture optimisation for dependable systems
Author: Mian, Zhibao
ISNI:       0000 0004 5362 1118
Awarding Body: University of Hull
Current Institution: University of Hull
Date of Award: 2014
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Abstract:
Model-based engineering (MBE) promises a number of advantages for the development of embedded systems. Model-based engineering depends on a common model of the system, which is refined as the system is developed. The use of a common model promises a consistent and systematic analysis of dependability, correctness, timing and performance properties. These benefits are potentially available early and throughout the development life cycle. An important part of model-based engineering is the use of analysis and design languages. The Architecture Analysis and Design Language (AADL) is a new modelling language which is increasingly being used for high dependability embedded systems development. AADL is ideally suited to model-based engineering but the use of new language threatens to isolate existing tools which use different languages. This is a particular problem when these tools provide an important development or analysis function, for example system optimisation. System designers seek an optimal trade-off between high dependability and low cost. For large systems, the design space of alternatives with respect to both dependability and cost is enormous and too large to investigate manually. For this reason automation is required to produce optimal or near optimal designs. There is, however, a lack of analysis techniques and tools that can perform a dependability analysis and optimisation of AADL models. Some analysis tools are available in the literature but they are not able to accept AADL models since they use a different modelling language. A cost effective way of adding system dependability analysis and optimisation to models expressed in AADL is to exploit the capabilities of existing tools. Model transformation is a useful technique to maximise the utility of model-based engineering approaches because it provides a route for the exploitation of mature and tested tools in a new model-based engineering context. By using model transformation techniques, one can automatically translate between AADL models and other models. The advantage of this model transformation approach is that it opens a path by which AADL models may exploit existing non-AADL tools. There is little published work which gives a comprehensive description of a method for transforming AADL models. Although transformations from AADL into other models have been reported only one comprehensive description has been published, a transformation of AADL to petri net models. There is a lack of detailed guidance for the transformation of AADL models. This thesis investigates the transformation of AADL models into the HiP-HOPS modelling language, in order to provide dependability analysis and optimisation. HiP-HOPS is a mature, state of the art, dependability analysis and optimisation tool but it has its own model. A model transformation is defined from the AADL model to the HiP-HOPS model. In addition to the model-to-model transformation, it is necessary to extend the AADL modelling attributes. For cost and dependability optimisation, a new AADL property set is developed for modelling component and system variability. This solves the problem of describing, within an AADL model, the design space of alternative designs. The transformation (with transformation rules written in ATLAS Transformation Language (ATL)) has been implemented as a plug-in for the AADL model development tool OSATE (Open-source AADL Tool Environment). To illustrate the method, the plug-in is used to transform some AADL model case-studies.
Supervisor: Bottaci, Leonardo Sponsor: University of Hull ; MAENAD (Project)
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.638461  DOI: Not available
Keywords: Computer science
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