Automatic Derivation of LQN Performance Models from UML software models using Epsilon
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Model-Driven Development (MDD) is an emerging software paradigm that raises the level of abstraction of software development by changing the focus from code to models and automates the generation of code from models. MDD also facilitates the analysis of non-functional properties, such as performance, in the early software development phases. The objective of this thesis is to develop a model transformation process that takes as input a UML software model with MARTE performance annotations, and generates a corresponding Layered Queueing Network (LQN) performance model in a format understood by the existing LQN tools. The transformation is developed in Epsilon, a new family of languages specialized in model transformations, refinement and management.
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Copyright © 2016 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.
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hassanzadehzargari-automaticderivationoflqnperformancemodels.pdf | 2023-05-04 | Public | Download |