Automatic Derivation of LQN Performance Models from UML software models using Epsilon

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Creator: 

Hassanzadeh Zargari, Mana

Date: 

2016

Abstract: 

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.

Subject: 

Engineering - Electronics and Electrical
System Science

Language: 

English

Publisher: 

Carleton University

Thesis Degree Name: 

Master of Applied Science: 
M.App.Sc.

Thesis Degree Level: 

Master's

Thesis Degree Discipline: 

Engineering, Electrical and Computer

Parent Collection: 

Theses and Dissertations

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