Inhalt

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   Abstract
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   Kurzfassung
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   Contents
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   List of Figures
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   List of Tables
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   List of Algorithms
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   Introduction and Preliminaries
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  Introduction
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   Introduction
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   Research Goals and Contributions
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   Dissertation Structure
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   Publications Associated to this Dissertation
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  Preliminaries and Background
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   A Glimpse into the Model-Driven Engineering World
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   Model-Driven Architecture
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   Models and Meta-Models
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   Abstraction Layers and Meta Object Facility
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   UML, XMI and OCL
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   Model Transformations and QVT
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   Model Versioning
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   Graph Representation of Models
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   Model Differencing
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   Model Differencing Concepts
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   Model Differencing Approaches
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   Generic Model Differencing Approaches
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   Difference Computation In This Dissertation
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   SiDiff Differences Computation Engine
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   SiLift Semantic Difference Lifting Engine
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   Summary
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   Generating Test Models
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   Controlled Generation of Models with Defined Properties
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   Introduction and Background
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   Existing Approaches for Generating Models
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   Direct Non-formal Approaches
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   Direct Formal Approaches
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   Indirect Approaches
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   Summary of the Reviewed Literature
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   SiDiff Model Generator
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   Requirements
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   Overview
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   Main Usage Scenarios
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   Interpretation Modes
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   Model Modification Process
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   Edit Operations of Models
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  Controlling the Generation Process
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   Model Properties in the Generation Process
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   Selection Policies
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   Decision Tables
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   Evaluation
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   Summary
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   Analysis of Design Models Evolution
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  Capturing the Evolution of Design Models
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   Motivation
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  Structural Differencing of Models
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   Representation and Editing of Models
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   Differencing of Models
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   Difference Calculation Using the SiDiff/SiLift Framework
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   Application to Evolving Java-based Systems
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   Example
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   Representation of Java Projects as Models
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   Low-level Changes
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   High-Level Changes
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   Selection of Sample Projects and the Data Sets
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   Summary
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   Statistical Analysis and Simulation of Design Models Changes
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   Statistical Models for Describing Changes
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   Mathematical Requirements
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   Discrete Pareto Distribution and Power Law
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   Beta Binomial Distribution
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   Yule, Waring and Beta-Negative Binomial Distributions
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   Generalized Poisson Distributions
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   Analysis of Changes and Results
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  Analysis of Low-Level Changes
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   Discrete Pareto Distribution
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   Beta Binomial Distribution
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   Yule Distribution
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   Waring Distribution
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   Beta-Negative Binomial Distribution
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   Generalized Poisson Distribution
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   Analysis of High-Level Changes
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   Conclusion of Analyses
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   Threats to the Validity of Analyses
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  Generating Random Variates of the Proposed Distributions
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   Introduction to Random Variate Generation
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   Random Variates of the Discrete Pareto Distribution
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   Random Variates of the Yule, Waring and Beta-Negative Binomial Distributions
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   Random Variates of the Beta Distribution
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   Random Variates of the Negative Binomial Distribution
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   Random Variates of the Beta Binomial Distribution
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   Random Variates of the Generalized Poisson Distribution
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   Summary of Random Variate Generations
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   Related Works
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   Summary
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   Time Series Analysis and Simulation of Design Models Evolution
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   Time Series
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   Stationary Time Series
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   General Linear Process and the Wold Decomposition Theorem
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   ARMA, GARCH and ARMA-GARCH Models
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   ARMA and ARIMA Models
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   ARCH and GARCH Models
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   ARMA-GARCH Models
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  Methodology for Time Series Modeling
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   Methodology of ARMA and ARIMA Models
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   Methodology of GARCH Models
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   Methodology of ARMA-GARCH Models
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   Accuracy of Forecasts
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  Modeling the Evolution
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   Data Description and Transformation
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   Time Series Models of Evolution
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   Estimation and Diagnostics of the Time Series Models
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   Assessing the Time Series Models
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   Comparing ARMA and ARMA-GARCH Models
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  Forecasting Performance of the Time Series Models
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   Accuracies of Forecasts
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   Comparing Accuracies of Forecasts
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   Simulation of Model Evolution
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   General Considerations for Simulation
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  Simulating Sequences of the Proposed Time Series Model
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   Random Variates of the Normal Distribution
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   Initial Conditions in the Simulation of the Time Series Models
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   Generating More Realistic Model Histories
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  Threats to the Validity of Analyses
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   Accuracy in the Measurement of Changes
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   Best Model Selection Strategy
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   Forecasting Performance of the Time Series Models
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   External Validity
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   Validity of the Simulation
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   Related Works
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   Summary and Conclusion
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   Conclusions and Outlook
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  Conclusions and Outlook
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   Summary and Conclusion
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   Outlook and Future Research Directions
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   References