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by Mehmet Aksit, Mark van den Brand, Loek Cleophas, Önder Babur, Bedir Tekinerdogan
Model Management and Analytics for Large Scale Systems
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Title page
Table of Contents
Copyright
Contributors
Analysis in the large: A foreword
Preface
Introduction
Why a book on model management and analytics
Book outline
Part 1: Concepts and challenges
Chapter 1: Introduction to model management and analytics
Abstract
1.1. Introduction
1.2. Data analytics concepts
1.3. The inflation of modeling artifacts
1.4. Relevant domains for MMA
References
Chapter 2: Challenges and directions for a community infrastructure for Big Data-driven research in software architecture
Abstract
2.1. Introduction
2.2. Related work
2.3. Experiences in creating & sharing a collection of UML software design models
2.4. Challenges for Big Data-driven empirical studies in software architecture
2.5. Directions for a community infrastructure for Big Data-driven empirical research in software architecture
2.6. Overview of CoSARI
2.7. Summary and conclusions
References
Chapter 3: Model clone detection and its role in emergent model pattern mining
Abstract
3.1. Introduction
3.2. Background material
3.3. MCPM – a conceptual framework for using model clone detection for pattern mining
3.4. Summary of challenges and future directions
3.5. Conclusion
References
Chapter 4: Domain-driven analysis of architecture reconstruction methods
Abstract
4.1. Introduction
4.2. Preliminaries
4.3. Domain model of architecture reconstruction methods
4.4. Concrete architecture reconstruction method
4.5. Related work
4.6. Discussion
4.7. Conclusion
Appendix 4.A. Primary studies
References
Part 2: Methods and tools
Chapter 5: Monitoring model analytics over large repositories with Hawk and MEASURE
Abstract
Acknowledgements
5.1. Introduction
5.2. Motivation
5.3. Background
5.4. Monitoring model analytics over large repositories with Hawk and MEASURE
5.5. Case study: the DataBio models
5.6. Related projects
5.7. Conclusions
Appendix 5.A. Running example
Appendix 5.B. EOL-based ArchiMate metric implementation
References
Chapter 6: Model analytics for defect prediction based on design-level metrics and sampling techniques
Abstract
6.1. Introduction
6.2. Background and related work
6.3. Methodology
6.4. Experimental results
6.5. Discussion
6.6. Conclusion
References
Chapter 7: Structuring large models with MONO: Notations, templates, and case studies
Abstract
7.1. Introduction
7.2. Modeling in the large
7.3. Structuring big models
7.4. Describing and specifying model structures
7.5. Case study 1: Library Management System (LMS)
7.6. Case study 2: BIENE Erhebung (ERH)
7.7. Discussion
7.8. Conclusions
References
Chapter 8: Delta-oriented development of model-based software product lines with DeltaEcore and SiPL: A comparison
Abstract
8.1. Introduction
8.2. Running example
8.3. Delta modeling for MBSPLs
8.4. Delta modeling with DeltaEcore and SiPL
8.5. Capabilities of DeltaEcore and SiPL
8.6. Related work
8.7. Conclusion
References
Chapter 9: OptML framework and its application to model optimization
Abstract
9.1. Introduction
9.2. Illustrative example, problem statement, and requirements
9.3. The architecture of the framework
9.4. Examples of models for registration systems based on various architectural views
9.5. Model processing subsystem
9.6. Model optimization subsystem
9.7. Related work
9.8. Evaluation
9.9. Conclusion
Appendix 9.A. Feature model
Appendix 9.B. Platform model
Appendix 9.C. Process model
Appendix 9.D. The instantiation of the value metamodel for energy consumption and computation accuracy
References
Part 3: Industrial applications
Chapter 10: Reducing design time and promoting evolvability using Domain-Specific Languages in an industrial context
Abstract
10.1. Introduction
10.2. Domain-Specific Languages
10.3. State of the art
10.4. Approach to practical investigation
10.5. DSL ecosystem design
10.6. Results of practical investigation
10.7. Evaluation
10.8. Conclusions
References
Chapter 11: Model analytics for industrial MDE ecosystems
Abstract
11.1. Introduction
11.2. Objectives
11.3. Background: SAMOS model analytics framework
11.4. MDE ecosystems at ASML
11.5. Model clones: concept and classification
11.6. Using and extending SAMOS for ASOME models
11.7. Case studies with ASML MDE ecosystems
11.8. Discussion
11.9. Related work
11.10. Conclusion and future work
References
Index
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