Cover image
by Mehmet Aksit, Mark van den Brand, Loek Cleophas, Önder Babur, Bedir Tekinerdogan
Model Management and Analytics for Large Scale Systems
- Cover image
- Title page
- Table of Contents
- Copyright
- Contributors
- Analysis in the large: A foreword
- Preface
- Part 1: Concepts and challenges
- Chapter 1: Introduction to model management and analytics
- 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
- Chapter 4: Domain-driven analysis of architecture reconstruction methods
- 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
- Chapter 7: Structuring large models with MONO: Notations, templates, and case studies
- Chapter 8: Delta-oriented development of model-based software product lines with DeltaEcore and SiPL: A comparison
- 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
- Chapter 5: Monitoring model analytics over large repositories with Hawk and MEASURE
- Part 3: Industrial applications
- Chapter 10: Reducing design time and promoting evolvability using Domain-Specific Languages in an industrial context
- 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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