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