Component Based Software Development and Domain Engineering

1. Component Based Software Developmentand Domain Engineering

2. Outline • Main Ideas • Detailed Map • Problem Description • Potential Approach • Related Work

3. Main Ideas

4. Detailed Map

5. Problems • How to scope the “problem domain”? • How to define “domain analysis process” in agile way? • What is the significance of “human factor”? • How to achieve “conceptual reuse” in CBSD? • How to identify and represent “variation points”? • How to automate the “domain engineering”?

6. How to scope the “problem domain”? • Problem Description: • Current product line scoping approaches fail to appropriately define the right set of alternatives with respect to business requirements. • Potential Approach: • Design Space Modeling • Related Work: • Product lines scoping • Design space modeling • Domain engineering

7. How to define “domain analysis process” in agile way? • Problem Description: • Domain analysis is a heavy process which requires lots of time and effort and inherently seems to contradict with agile approaches. On the other hand for deriving the right concepts we need to still apply domain analysis in agile methods. In this problem we focus on the integration of agile and domain analysis processes. • Potential Approach: • Applying agile practices in domain analysis practices and vice versa (integrating). • Related Work: • Domain analysis • Method engineering • Process engineering • Meta-modeling

8. What is the significance of “human factor”? • Problem Description: • How to assess the expertise of the domain engineering team of the project? • Potential Approach: • Assessment and metrics of human expertise • Related Work: • Metrics • Knowledge management • Human factors engineering • Participatory design and teamworking

9. How to achieve “conceptual reuse” in CBSD? • Problem Description: • after cataloging domain models (assets) it is very hard to identify and search the assets with respect to the given semantic requirements. • Potential Approach: • Smart querying and indexing of data • Smart (semantic) matching algorithm • Integrating (google) like approaches in domain analysis process. • Related Work: • Knowledge management & engineering • Conceptual modeling • Semantic spaces • Semantic search

10. How to identify and represent “variation points”? • Problem Description: • Current variability analysis represents statically but we would like to address conditional variation. • Conditions may vary on time, requirements, etc. • Potential Approach: • Rule-based definition of variability • Extending feature diagrams • Related Work: • Configuration and reconfiguration management • Modeling conditional variation • Exploiting automated tools

11. How to automate the “domain engineering”? • Problem Description: • Existing domain engineering approaches focus on manually identification, specification and evaluation of knowledge sources. In order to support the domain engineer with routine activities we aim to develop tools. • Potential Approach: • Defining Meta-Models for Knowledge Sources, Concepts and Catalogs. • Tool development • Related Work: • Ontology • Knowledge engineering • Meta-modeling