WP4: Integration, Validation, and Dissemination
Integrating results, validation, case studies, and community dissemination
WP4 integrates findings from other work packages, validates the approaches through case studies, and disseminates results to the research community and practitioners.
Objectives
Case studies are central to the CLEM project: they drive requirements analysis for the interfaces and decision-making mechanisms (WP2), and they validate the usability and effectiveness of LEM approaches across realistic modeling scenarios. By selecting diverse industrial and academic case studies, we ensure that LEM concepts and tools generalize beyond our immediate research context.
Partner Contributions
U.Rennes: Provides expertise in xDSML design and abstract syntax development for case studies, working closely with industrial partners to extract and formalize domain requirements.
JKU: Contributes expertise in search-based model transformations and exploration algorithms (via MOMoT), helping to define exploration strategies and validate LEM techniques against realistic test and optimization scenarios.
Tasks
Task T4.1: Case Studies Requirements for Live and Exploratory Modeling (Lead: U.Rennes, PostDoc)
Description: Selection and analysis of industrial and academic case studies to guide LEM design. Requirements elicitation from domain experts, formalization of modeling scenarios, identification of key challenges in model development and exploration. This task ensures that LEM features directly address real-world modeling needs and scalability constraints.
Challenge: Balancing diverse domain-specific requirements while maintaining coherence with LEM concepts; managing industrial partner engagement and data confidentiality.
Task T4.2: Case Studies Experimentation and Validation (Lead: JKU, PhD Researcher)
Description: Experimental validation of LEM concepts and tools through implementation and execution of case study models. Evaluation of live execution performance, exploratory modeling effectiveness, and user experience. Gathering metrics on model quality, discovery time, and developer productivity using the LEM-enabled workbench. Comparative analysis against baseline modeling approaches.
Challenge: Defining relevant metrics and establishing fair baselines for comparison; ensuring case studies are representative without becoming unwieldy.