Using Genetic Algorithms to Modularize Conceptual Models
Conceptual models often evolve into large and monolithic artifacts. This is a threat to comprehensibility and maintainability as the size of the resulting artefacts exceeds the cognitive processing capabilities of human beings. Breaking down monoliths into a modular structure is an established technique in software and systems engineering.
Part of this project is to realize Genetic Algorithms (GA) to automatically derive modularizations of given monolithic models. From a conceptual perspective, this project involves the challenge of encoding the problem and solution domain into a format, that can be used as an input for the GA. Moreover, a fitness function – most likely a multi-objective one – needs to be defined, based on domain-specific metrics, that yields the GA toward the good solutions. From a technological perspective, this project can extend an existing EMF and Jenetics based application or build a new one.
Possible applications can be on Entity Relationship models or other languages like BPMN, DMN, and ArchiMate.
Further Reading (Excerpt)
- Bork, Dominik, Antonio Garmendia, and Manuel Wimmer. “Towards a Multi-Objective Modularization Approach for Entity-Relationship Models.” ER Forum, Demo and Posters 2020. CEUR-WS. org, 2020. http://ceur-ws.org/Vol-2716/paper4.pdf https://model-engineering.info/img/ModulER-Presentation.pdf
- Moody, D. L., & Flitman, A. (1999). A methodology for clustering entity relationship models - a human information processing approach. In International Conference on Conceptual Modeling (pp. 114-130). Springer, Berlin, Heidelberg.