We are happy to see the first article in the domain of EA Debt being published. It results from two
Naturally, with the increasing complexity of the modeled system under study, also the complexity of the model itself increases. Although EA modeling is widely adopted in industry and much research is conducted in the field, the analysis of EA models is surprisingly underrepresented. Generally, two analysis approaches can be distinguished: manual and automated. Given the discussed complexity of EA models, manual analysis can be error-prone. Automated model analysis can mitigate this problem by scaling well and by providing interactive analysis means that extend static ones.
We analyze automatically and efficiently even large EA models with the aim to detect EA Smells. Generally, a smell describes a qualitative issue that effects future efforts (e.g., maintenance) and not the functionality. While Code Smells analyze source code, EA Smells analyze an organization from a more holistic point of view and go beyond a technical scope.
To allow the analysis of other EA models than ArchiMate and to realize a scalable approach, we generalize the EA model to a Knowledge Graph (KG) and provide queries representing respective EA Smells. Hence, the detection of EA Smells can be applied to all EA models, which can be represented as a KG. We propose a generic and extensible platform that facilitates the transformation of EAs into KG representations. The platform can be easily extended to support further modeling languages. Once a transformation is realized, the existing EA Smells queries can be efficiently executed even on very large models and model corpora.
The core platform (see figure above) allows the transformation of ArchiMate models into graph structures. We enhance this platform with the capability to transform EA models conforming to the Open Group Exchange format to a KG. Further, we enhanced the platform by means of semantic queries to automatically detect EA Smells.
In: Barn, Balbir S.; Sandkuhl, Kurt (Ed.): The Practice of Enterprise Modeling. PoEM 2022, Springer International Publishing, Cham, 2022.
In: Leopold, Henrik; Proper, Henderik A. (Ed.): EMISA 2022, Gesellschaft für Informatik e.V. 2022.
Using Knowledge Graphs to Detect Enterprise Architecture Smells Proceedings Article
In: Serral, Estefanía; Stirna, Janis; Ralyté, Jolita; Grabis, Jānis (Ed.): The Practice of Enterprise Modeling, pp. 48–63, Springer International Publishing, Cham, 2021, ISBN: 978-3-030-91279-6.
In: Ghose, Aditya; Horkoff, Jennifer; Souza, Vitor E. Silva; Parsons, Jeffrey; Evermann, Joerg (Ed.): 40th International Conference on Conceptual Modeling, Springer Springer, LNCS, 2021.
In: 2021 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C) – Tools & Demonstrations Track, ACM/IEEE IEEE Xplore Digital Library, USA, 2021.