Automatic Support for Multi-Domain Model Management

The process of developing complex systems often involves knowledge of engineers from multiple domains: e.g., to develop a robot one needs to combine expertise about mechanics, electronics, and software. Such domain-specific knowledge is often represented in a form of interdependent models, consequently a change in a model of one domain might impact a model from a different domain. Thus, identifying which models are affected due to a change is an important problem, which is further exacerbated due to heterogeneity of modeling notations used.The aim of this PhD research project is to facilitate model management in a multi-domain setting. In the earlier stage of this study, we investigated the available approaches used to manage models from different domains. We concluded that the available approaches are tool-dependent, and do not fully support co-evolution of the models. Additionally, previous research recommends to explicitly indicate the dependency between models in order to support the co-evolution of models from different domains. Since these models are created using different modeling notations we believe that it is not reasonable to develop a tool to parse every notation. Furthermore, it is possible that the source code of the model is missing, but engineers still have an image of the model. Thus, to ensure the maintenance of multi-domain systems we investigated the suitability of optical character recognition (OCR) as a uniform approach. We observed that even though OCR has shortcomings, it produces satisfactory results, and once the identified shortcomings are addressed, OCR can become a crucial technology to support the evolution of multi-domain systems. To this end we envision the development of an infrastructure where we can use OCR to identify relationships between models from different domains, store them in a structured manner making it easier to maintain the consistency of the entire system.