Our paper titled Towards Aligning Multi-Concern Models via NLP was accepted to be presented at the Model-Driven Requirements Engineering (MoDRE) Workshop. The workshop will be co-located with the IEEE Requirements Engineering Conference, taking place in Lisbon (Portugal) the 4th of September 2017. The paper briefly explains the natural language processing-based reasoning mechanisms that are implemented to support modellers in the PACAS platform by providing sets of concepts from the Air Traffic Management Information Reference Model (AIRM) as suggestions. The approach PACAS developed can be used with other domain ontologies and diverse modeling languages without unifying their meta-models.
The design of large-scale complex systems requires their analysis from multiple perspectives, often through the use of requirements models. Diversely located experts with different backgrounds (e.g., safety, security, performance) create such models using different requirements modeling languages. One open challenge is how to align these models such that they cover the same parts of the domain. We propose a technique based on natural language processing (NLP) that analyzes several models included in a project and provides suggestions to modelers based on what is represented in the models that analyze other concerns. Unlike techniques based on meta-model alignment, ours is flexible and language agnostic. We report the results of a focus group session in which experts from the air traffic management domain discussed our approach.
The preprint of Towards Aligning Multi-Concern Models via NLP can be accessed here.