Towards Aligning Multi-Concern Models via NLP

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

Towards Aligning Multi-Concern Models via NLP

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