Enhancing the extraction of SBVR business vocabularies and business rules from UML use case diagrams with natural language processing

Abstract

Being among the best-selling and most advanced features of model-driven development, model-to-model transformation could help improving one of the most time- and resource-consuming efforts in the process of model-driven information systems engineering, namely, discovery and specification of business vocabularies and business rules within the problem domain. Nonetheless, despite the relatively high levels of automation throughout the whole systems' model-driven development process, business modeling stage remains among the most under re-searched areas throughout the whole process. In this paper, we introduce a novel natural language processing (NLP) technique to one of our latest developments for the automatic extraction of SBVR business vocabularies and business rules from UML use case diagrams. This development remains arguably the most comprehensive development of this kind currently available in public. The experiment provided proof that the developed NLP enhancement delivered even better extraction results compared to the already satisfactory performance of the previous development. This work contributes to the research in the areas of model transformations and NLP within the model-driven development of information systems, and beyond.