Formal models from controlled natural language via cognitive grammar and configuration

In the past decade the rise of Model-Driven Engineering has resulted in major changes to industrial software development. Using models for describing behaviours allows replacing part of the coding activities with higher-level definitions of concepts and behaviour: commonly cited as reducing costs, errors and time-to-market, while improving software quality and communication between stakeholders. However, we continue to see spectacular failures of large, expensive IT projects, often blamed on badly specified requirements. With high-level models of business environments—independent of technical considerations—now possible and standards like the Semantics of Business Vocabulary and Business Rules (SBVR) emphasising business and domain experts as the owners of their business requirements, we are reaching the point where formal modelling of requirements by the business experts themselves is necessary. However, requirements specifications written in natural language (e.g. English) still play a fundamental role in communicating the requirements. This thesis investigates an approach to semi-automatically transforming natural language specifications written by business/domain experts into formal models. The aim is to support the user in the process of formalising their requirements as models to reduce errors, ambiguities,inconsistencies, and the time taken. Thesis (PhD)--University of South Australia, 2016. Includes bibliographical references (pages 243-261)