A Comprehensive Requirement Capturing Model Enabling the Automated Formalisation of NL Requirements

Formalising natural language (NL) requirements is essential to have formal specifications that enable formal checking and improve the quality of requirements. However, the existing formalisation techniques require engineers to (re)write the system requirements using a set of requirements templates with predefined and limited structure and semantics. The main drawback of using such templates, usually with a fixed format, is the inability to capture diverse requirements outside the scope of the template structure. To address this limitation, a comprehensive reference model is needed to enable capturing key requirement properties regardless of their format, order, or structure. NLP technique can then be used to convert unrestricted NL requirements into the reference model. Using a set of transformation rules, the reference model representing the requirements can be transformed into the target formal notation. In this paper, we introduce requirement capturing model (RCM) to represent NL requirements by adapting to their key properties and without imposing constraints on how the requirements are written. We also implemented a requirements formalisation approach that supports transforming RCM into temporal logic (TL). In addition, we developed an automated similarity checking approach to check the correctness of the constructed RCM structures against the source NL requirements. We carried out extensive evaluation of RCM by comparing it against 15 existing requirements representation approaches on a dataset of 162 requirement sentences. The results show that RCM supports a much wider range of requirements formats compared to any of the existing approaches.