  Most existing automated requirements formalisation techniques require system
engineers to (re)write their requirements using a set of predefined requirement
templates with a fixed structure and known semantics to simplify the
formalisation process. However, these techniques require understanding and
memorising requirement templates, which are usually fixed format, limit
requirements captured, and do not allow capture of more diverse requirements.
To address these limitations, we need a reference model that captures key
requirement details regardless of their structure, format or order. Then, using
NLP techniques we can transform textual requirements into the reference model.
Finally, using a suite of transformation rules we can then convert these
requirements into formal notations. In this paper, we introduce the first and
key step in this process, a Requirement Capturing Model (RCM) - as a reference
model - to model the key elements of a system requirement regardless of their
format, or order. We evaluated the robustness of the RCM model compared to 15
existing requirements representation approaches and a benchmark of 162
requirements. Our evaluation shows that RCM breakdowns support a wider range of
requirements formats compared to the existing approaches. We also implemented a
suite of transformation rules that transforms RCM-based requirements into
temporal logic(s). In the future, we will develop NLP-based RCM extraction
technique to provide end-to-end solution.
