RCM: Requirement Capturing Model for Automated Requirements Formalisation

TL;DR

This paper introduces the Requirement Capturing Model (RCM), a flexible reference framework that captures key requirement details regardless of format, enabling more diverse requirements formalisation through NLP and transformation rules.

Contribution

The paper presents the first reference model for requirements capturing that supports diverse formats and demonstrates its robustness compared to existing approaches.

Findings

RCM supports a wider range of requirement formats.

RCM outperforms 15 existing approaches in robustness.

Transformation rules convert RCM requirements into temporal logic.

Abstract

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.