Kolahdouz Rahimi, S;
Lano, K;
Yassipour Tehrani, S;
Lin, C;
Liu, Y;
Umar, MA;
(2026)
An Approach for the Comparative Evaluation of Requirements Formalisation Approaches.
In: José Domínguez Mayo, F and Ferreira Pires, L and Seidewitz, E, (eds.)
Model-Based Software and Systems Engineering.
(pp. pp. 132-150).
Springer Nature Switzerland
(In press).
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Yassipour Tehrani_Comparative_evaluation_of_requirements_formalisation_approaches.pdf - Accepted Version Access restricted to UCL open access staff until 22 August 2026. Download (562kB) |
Abstract
Various approaches have been proposed to automate the formalisation of software requirements from semi-formal or informal documents. However, this area of research lacks well-established case studies to serve as benchmarks for comparing different methods. Additionally, there is a need for clear, objective criteria to effectively assess the outcomes of these formalisation approaches. These gaps make it challenging to identify which techniques are most suitable for specific formalisation tasks. This paper addresses these issues by introducing a set of standardized case studies and a structured framework for evaluating the performance of requirements formalisation techniques using measurable criteria. We apply this evaluation framework to assess five different formalisation methods, which include both rule-based and machine learning-driven approaches.
| Type: | Proceedings paper |
|---|---|
| Title: | An Approach for the Comparative Evaluation of Requirements Formalisation Approaches |
| Event: | Model-Based Software and Systems Engineering 12th International Conference, MODELSWARD 2024 |
| ISBN-13: | 9783031968402 |
| DOI: | 10.1007/978-3-031-96841-9_7 |
| Publisher version: | https://doi.org/10.1007/978-3-031-96841-9_7 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Requirements formalisation, Model-driven engineering, NLP, Machine learning |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10217327 |
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