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An Approach for the Comparative Evaluation of Requirements Formalisation Approaches

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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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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