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Issue title: Special section: Selected papers of LKE 2019
Guest editors: David Pinto, Vivek Singh and Fernando Perez
Article type: Research Article
Authors: Shweta, a; * | Sanyal, Ratnab
Affiliations: [a] Information Technology, Indian Institute of Information Technology Allahabad, Allahabad, Uttar Pradesh, India | [b] NIIT University, Neemrana, Alwar, Rajasthan, India
Correspondence: [*] Corresponding author. Shweta, Department of Information Technology, Indian Institute of Information Technology Allahabad, Jhalwa, Prayagraj, Uttar Pradesh, 211012, India. E-mail: [email protected].
Abstract: In this research work, we propose a rule based approach for the automatic extraction of UML diagram from the unstructured format of software functional requirements. The existing work provides decent results for active sentences and positive sentences but the challenge in our work is to automatic extract class diagram elements from passive voice type sentences and negative sentences. Furthermore, there is scope to do more research in extraction process using multi-word terms. Thus, we have endeavored to automatic extract the class diagram elements by overcoming these challenges. The methodology uses the Stanford CoreNLP Tools along with Java for the practical implementation of formulated rules. Our approach has proved that without supplant the human being and their decision making, one could reduce the human effort while designing functional requirements. Several case studies were performed to compare class diagrams generated by our methodology to the ones created by experts. Our methodology outperforms the existing work and provides impressive Average completeness (0.82), Average correctness (0.92) and Average redundancy (0.15). Results show that class diagram elements extracted by our methodology are precise as well as accurate and hence, in practice, such class diagrams would be a good preliminary diagram to converge towards to precise and comprehensive class diagrams.
Keywords: Unified modeling language, class diagram, natural language processing, functional requirements
DOI: 10.3233/JIFS-179871
Journal: Journal of Intelligent & Fuzzy Systems, vol. 39, no. 2, pp. 2047-2059, 2020
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