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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 6, 2021.
Abstract: The User Story format has become the most popular way of expressing requirements in Agile methods. However, a requirement does not state how a solution will be physically achieved. The purpose of this paper is to present a new approach that automatically transforms user stories into UML diagrams. Our approach aims to automatically generate UML diagrams, namely class, use cases, and package diagrams. User stories are written in natural language (English), so the use of a natural language processing tool was necessary for their processing. In our case, we have used Stanford core NLP. The automation approach consists of the combination of rules formulated as a predicate and an ontological model. Prolog rules are used to extract relationships between classes and eliminate those that are at risk of error. To extract the design elements, the prolog rules used dependencies offered by Stanford core NLP. An ontology representing the components of the user stories was created to identify equivalent relationships and inclusion use cases. The tool developed was implemented in the Python programming language and has been validated by several case studies.
Samia Nasiri, Yassine Rhazali, Mohammed Lahmer and Amina Adadi, “From User Stories to UML Diagrams Driven by Ontological and Production Model” International Journal of Advanced Computer Science and Applications(IJACSA), 12(6), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120637
@article{Nasiri2021,
title = {From User Stories to UML Diagrams Driven by Ontological and Production Model},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0120637},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0120637},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {6},
author = {Samia Nasiri and Yassine Rhazali and Mohammed Lahmer and Amina Adadi}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.