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Class Diagram Generation from Text Requirements: An Application of Natural Language Processing
Faculty
Science
Year:
2021
Type of Publication:
ZU Hosted
Pages:
Authors:
Mohamed El Sayed Ahmed Muhamed
Staff Zu Site
Abstract In Staff Site
Journal:
Deep Learning Approaches for Spoken and Natural Language Processing springer
Volume:
Keywords :
Class Diagram Generation from Text Requirements:
Abstract:
The software development scheme starts with identifying the requirements for the analysis. The requirements’ process levels begin from analyzing the requirements to sketching the program’s design, which is an essential task for programmers and software engineers. The motivation behind this task is related to the designations of software requirements created in the natural language (NL). To minimize these errors, we can transfer the software requirements to a computerized form using the UML diagram. This research proposes a new method to offer a UML class version from software program specifications using natural language processing (NLP) techniques. The proposed method presents the class diagram in a known configuration and figures out the relationship between the instruction and the class diagram. In this chapter, we aim to enhance the procedure of producing the UML diagrams by utilizing the natural language, which will help the software developers to analyze the software requirements with fewer errors and in an efficient way. The proposed method produced more reliable results compared to the existing techniques available. The proposed approach provides a better analysis of the given requirements and delivers better diagrams while also helping the software engineers.
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Mohamed El Sayed Ahmed Muhamed, "Efficient schemes for playout latency reduction in P2P-VOD systems", Springer, 2018
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Mohamed El Sayed Ahmed Muhamed, "a novel algorithm for source localization based on nonnegative matrix factroization using \alpha 'beta divergence in chochleagram", WSEAS, 2013
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Mohamed El Sayed Ahmed Muhamed, "Open cluster membership probability based on K-means clustering algorithm", Springer, 2016
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