Impact of passive and negative sentences in automatic generation of static UML diagram using NLP

What is it about?

The elicitated software requirements are generally documented in natural language text form i.e. English. The main drawback of NL text is ambiguity. Thus, to remove the ambiguity between the developers and customers, there is a need to convert the text into a diagrammatic representation. The automatic conversion of software requirements text into UML models is carried out using NLP Tools and Techniques. In this paper, we have converted unstructured formats of requirement text into the class diagrams.

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Photo by Sean Lim on Unsplash

Photo by Sean Lim on Unsplash

Why is it important?

In this paper, we propose a methodology that formulates extraction rules for the passive and negative sentences and extracts class diagram elements from the requirement text. Our methodology provides better results as compared to the existing methodologies which employed only the extraction rules that can extract the class diagram elements from active and positive sentences.