Towards a Generation of Class Diagram From User Stories in Agile Methods

Towards a Generation of Class Diagram From User Stories in Agile Methods

Samia Nasiri, Yassine Rhazali, Mohammed Lahmer
Copyright: © 2021 |Pages: 25
DOI: 10.4018/978-1-7998-3661-2.ch008
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Model-driven architecture (MDA) is an alternative approach of software engineering that allows an automatic transformation from business process model to code model. In MDA there are two transformation kinds: transformation from computing independent model (CIM) to platform independent model (PIM) and transformation from PIM to platform specific model (PSM). In this chapter, the authors based on CIM to PIM transformation. This transformation is done by developing a platform that generates class diagram, presented in XMI file, from specifications that are presented in user stories, which are written in natural language (English). They used a natural language processing (NLP) tool named “Stanford Core NLP” for extracting of the object-oriented design elements. The approach was validated by focusing on two case studies: firstly, comparing the results with the results other researchers; and secondly, comparing the results with the results obtained manually. The benefits of the approach are aligned with agile methods goals.
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Several approaches have been developed in natural language processing (NLP) to generate the conceptual model from unrestricted text requirements. But few researchers have generated a class diagram or conceptual model based on user stories.

In (Mich, 1996), and (Mich & Garigliano, 2002), the researchers propose semi-automatic approaches by developing a tool called LOTIFA which allows extracting objects from user needs without distinguishing between classes, and attributes. However, in (Mich & Garigliano, 2002), the computer scientist intervenes to refine the results obtained.

Key Terms in this Chapter

Stanford Core NLP: Is a natural language software that provides a set of human language technology tools, including the part-of-speech (POS) tagger, the named entity recognizer (NER), the parser, the coreference resolution system.

POS Tagger: Specifies parts of speech to each word such as noun, verb, adjective,

Coreference Resolution: Is a task of recognizing the token in a sentence that refers to the same underlying real-world entities.

Lemma: Lemma in NLP applications allows recognizing different tokens as instances of the same word; like the infinitive form of a verb, the singular plural of the most noun.

NER: Is text analysis that allows classifying the named entities under various predefined classes.

Typed Dependencies: Designate grammatical relationships between the words in a sentence.

Tokenization: Tokenization is the process of splitting a sentence into a list of words.

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