Extract Entity And Attributes From User Requirements by Applying On Natural Language Processing (NLP) Model.
Keywords:
User requirement, Natural Language Processing (NLP), Python, Spacy, Displacy, SRS, Parts of Speech (POS)Abstract
Automatic generation of entity and attributes from end user requirement reduces cost, time and complexity of software development process. The main aim of this research work is to transform user requirement to Natural Language Processing (NLP) for reducing the ambiguity, inconsistency and incompleteness, and finally extract entity and attributes. This study discusses some initial experiments which are encouraging further research to help in improving the software development process. It focuses on planning of software development, software requirement analysis and further it leads to design of software. During software development, it undergoes and seems to face different types of risks. As in planning phase, different user requirements need to be collected and next systems requirements are to be considered. As end user requirements are in the form of various shapes and sizes, proposed module NLP aims to automatically convert information stored in natural language to machine understandable form. The module mainly focuses on extraction of knowledge from unstructured data to structured format. Module tries to understand user requirements using NLP and lists out entity and attributes. Module is capable of creating SRS document and helps in knowing functional and non-functional requirements. A SRS document is nothing but template representing the structure of document which includes chapters, sections and practical guidelines. This study proposes a methodology that utilizes regular language handling to remove substances and properties. The approach begins by using NLP techniques to translate user requirement to words with its Part of Speech (POS). Thus, this study proposes a methodology that utilizes regular language handling to remove substances and properties.
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