Natural Language Processing and Requirement Engineering

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Natural Language Processing and Requirement Engineering

With the growing development of various applications in the world of information technology, there is intense competition between various companies for software development. One of the features that can make a company win over its competitors is the quality of its applications.

But when the software quality is main concern, the speed and cost of software development will decrease and increase respectively. Therefore, companies must look for solutions to increase speed of software development and reduce production costs. One of the technologies that can play an important role in this field is Natural language processing (NLP). In software engineering, one of the applications of NLP can be to process requirement documents. For example, we can use text processing to detect non-functional requirements in textual requirements. Non-functional requirements includes security, performance, availability, extensibility and portability, which recognition of them in the early stages of software development can play an important role in increasing software quality. Also, classification and prioritization of requirements are the major challenges during software development that by using the advantages of text processing, the time and cost for analysis the collected documents can be reduced. Liping Zhao and et al. categorized the most important issues related to NLP and RE as follows:

“- For the analysis phase, the main problem is the detection of language issues in requirements documents.

– For the management phase, the main problem is the identification of traceability relationships between requirements.

– For the elicitation phase, the main problem is the extraction of requirements concepts.

– For the modeling phase, the main problem is the extraction of requirements concepts and the composition of conceptual models. “.

On the other hand, various NLP techniques have developed for analysis requirements documents such as word embedding techniques especially Google’s vector representation of words (Word2Vec).The most important issue in using NLP techniques in requirements engineering is the collaboration between requirements engineering experts and NLP specialists and. Requirements engineering experts can provide the requirements documents and making them available to NLP specialists.

Links to other subjects:

• Combining natural language and AI for automation of SE

• Leveraging natural language processing in requirements Engineering

• Technical debt identification using NLP

• NLP based software architecture knowledge extraction 

• Software faults detection improvement using NLP

• NLP based models for software artifacts’ quality evaluation

• NLP based computer assisted coding

• NLP assisted Software Debugging and Testing

• Application of NLP to Program Analysis

• Application of NLP to Software Documentation & Summarization

• Application of NLP in Software Maintenance

• Application of NLP to Software Optimization

• Application of NLP in Program comprehension

• Advancement in technologies of NLP for SE

• NLP-based Tools for software engineering

• NLP and Software Traceability

• NLP in Quality Assessment and Improvement

• NLP and Software Reverse Engineering

• Mining Natural Language Data from Software Artifacts

• Current Issues in Software Engineering for Natural Language Processing

• Current Issues in Software Engineering for Natural Language Processing


[1]       A. Casamayor, D. Godoy, and M. Campo, “Identification of non-functional requirements in textual specifications: A semi-supervised learning approach,” Inf. Softw. Technol., vol. 52, no. 4, pp. 436–445, 2010.

[2]       L. Zhao et al., “Natural language processing (NLP) for requirements engineering: A systematic mapping study,” arXiv Prepr. arXiv2004.01099, 2020.


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