Turkish Journal of Computer and Mathematics Education
Journal license

Journal

Turkish Journal of Computer and Mathematics Education


Volume
& Issue

Volume 12, Issue 3


Published
on

April 5, 2021


Pages

2224-2229


DOI

Article

Non-Functional Requirement Detection Using Machine Learning and Natural Language Processing


Authors

Hazlina Shariff Affiliation:
Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, 43400, Serdang, Selangor, Malaysia
and Mar Yah Said* Affiliation:
Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, 43400, Serdang, Selangor, Malaysia


Abstract

A key aspect of software quality is when the software has been operated functionally and meets user needs. A primary concern with non-functional requirements is that they are always being neglected because their information is hidden in the documents. NFR is a tacit knowledge about the system and as a human, a user usually hardly knows how to describe NFR. Hence, affect the NFR to be absent during the elicitation process. The software engineer has to act proactively to demand the software quality criteria from the user so the objective of requirements can be achieved. In order to overcome these problems, we use machine learning to detect the indicator term of NFR in textual requirements so we can remind the software engineer to elicit the missing NFR. We developed a prototype tool to support our approach to classify the textual requirements and using supervised machine learning algorithms. Survey was done to evaluate the effectiveness of the prototype tool in detecting the NFR.


Keywords

non-functional requirement, software requirement, machine learning, natural language processing


Citation

Shariff, H. & Said, M. Y. (2021). Non-functional requirement detection using machine learning and natural language processing. Turkish Journal of Computer and Mathematics Education, 12(3), 2224–2229.

Published by: Engineering Journals

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