Open AccessNon-Functional Requirement Detection Using Machine Learning and Natural Language Processing
Author(s) -
Hazlina Shariff et.al
Publication year - 2021
Publication title -
türk bilgisayar ve matematik eğitimi dergisi
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.218
H-Index - 3
ISSN - 1309-4653
DOI - 10.17762/turcomat.v12i3.1171
Subject(s) - computer science , software , functional requirement , quality (philosophy) , process (computing) , software engineering , machine learning , artificial intelligence , software requirements , key (lock) , tacit knowledge , user requirements document , natural language , software development , software construction , knowledge management , programming language , computer security , philosophy , epistemology
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 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 know 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 wasdone toevaluate theeffectiveness of the prototype tool in detecting the NFR.