期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2019
卷号：10
期号：11
页码：258-263
出版社：Science and Information Society (SAI)
摘要：Intelligent software engineering has emerged in
recent years to address some difficult problems in requirements
engineering. Requirements are crucial for software development.
Moreover, the classification of natural language user
requirements into functional and non-functional requirements is
a fundamental challenge as it defines the fulfillment criteria of
the users’ expected needs and wants. Therefore the research of
this article aims to explore and compare random forest algorithm
and gradient boosting algorithm to determine the accuracy of
functional requirements and non-functional requirements in the
process of requirements classification through the conduct of
experiments. Random forest and gradient boosting are ensemble
algorithms in machine learning that combines the decisions from
several base models to improve the prediction performance.
Experimental results show that the gradient boosting algorithm
yields improved prediction performance when classifying nonfunctional
requirements, in comparison to the random forest
algorithm. However, the random forest algorithm is more
accurate to classify functional requirements.
