@Article{M-10138,
AUTHOR = {Waziri, Sadiq},
TITLE = {A Review on Classification of Extracted Features from Software Requirements Specification Documents using Support Vector Machine Learning Technique},
JOURNAL = {Scientific Research Journal of Science, Engineering and Technology},
VOLUME = {2},
YEAR = {2024},
NUMBER = {2},
ARTICLE-NUMBER = {M-10138},
URL = {https://isrdo.org/journal/SRJSET/currentissue/a-review-on-classification-of-extracted-features-from-software-requirements-specification-documents-using-support-vector-machine-learning-technique},
ISSN = {2584-0584},
ABSTRACT = {Manual classification
of extracted features from large datasets can be tedious and time-consuming.
This paper reviews the methods for classifying extracted features from SRS
documents using Machine Learning (ML), with focus on linear Support Vector
Machine (SVM) technique. We also explore other classification techniques, such
as decision trees (DT), naïve Bayes (NB), and k-nearest neighbors (KNN)—for classifying
the extracted features into mandatory and optional. Previous studies have
compared different classification techniques for feature modeling. The primary goal
of this review is to identify the best method for binary classification of
features for software product lines engineering (SPLE). The proposed system
will be tested on nine SRS documents that were chosen from the Public
Requirements dataset with accuracy, precision, recall, and F1 scores used for
evaluation.},
DOI = {}
}