TY - M-10123
AU - Waziri, Sadiq
TI - Classification of Extracted Features from Software Requirements Specification Documents using Support Vector Machine Learning Technique
T2 - Scientific Research Journal of Science, Engineering and Technology

PY - 2024
VL - 2
IS - 2
SN - 2584-0584

AB - This paper presents the
results of an experiment on the classification of extracted features from
Software Requirements Specification (SRS) documents using various Machine
Learning techniques. The primary focus was on the linear Support Vector Machine
(SVM) technique, with comparative analysis involving three additional
techniques, namely Decision Tree (DT), Naïve Bayes (NB), and K-Nearest
Neighbors (KNN). During the experimentation, features, which are fundamental
building blocks of Software Product Lines [1], were classified into
optional and mandatory. This differentiation facilitates both variability and
similarity within a product family [1]. While previous research has
explored similar classifications using diverse techniques, this study
specifically identifies the most effective method for binary classification of
features for feature modeling. The experiment was conducted on nine selected
documents from the PURE dataset. The performance of each model was evaluated rigorously
based on accuracy, precision, recall (sensitivity), and F1-score. The findings
provide valuable insights into the optimal classification technique, enhancing
the development and management of software product lines.

KW - Requirements Feature
KW -  Feature Extraction
KW -  Feature Classification
KW -  Feature Modeling
KW -  Support Vector Machine
DO -