Hybrid SVM-Bidirectional Long Short-Term Memory Model for Fine-Grained Software Requirement Classification
Mahmuda Akter Metu, Nazneen Akhter, Sanjeda Nasrin, Tasnim Anzum, Afrina Khatun, Rashed Mazumder · Journal of Advances in Information Technology · 2024
This study focuses on advancing the classification of software requirements, particularly within the subclasses of Non-Functional requirements.Four machine learning algorithms-Support Vector Machine (SVM), Logistic Regression (LR), K-Nearest Neighbors (KNN), and Naive Bayes (NB)-were initially implemented, with SVM exhibiting superior performance.To enhance overall accuracy, a voting classifier ensemble method was employed, resulting in significant improvement.In the realm of deep learning, a standalone Bidirectional Long Short-Term Memory (Bi-LSTM) network faced challenges in fine-tuning.To harness the strengths of both machine learning and deep learning, we proposed a hybrid model by integrating SVM with Bi-LSTM.This hybrid model surpassed all prior experiments, highlighting the synergistic potential between traditional machine learning and deep learning.Our findings showcase the effectiveness of combining SVM's discriminative power with Bi-LSTM's sequential understanding, yielding a robust classification model for software requirements.This research contributes to the advancement of requirement analysis, providing a practical solution for accurately identifying diverse requirement types within the nuanced domain of Non-Functional requirements.