2024
doi: 10.12720/jait.15.8.914-922
Hybrid SVM-Bidirectional Long Short-Term Memory Model for Fine-Grained Software Requirement Classification
Mahmuda Akter Metu1
,
Nazneen Akhter2,* ,
Sanjeda Nasrin1 ,
Tasnim Anzum1 ,
Afrina Khatun1 ,
Rashed Mazumder3
- 1Department of Information and Communication Technology, Faculty of Science and Technology, Bangladesh University of Professionals, Mirpur, Dhaka-1216, Bangladesh
- 2Department of Computer Science and Engineering, Faculty of Science and Technology, Bangladesh University of Professionals, Mirpur, Dhaka-1216, Bangladesh
- 3nstitute of Information Technology, Jahangirnagar University, Savar, Dhaka-1342, Bangladesh
- Manuscript receivedDecember 21, 2023
- revisedFebruary 27, 2024
- acceptedMay 20, 2024
- publishedAugust 7, 2024
Abstract
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.
Keywords
- Software requirements
- Bidirectional Long Short-Term Memory (Bi-LSTM)
- Support Vector Machine (SVM)
How to Cite
Mahmuda Akter Metu, Nazneen Akhter, Sanjeda Nasrin, Tasnim Anzum, Afrina Khatun, and Rashed Mazumder, "Hybrid SVM-Bidirectional Long Short-Term Memory Model for Fine-Grained Software Requirement Classification," Journal of Advances in Information Technology, vol. 15, no. 8, pp. 914-922, 2024. https://doi.org/10.12720/jait.15.8.914-922
Copyright & License
Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
