1. Sahatiya, P., & Shah, H. (2025).” Transformer-Based Framework for Enhancing Software Defect Prediction: Integration with LSTM and Hybrid Learning”, Journal of Information Systems Engineering and Management, Vol. 10. No. 38s (2025), DOI: https://doi.org/10.52783/jisem.v10i38s.6837
2. Gottumukkala, D. P., PVGD, P. R., & Rao, S. K. (2025). Topic modeling-based prediction of software defects and root cause using BERTopic, and multioutput classifier. Scientific Reports, 15(1), 25428.
3. Monteiro, M. L., Cabral, G. G., & OLiveira, A. L. (2025). CodeFlowLM: Incremental Just-In-Time Defect Prediction with Pretrained Language Models and Exploratory Insights into Defect Localization. arXiv preprint arXiv:2512.00231.
4. Mohammad, F., & Ryu, D. (2025, February). Multimodal Learning for Just-In-Time Software Defect Prediction in Autonomous Driving Systems. In 2025 IEEE International Conference on Big Data and Smart Computing (BigComp) (pp. 88-89). IEEE.
5. Alnagi, E., & Azzeh, M. (2024, August). Just-in-time software defect prediction techniques: A survey. In 2024 15th International Conference on Information and Communication Systems (ICICS) (pp. 1-6). IEEE.
6. Liu, J., Ai, J., Lu, M., Wang, J., & Shi, H. (2023). Semantic feature learning for software defect prediction from source code and external knowledge. Journal of Systems and Software, 204, 111753.
7. Jiang, Y., Shen, B., & Gu, X. (2025). Just-in-time software defect prediction via bi-modal change representation learning. Journal of Systems and Software, 219, 112253.
8. Mohammad, F., & Ryu, D. (2025, February). Multimodal Learning for Just-In-Time Software Defect Prediction in Autonomous Driving Systems. In 2025 IEEE International Conference on Big Data and Smart Computing (BigComp) (pp. 88-89). IEEE.
9. Zhu, H. N., Furth, R. M., Pradel, M., & Rubio-González, C. (2025). From Bugs to Benchmarks: A Comprehensive Survey of Software Defect Datasets. arXiv preprint arXiv:2504.17977.
10. Abdu, A., Abdo, H. A., Ullah, I., Khan, J., Gu, Y. H., & Algabri, R. (2025). Deep multi-metrics learning for mobile app defect prediction using code and process metrics. Scientific Reports, 15(1), 38620.
11. Prabha, C. L., & Shivakumar, N. (2020, June). Software defect prediction using machine learning techniques. In 2020 4th International conference on trends in electronics and informatics (ICOEI) (48184) (pp. 728-733). IEEE.
12. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.
13. Tsai, Y. H. H., Bai, S., Liang, P. P., Kolter, J. Z., Morency, L. P., & Salakhutdinov, R. (2019, July). Multimodal transformer for unaligned multimodal language sequences. In Proceedings of the conference. Association for computational linguistics. Meeting (Vol. 2019, p. 6558).
14. Nassif, A. B., Talib, M. A., Azzeh, M., Alzaabi, S., Khanfar, R., Kharsa, R., & Angelis, L. (2023). Software defect prediction using learning to rank approach. Scientific Reports, 13(1), 18885.
15. Giorgio, L., Nicola, M., Fabio, S., & Andrea, S. (2021, December). Continuous defect prediction in CI/CD pipelines: A machine learning-based framework. In International Conference of the Italian Association for Artificial Intelligence (pp. 591-606). Cham: Springer International Publishing.
16. Dao, P. Q., Roantree, M., & Ngo, V. M. (2025). An Enhanced Dual Transformer Contrastive Network for Multimodal Sentiment Analysis. arXiv preprint arXiv:2510.23617.
17. Prabha, C. L., & Shivakumar, N. (2020, June). Software defect prediction using machine learning techniques. In 2020 4th International conference on trends in electronics and informatics (ICOEI) (48184) (pp. 728-733). IEEE.
