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A Systematic Review of CodeBERT and Bi-LSTM Models with Grey Wolf Optimization for Semantic Name-Based Code Defect Detection Using Deep Neural Networks


Authors : Radhalakshmi Rajagopalan; Dr. Radha V.

Volume/Issue : Volume 11 - 2026, Issue 8 - August


Google Scholar : https://tinyurl.com/eecwcdnt

DOI : https://doi.org/10.38124/ijisrt/26aug1275

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.


Abstract : This review contributes a comprehensive framework for evaluating and implementing semantic code defect detection systems combining pre-trained code models, recurrent architectures, and nature-inspired optimization algorithms.

Keywords : CodeBERT, Bi-LSTM, Grey Wolf Optimization, Code Defect Detection, Semantic Analysis, Deep Learning, Software Engineering, Naming Conventions

References :

  1. Ahmad, W. U., Chakraborty, S., Ray, B., & Chang, K. W. (2020). A transformer-based approach for source code summarization. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 4998-5007).
  2. Benson, M., & Kariv, D. (2020). The economic impact of software bugs. IEEE Software, 37(4), 13-15.
  3. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 4171-4186).
  4. Feng, Z., Guo, D., Tang, D., Duan, N., Feng, X., Gong, M., Shou, L., Qin, B., Liu, T., Jiang, D., & Zhou, M. (2020). CodeBERT: A pre-trained model for programming and natural languages. In Findings of the Association for Computational Linguistics: EMNLP 2020 (pp. 1536-1547).
  5. Graves, A., & Schmidhuber, J. (2005). Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Networks, 18(5-6), 602-610.
  6. Li, Z., Zou, D., Xu, S., Ou, X., Jin, H., Wang, S., Deng, Z., & Zhong, Y. (2019). VulDeePecker: A deep learning-based system for vulnerability detection. IEEE Transactions on Dependable and Secure Computing, 18(2), 594-612.
  7. Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in Engineering Software, 69, 46-61.
  8. Pradel, M., & Sen, K. (2018). DeepBugs: A learning approach to name-based bug detection. Proceedings of the ACM on Programming Languages, 2(OOPSLA), 1-25.

This review contributes a comprehensive framework for evaluating and implementing semantic code defect detection systems combining pre-trained code models, recurrent architectures, and nature-inspired optimization algorithms.

Keywords : CodeBERT, Bi-LSTM, Grey Wolf Optimization, Code Defect Detection, Semantic Analysis, Deep Learning, Software Engineering, Naming Conventions

Paper Submission Last Date
30 - September - 2026

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