A proactive approach to software security using DCodeBERT for vulnerability management

Indurthi Ravindra Indurthi (VNR Vignana Jyothi Institute of Engineering and Technology)
Shaik Abdul Hameed (VNR Vignana Jyothi Institute of Engineering and Technology)
Polasi Sushma (Vignana Bharathi Institute of Technology)
Jose Pitchaiya (Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology)
Veeramreddy Surya Narayana Reddy (VNR Vignana Jyothi Institute of Engineering and Technology)
Maganti Syamala (Koneru Lakshmaiah Education Foundation)

Bulletin of Electrical Engineering and Informatics
Vol 15, No 1: February 2026

The complexity of modern software has increased security risks, emphasizing the need for automated detection and correction. DCodeBERT, a CodeBERT-based vulnerability detection and remediation framework, is introduced in this study. DCodeBERT uses a multi-task learning framework with shared-private layers, gradient normalization, and uncertainty weighting to stand out. This architecture lets the model capture general representations while preserving task-specific details. From open-source repositories and vetted vulnerability databases, 85,000 code snippets—vulnerable, clean, and repaired—were collected. C, C++, Java, and Python programming languages (PLs) make this dataset highly usable. DCodeBERT surpasses CodeGPT, VulDeePecker, CodeT5 Small, GraphCodeBERT, and Devign in accuracy, precision, recall, and F1-score. Statistics show that the improvements are significant, and qualitative inspection shows that the resulting patches fix buffer overflows and injection problems within semantic validity. This novel approach combines multi-task optimization with natural and PL semantic integration for high cross-language performance. The findings show that DCodeBERT improves vulnerability management in software development settings.