Multilingual Source Code Vulnerability Detection Using Deep Learning: A Semantic Representation and Transfer Learning Approach
TL;DR
This work presents a deep learning approach for multilingual vulnerability detection that emphasizes semantic transfer rather than architectural complexity and suggests that stabilizing semantic representations during transfer is key to improving generalization while maintaining practical efficiency under moderate computational constraints.
Abstract
Detecting vulnerabilities in source code remains a major challenge as modern software systems increasingly span multiple programming languages. This work presents a deep learning approach for multilingual vulnerability detection that emphasizes semantic transfer rather than architectural complexity. The proposed method combines contextualized embeddings from CodeBERT/GraphCodeBERT with a BiLSTM and attention mechanism to capture code semantics and adopts a cross-lingual transfer setting where models trained on one language (e.g., Python) are evaluated on another (e.g., Java). To improve robustness under data imbalance, SMOTE and stratified cross-validation are incorporated into the training process. Experiments on Juliet and CodeXGLUE show that the model achieves an F1-score of about 0.87 and a ROC-AUC of 0.85 in intra-language settings, while guided fine-tuning improves cross-language F1-score by an average of 0.18 and ROC-AUC by approximately 0.13 compared with the direct transfer baseline. These results suggest that stabilizing semantic representations during transfer is key to improving generalization while maintaining practical efficiency under moderate computational constraints.