Optimizing Transformer-Based Vulnerability Detection and PatchGeneration Using Multi-Swarm AI-PSO

Introduction: The growing complexity of software systems has led to an increase in security vulnerabilities, highlighting the need for effective automated methods for vulnerability detection and patch generation Methods: This study proposes a novel framework combining deep learning and evolutionary optimization for software vulnerability mitigation. The framework distinctly leverages two advanced transformer models: T5 is employed for code generation, specifically fine-tuned on a curated dataset of vulnerable code snippets and their corresponding patched versions to generate secure code. In parallel, CodeBERT is utilized for vulnerability detection, serving as a classifier to identify and validate potential weaknesses in source code prior to and after transformation. To optimize the performance of the generative T5 model, the study implements a multi-swarm artificial intelligence-based particle swarm optimization (AI-PSO) technique. This evolutionary approach focuses on tuning critical hyperparameters such as the learning rate and training epochs using differential evolution strategies. Results: The integrated framework significantly enhanced the T5 model’s ability to generate accurate and effective patches. Empirical results showed an average improvement of 84.32% in patch generation accuracy compared to standard fine-tuning methods. Furthermore, the AI-PSO approach outperformed traditional hyperparameter optimization techniques, including grid search and random search, by achieving optimal configurations with fewer iterations. Discussion: The improved accuracy and efficiency can be attributed to the synergistic effect of deep learning and intelligent optimization. The adaptive search capability of AI-PSO allows the model to generalize better across different types of vulnerabilities, making the framework robust and scalable Conclusion: This study demonstrates the effectiveness of combining transformer-based code generation with evolutionary hyperparameter tuning for automated software vulnerability mitigation. The proposed hybrid approach offers a promising solution for secure software development, providing both scalability and improved performance over existing methods.