Enhanced Automated Code Vulnerability Repair using Large Language Models
David de-Fitero-Dominguez Eva Garcia-Lopez Antonio Garcia-Cabot Jose-Javier Martinez-Herraiz
This research addresses the complex challenge of automated repair of code
vulnerabilities, vital for enhancing digital security in an increasingly
technology-driven world. The study introduces a novel and efficient format for
the representation of code modification, using advanced Large Language Models
(LLMs) such as Code Llama and Mistral. These models, fine-tuned on datasets
featuring C code vulnerabilities, significantly improve the accuracy and
adaptability of automated code repair techniques. A key finding is the enhanced
repair accuracy of these models when compared to previous methods such as
VulRepair, which underscores their practical utility and efficiency. The
research also offers a critical assessment of current evaluation metrics, such
as perfect predictions, and their limitations in reflecting the true
capabilities of automated repair models in real-world scenarios. Following
this, it underscores the importance of using test datasets devoid of train
samples, emphasizing the need for dataset integrity to enhance the
effectiveness of LLMs in code repair tasks. The significance of this work is
its contribution to digital security, setting new standards for automated code
vulnerability repair and paving the way for future advancements in the fields
of cybersecurity and artificial intelligence. The study does not only highlight
the potential of LLMs in enhancing code security but also fosters further
exploration and research in these crucial areas.