# Improving VulRepair's Perfect Prediction by Leveraging the LION Optimizer
## 7 Conclusions  
Software vulnerabilities are defects in programming codes that can be exploited, necessitating tools like AIBugHunter to detect and repair such vulnerabilities. Developed by the ASWM research group, AIBugHunter scans C/C++ programs, identifies vulnerabilities by CWE type, estimates severity using CVSS scores, and suggests repairs. The tool integrates with Visual Studio Code and utilizes LineVul and VulRepair technologies for enhanced detection and repair capabilities. LineVul, a transformer-based model, predicts and locates vulnerabilities, whereas VulRepair, based on the T5 architecture, effectively repairs them, achieving a 44% perfect prediction rate. This research utilizes datasets like Big-Vul and CVEfixes to evaluate and improve the tool's performance. This study highlights the importance of pretraining with natural and programming languages, as well as the effectiveness of subword tokenization in improving repair predictions. [PERSON] et al. significantly improved their model, illustrating superiority over competitors such as VRepair and CodeBERT. Their methodology was based on technologies from 2022. For example, they used Python 3.9.7, but version 3.10.12 is now available. They employed an NVIDIA GPU 3090, but the more recent 4090 model is now available, two years later.  
After gaining insight into VulRepair through research, we obtained the GitHub model referenced above and modified its procedure. Our study used Google Colab, Google Drive, and a Google V100 GPU to develop and evaluate ImpVulRepair, rather than using a local machine for training and testing. We carefully fine-tuned and adjusted the hyperparameters surrounding the ImpVulRepair model for training and then integrated contemporary libraries. Unlike VulRepair, which uses the AdamW optimizer, our method adopts newer technologies, including the LION optimizer developed by Google Brain in 2023. This methodology allows us to better detect vulnerable functions and increases the perfect prediction percentage. Our model demonstrates a perfect prediction of 56% compared to VulRepair's 44%. This means that we can provide more patches for vulnerabilities and prevent more cyber attacks.  
All authors contributed equally to this project. Writing--original draft, B.K.; Writing--review and editing, Y.L. and J.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This material is based upon work supported by the National Science Foundation [Grant Number 2334243]. The funding source did not influence the study's preparation, design, data collection, analysis, or the decision to publish.  
**Institutional Review Board Statement:** Not Applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** As referenced, ASWM research or [PERSON] et al. provide their research for replication as accessed on 2 February 2024 at [[https://github.com/awsm-research/VulRepair](https://github.com/awsm-research/VulRepair)]([https://github.com/awsm-research/VulRepair](https://github.com/awsm-research/VulRepair)).  
**Conflicts of Interest:** The authors declare no conflicts of interest.