In many of the current software applications, numerous vulnerabilities may be present.1 Attackers attempt to exploit existing vulnerabilities that lead to security breaches, unauthorized entry,2 data theft, or incapacitation of a computer system. Rather than addressing software or hardware3 vulnerabilities at a later stage, it is better to address them immediately. DevSecOps, when utilized4 in application development, tackles these vulnerabilities at an early stage. AIBughunter is a tool5 that addresses this problem in software and was developed by the ASWM research group to predict,6 classify, and repair software vulnerabilities. AIBughunter integrates LineVul to find vulnerable7 code lines and returns information about the type of vulnerability and its severity to developers.8 It also includes a tool, VulRepair, which detects and repairs vulnerabilities. VulRepair currently9 predicts patches for vulnerable functions at 44%. In order to become truly effective, this number 10 needs to be increased. This study examines VulRepair to see whether the 44% Perfect Prediction 11 can be increased. VulRepair is a T5 based model that uses Natural Language and Programming 12 Languages for pre-training along with Byte Pair Encoding. It outperforms other existing models, 13 such as VRepair and CodeBERT. However, the hyperparameters may not be optimized due to the 14 development of new optimizers. We review a Deep Neural Network (DNN) optimizer developed by 15 Google in 2023. This optimizer called Evolved Sign Momentum (LION) is available in PyTorch. We 16 applied this optimizer to VulRepair and tested its influence on hyperparameters. After adjusting the 17 hyperparameters, we obtained a 56% Perfect Prediction, which exceeds the value of the VulRepair 18 report of 44%. This means that VulRepair can repair more vulnerabilities and avoid more attacks. As 19 far as we know, our approach of utilizing an alternative to AdamW, the standard optimizer, has not 20 been previously applied to enhance VulRepair and similar models. 21

The main function of dUTPases is to regulate the cellular levels of dUTP and dTTP, thereby playing a crucial role in the DNA repair mechanisms. Despite the fact that mutant organisms with the obliterated dUTPase enzymatic activity remain viable, it is not possible to knockout the dut gene completely due to the lethal consequences of such mutation for the organism. As a result, it is considered that this class of enzymes perform an additional function that is essential for the organism's survival. In this study, we provide the evidence that the dUTPase of bacteriophage T5 possesses supplemental function in addition to its canonical role. We determined the crystal structure of bacteriophage T5 dUTPase with a resolution of 2.0 Å, and discovered a distinct short loop, consisting of 6 amino acid residues which represents unique structural feature specific to the T5-like phages dUTPases. The removal of this element did not affect the overall structure of the homotrimer, but had significant effects on the development of the phage. Furthermore, it has been shown that the enzymatic function and the novel function of bacteriophage T5 dUTPase are unrelated and independent from each other.