Patch Generation

Patch generation focuses on automatically creating code or data modifications to address various problems, ranging from fixing software bugs to mitigating adversarial attacks on computer vision systems. Current research heavily utilizes large language models (LLMs) and generative adversarial networks (GANs) to generate these patches, often incorporating techniques like retrieval-augmentation and conversational approaches to improve accuracy and efficiency. This field is significant for improving software reliability, enhancing the robustness of machine learning models, and automating complex tasks in software engineering and computer vision.