The field of software engineering and security is rapidly evolving, with a growing focus on leveraging large language models (LLMs) and other artificial intelligence (AI) techniques to improve software development, testing, and security. Recent research has highlighted the potential of LLMs to enhance code evaluation metrics, vulnerability discovery, and security testing, among other areas. Notably, the integration of LLMs with traditional software engineering techniques has shown promise in improving the accuracy and efficiency of various software development tasks. Furthermore, the development of new frameworks and tools, such as those for automated attack tree-based security test generation and semantic-aware fuzzing, has demonstrated significant advancements in automating security testing methodologies. Overall, the field is moving towards a more integrated and AI-driven approach to software engineering and security, with a focus on improving the reliability, efficiency, and security of software systems. Noteworthy papers include: LoCaL, which proposes a new benchmark for evaluating code evaluation metrics and highlights the need for more robust metrics that can mitigate surface bias. Synergizing Static Analysis with Large Language Models for Vulnerability Discovery and beyond, which demonstrates the potential of combining static analysis with LLMs to improve vulnerability discovery. STAF, which introduces a novel approach to automating security test case generation using LLMs and a four-step self-corrective Retrieval-Augmented Generation framework.