The field of software engineering is experiencing a significant shift towards automation, with a focus on improving efficiency, reliability, and transparency. Researchers are exploring innovative approaches, such as combining neural learning with symbolic reasoning, to overcome the limitations of current methods. The integration of artificial intelligence, particularly large language models, is also being investigated to automate tasks like code generation, software integration, and system validation. Furthermore, the potential of quantum artificial intelligence is being tapped to solve complex software engineering problems. Notable papers in this area include: A Path Less Traveled: Reimagining Software Engineering Automation via a Neurosymbolic Paradigm, which proposes a hybrid methodology for AI-driven software engineering automation. Automating Automotive Software Development: A Synergy of Generative AI and Formal Methods, which combines generative AI with model-driven engineering to automate automotive software development. Capability-Driven Skill Generation with LLMs: A RAG-Based Approach for Reusing Existing Libraries and Interfaces, which presents a method for generating executable code based on natural language user input. Quantum Artificial Intelligence for Software Engineering: the Road Ahead, which highlights open research opportunities and challenges in applying quantum AI to software engineering.