The field of software development is undergoing a significant transformation with the integration of Artificial Intelligence (AI) and Large Language Models (LLMs). Recent studies have demonstrated the potential of AI-assisted tools in improving code review, automated unit test generation, and bug detection. Notable trends include the development of LLM-based approaches for automated program repair, the use of multi-agent systems and finite state machines to improve the accuracy and effectiveness of AI-assisted software development tools, and the application of AI and LLMs in software development to improve code quality, reduce debugging time, and enhance overall productivity. Noteworthy papers in this area include studies on AI-assisted code review, LLM-based unit test generation, and bug detection using multi-agent systems. For instance, AI-Assisted Fixes to Code Review Comments at Scale presents a system for providing AI-assisted fixes for code review comments. Input Reduction Enhanced LLM-based Program Repair proposes an approach to reduce test inputs for LLM-based program repair. BugScope introduces a multi-agent system for bug detection that emulates human auditors. The integration of AI and LLMs in software development has also led to advancements in code analysis, generation, and maintenance. Researchers are exploring innovative approaches to improve the accuracy and efficiency of code generation, commit message generation, and bug localization. Notable papers in this area include HistoryFinder, which introduces a new method for accurate and efficient method history generation, and RepoScope, which proposes a call chain-aware multi-view context for repository-level code generation. Roseau, a novel static analysis tool for API breaking change analysis, also demonstrates high accuracy and performance in detecting breaking changes in software libraries. Furthermore, the field of software development is witnessing significant advancements in code debugging and optimization. Researchers are exploring innovative approaches to improve the efficiency and effectiveness of debugging techniques, such as voice-assisted debugging and multimodal error feedback. Novel approaches are being proposed to overcome the limitations of existing methods, such as static and dynamic approaches, and to improve the precision and recall of type checking. The application of AI and LLMs in software development is not limited to these areas. The field of automotive software development is experiencing a significant shift with the integration of Generative Artificial Intelligence (GenAI). This technology has the potential to revolutionize the industry by reducing the amount of human intervention needed and effort for handling complex underlying processes. Notable papers in this area include studies on using conversational AI to support think-aloud practice in technical interview preparation, using GenAI to automate meetings in automotive engineering, and GenAI for automotive software development. The integration of AI and LLMs in software development is also transforming the field of software engineering and game development. AI-powered tools are being explored for their potential to democratize game modding, making it more accessible to a wider range of users. Furthermore, there is a growing interest in understanding how AI teammates can collaborate with human developers in software engineering, with a focus on autonomous coding agents and their impact on the development process. In addition, the field of software development is witnessing a significant shift towards understanding the interplay between socio-technical factors and code quality. Recent studies have highlighted the importance of considering community-level dysfunctions and their impact on maintainability decay in open-source ecosystems. Noteworthy papers in this area include Socio-Technical Smell Dynamics in Code Samples, which investigates the co-occurrence and longitudinal interplay of code smells and community smells in code samples, and VERIRAG: Healthcare Claim Verification via Statistical Audit in Retrieval-Augmented Generation, which introduces a framework for evaluating the methodological rigor of evidence in clinical decision support systems. The integration of AI and LLMs in software development is a rapidly evolving field, with new developments and advancements emerging continuously. As the field continues to grow and mature, it is likely that we will see even more innovative applications of AI and LLMs in software development, leading to improved code quality, reduced debugging time, and enhanced overall productivity.