The field of software development and maintenance is experiencing significant advancements, driven by the increasing adoption of Deep Learning (DL) and Large Language Models (LLMs). Researchers are exploring innovative approaches to manage technical debt, improve code review processes, and automate testing. A key area of focus is the application of LLMs to automate tasks such as fault analysis, test oracle discovery, and systematic review screening. These advancements have the potential to improve software quality, reduce development time, and increase efficiency. Noteworthy papers in this area include: A First Look at the Lifecycle of DL-Specific Self-Admitted Technical Debt, which highlights the need for targeted technical debt management strategies. AutoEmpirical: LLM-Based Automated Research for Empirical Software Fault Analysis, which demonstrates the potential of LLMs to improve efficiency in fault analysis. Automated Discovery of Test Oracles for Database Management Systems Using LLMs, which introduces a novel framework for automated test oracle discovery. AISysRev -- LLM-based Tool for Title-abstract Screening, which develops a tool for systematic review screening using LLMs. Oops!... I did it again. Conclusion (In-)Stability in Quantitative Empirical Software Engineering: A Large-Scale Analysis, which investigates threats to validity in complex tool pipelines for evolutionary software analyses.