In recent years, Large Language Models (LLMs), such as GPT-4 and Claude-3.5, have shown impressive performance in various downstream applications, including software engineering. In this talk, I will discuss the potential impact of modern LLMs on the important problem of software quality assurance, along with our recent research findings. I will first talk about the new opportunities and possibilities LLMs can offer for better quality assurance of real-world software systems. Next, I will discuss the new quality assurance issues or challenges raised by LLMs themselves and deep learning in general. Lastly, I will conclude with how our software engineering community can help advance and co-evolve with code-specific or even general-purpose LLMs.