The field of software engineering is witnessing a significant shift towards leveraging natural language processing (NLP) techniques to improve the quality and efficiency of software development. With the rise of large language models (LLMs), researchers are exploring innovative ways to address the challenges posed by ambiguous natural language requirements. A key direction in this area is the development of automated repair techniques for ambiguous requirements, which can significantly improve the performance of LLMs in code generation tasks. Another important trend is the application of NLP in requirements engineering for specialized systems, such as Retrieval Augmented Generation (RAG) systems, where data scientists must identify context-specific retrieval requirements through iterative experimentation with users. The use of generative AI is also being explored in agile software development, where it can help evaluate the quality of epics and improve the overall development process. Furthermore, researchers are working on designing accessible and user-friendly interfaces for small business owners to leverage generative AI in business planning. Noteworthy papers in this area include: Automated Repair of Ambiguous Natural Language Requirements, which introduces a novel approach to decomposing the problem of automated repair into simpler subproblems, resulting in targeted and minimal requirement repairs. Towards Requirements Engineering for RAG Systems, which presents an empirical process model for eliciting retrieval requirements in complex domain-specific applications. A Case Study Investigating the Role of Generative AI in Quality Evaluations of Epics in Agile Software Development, which explores the opportunities and challenges of using LLMs to evaluate agile epic quality. BizChat: Scaffolding AI-Powered Business Planning for Small Business Owners Across Digital Skill Levels, which introduces a web application that helps business owners write their business plan using a large language model.