# Using ChatGPT in Software Requirements Engineering: A Comprehensive Review
## 1 Introduction  
Software requirements engineering is a critical phase in the software development lifecycle that lays the foundation for successfully delivering software products that meet stakeholders' needs and expectations. Traditionally, this process involves eliciting, analyzing, specifying, and validating requirements, often time-consuming, labor-intensive, and prone to human error and bias. Herein lies the potential for large language models (LLMs) to increase and streamline these processes by providing efficient and effective solutions for requirement elicitation, documentation, and validation [1]. An IEEE consolidated definition for a software requirement is a documented condition or capability that a system or system component meets or possesses to solve a contractual, standard, specification problem, or objective [2]. In recent years, advances in natural language processing (NLP) have paved the way for transformative applications in diverse domains, such as healthcare, finance, e-commerce, social media, and more. Among these breakthroughs, LLMs have emerged as powerful tools with the potential to revolutionize software engineering practices, particularly in software requirements engineering. LLMs like ChatGPT have unprecedented capabilities for understanding, generating, and processing natural language text. Their vast pre-trained knowledge and ability togenerate contextually relevant text make them promising candidates to assist and automate various tasks in software development [3]. However, integrating LLMs into software requirements engineering presents challenges and concerns. While LLMs exhibit impressive capabilities in generating coherent and contextually relevant text, they may also produce inaccuracies, ambiguities, or biased outputs, posing risks to the reliability and quality of software requirements. Moreover, ethical considerations such as data privacy, model bias, and transparency justify careful examination when employing LLMs in sensitive domains like software development [4]. A critical aspect of using LLMs effectively is formulating appropriate prompts--the input text or queries provided to these models to generate desired outputs [5]. Prompt patterns, defined as structured and systematic approaches to creating prompts for LLMs, have emerged as critical tools for realizing the full potential of these models. Researchers and practitioners can guide LLMs to produce outputs that align more closely with their intended objectives by defining specific patterns or templates for prompts. These patterns facilitate the generation of coherent and contextually relevant text and enable finer control over the outputs generated by LLMs [6; 7]. This study aims to evaluate the impact of ChatGPT on the software requirements elicitation process. To achieve this goal, we investigated the benefits and challenges of this Generative AI (GenAI) based on studies conducted by other authors and provided a critical analysis of these studies. The main contributions of this work are the following:  
* Understanding the role of GenAI in software requirements;
* Characterizing the benefits and challenges of using ChatGPT to assist with software requirements;
* Identifying future research directions. Considering the transformative potential of large language models (LLMs) like ChatGPT in software requirements engineering, this research seeks to answer the following research question:  
RQ: How can ChatGPT be used to improve software requirements engineering processes, and what are the associated benefits, challenges, and ethical considerations? To address this question, we conducted a literature review evaluating the application of ChatGPT in software requirements engineering. Through this analysis, we elucidate the role of ChatGPT in streamlining requirement elicitation, documentation, and validation while characterizing the benefits and delineating the challenges of integrating ChatGPT in this domain.