生成式人工智能对需求工程未来的影响

中文摘要 AI 辅助

人工智能（AI）的最新进展，特别是大语言模型（LLM），正通过增加对领域知识的获取以及为软件工程（SE）提供自动化支持，改变我们设计和构建系统的方式。随着通用型SE智能体的出现使实现成本降低，工程工作重心从编写正确代码转向表达、策展、验证和评估需求。本文首先调研了在此转型之前AI用于需求工程（RE）研究的现状，然后回顾了LLM的进展。我们调研了两个后续研究领域：提示编程（将LLM指令视为SE术语中的程序）和通用型SE智能体（结合多项LLM进展以产生完成SE任务的半自主流程）。最后，我们沿着两个维度探索需求工程的未来：改变我们通过SE流程与需求交互方式的事项，以及更广泛地改变软件开发者和利益相关者（包括最终用户）体验需求方式的事项。本文旨在告知RE研究者如何在这一转型中导航，以选择未来的研究重点。

英文摘要

Recent advances in artificial intelligence (AI), particularly large language models (LLMs), are transforming how we design and build systems by increasing access to domain knowledge and by providing automation support to software engineering (SE). As implementation becomes less expensive through generalist SE agents, engineering effort shifts away from writing correct code and toward expressing, curating, verifying, and evaluating requirements. In this paper, we survey the state of the art in AI for requirements engineering (RE) research leading up to the transformation, before reviewing advances in LLMs. We survey two subsequent research areas: prompt programming, which treats LLM instructions as a program in SE vernacular, and generalist SE agents, which combine multiple LLM advances to yield semi-autonomous processes that complete SE tasks. Finally, we explore the future of requirements engineering along two axes: matters changing how we interact with requirements through the SE process, and matters changing how requirements are experienced by software developers and stakeholders more broadly, including end-users. This article aims to inform how RE researchers can navigate this transformation in the selection of future research priorities.

发表机构

• Carnegie Mellon University（卡内基梅隆大学）

机构由 AI 辅助整理，请以论文原文为准。