Requirements Engineering (RE) is essential for developing complex and regulated software projects. Given the challenges in transforming stakeholder inputs into consistent software designs, Qualitative Data Analysis (QDA) provides a systematic approach to handling free-form data. However, traditional QDA methods are time-consuming and heavily reliant on manual effort. In this paper, we explore the use of Large Language Models (LLMs), including GPT-4, Mistral, and LLaMA-2, to improve QDA tasks in RE. Our study evaluates LLMs' performance in inductive (zero-shot) and deductive (one-shot, few-shot) annotation tasks, revealing that GPT-4 achieves substantial agreement with human analysts in deductive settings, with Cohen's Kappa scores exceeding 0.7, while zero-shot performance remains limited. Detailed, context-rich prompts significantly improve annotation accuracy and consistency, particularly in deductive scenarios, and GPT-4 demonstrates high reliability across repeated runs. These findings highlight the potential of LLMs to support QDA in RE by reducing manual effort while maintaining annotation quality. The structured labels automatically provide traceability of requirements and can be directly utilized as classes in domain models, facilitating systematic software design.

本研究解决了需求工程中将利益相关者输入转化为一致软件设计的难题，传统的定性数据分析方法冗长且依赖手动劳动。我们提出使用大型语言模型提升定性数据分析的效率，发现GPT-4在演绎注释任务中与人类分析师的一致性显著，而通过提供详细和丰富的上下文提示，可以显著提高注释的准确性和一致性。此研究展示了LLMs在需求工程定性数据分析中的巨大潜力，能够减少人工工作量，同时保持注释质量。

从归纳到演绎：基于大型语言模型的需求工程中的定性数据分析