The use of large language models (LLMs) for qualitative analysis is gaining attention in various fields, including software engineering, where qualitative methods are essential for understanding human and social factors. This study aimed to investigate how LLMs are currently used in qualitative analysis and their potential applications in software engineering research, focusing on the benefits, limitations, and practices associated with their use. A systematic mapping study was conducted, analyzing 21 relevant studies to explore reported uses of LLMs for qualitative analysis. The findings indicate that LLMs are primarily used for tasks such as coding, thematic analysis, and data categorization, offering benefits like increased efficiency and support for new researchers. However, limitations such as output variability, challenges in capturing nuanced perspectives, and ethical concerns related to privacy and transparency were also identified. The study emphasizes the need for structured strategies and guidelines to optimize LLM use in qualitative research within software engineering, enhancing their effectiveness while addressing ethical considerations. While LLMs show promise in supporting qualitative analysis, human expertise remains crucial for interpreting data, and ongoing exploration of best practices will be vital for their successful integration into empirical software engineering research.

翻译：大语言模型在定性分析中的应用正受到包括软件工程在内的多个领域的关注，其中定性方法对于理解人因与社会因素至关重要。本研究旨在调查大语言模型目前在定性分析中的应用现状及其在软件工程研究中的潜在应用，重点关注其使用的优势、局限与实践。通过开展系统性图谱研究，分析21项相关文献以探究大语言模型在定性分析中的实际应用。研究发现，大语言模型主要用于编码、主题分析和数据分类等任务，其优势包括提升分析效率与支持新晋研究人员。然而，研究也识别出若干局限性，如输出结果的不稳定性、捕捉细微视角的挑战，以及与隐私和透明度相关的伦理问题。本研究强调需要制定结构化策略与指导方针，以优化大语言模型在软件工程定性研究中的应用，在提升其效能的同时妥善处理伦理考量。尽管大语言模型在支持定性分析方面展现出潜力，但数据解读仍需依赖人类专业知识，持续探索最佳实践对其成功融入实证软件工程研究至关重要。