Applications and Implications of Large Language Models in Qualitative Analysis: A New Frontier for Empirical Software Engineering

2024-12-09

arXiv.org

AI Summary

This study addresses critical challenges in applying large language models (LLMs) to qualitative software engineering research—including result instability, semantic distortion, insufficient detail capture, and privacy-ethics risks—through a systematic empirical investigation. We conducted a systematic mapping study and qualitative meta-synthesis of 21 empirical studies, complemented by hands-on LLM-assisted coding, thematic generation, and data categorization, yielding the first practice-oriented LLM-based qualitative analysis taxonomy for software engineering. Our principal contributions are: (1) an integrated framework balancing analytical efficacy—demonstrating significant gains in coding efficiency and accessibility for novice researchers—and ethical governance—ensuring privacy preservation, result interpretability, and bias mitigation; (2) a clear delineation of LLM limitations, particularly regarding thematic saturation and cross-document consistency; and (3) a methodological guideline for trustworthy, LLM-augmented qualitative research in software engineering.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Knowledge Representation and Reasoning: Qualitative Reasoning
• Natural Language Processing: (Large) Language Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

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.

Problem

• Large Language Models
• Software Engineering Research
• Data Analysis Ethics

Innovation

• Large Language Models
• software engineering research
• guidelines and methodologies