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

• URL: http://arxiv.org/abs/2412.06564v3
• Date: Mon, 03 Feb 2025 03:59:00 GMT
• Title: Applications and Implications of Large Language Models in Qualitative Analysis: A New Frontier for Empirical Software Engineering
• Authors: Matheus de Morais Leça, Lucas Valença, Reydne Santos, Ronnie de Souza Santos,
• Abstract summary: The study emphasizes the need for structured strategies and guidelines to optimize LLM use in qualitative research within software engineering. 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.
• Score: 0.46426852157920906
• License:
• 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.

Related papers

• From Selection to Generation: A Survey of LLM-based Active Learning [153.8110509961261]
  Large Language Models (LLMs) have been employed for generating entirely new data instances and providing more cost-effective annotations. This survey aims to serve as an up-to-date resource for researchers and practitioners seeking to gain an intuitive understanding of LLM-based AL techniques.
  arXiv  Detail & Related papers  (2025-02-17T12:58:17Z)
• Large Language Model for Qualitative Research -- A Systematic Mapping Study [3.302912592091359]
  Large Language Models (LLMs), powered by advanced generative AI, have emerged as transformative tools. This study systematically maps the literature on the use of LLMs for qualitative research. Findings reveal that LLMs are utilized across diverse fields, demonstrating the potential to automate processes.
  arXiv  Detail & Related papers  (2024-11-18T21:28:00Z)
• EVOLvE: Evaluating and Optimizing LLMs For Exploration [76.66831821738927]
  Large language models (LLMs) remain under-studied in scenarios requiring optimal decision-making under uncertainty. We measure LLMs' (in)ability to make optimal decisions in bandits, a state-less reinforcement learning setting relevant to many applications. Motivated by the existence of optimal exploration algorithms, we propose efficient ways to integrate this algorithmic knowledge into LLMs.
  arXiv  Detail & Related papers  (2024-10-08T17:54:03Z)
• Reconciling Methodological Paradigms: Employing Large Language Models as Novice Qualitative Research Assistants in Talent Management Research [1.0949553365997655]
  This study proposes a novel approach by leveraging Retrieval Augmented Generation (RAG) based Large Language Models (LLMs) for analyzing interview transcripts. The novelty of this work lies in strategizing the research inquiry as one that is augmented by an LLM that serves as a novice research assistant. Our findings demonstrate that the LLM-augmented RAG approach can successfully extract topics of interest, with significant coverage compared to manually generated topics.
  arXiv  Detail & Related papers  (2024-08-20T17:49:51Z)
• A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges [60.546677053091685]
  Large language models (LLMs) have unlocked novel opportunities for machine learning applications in the financial domain. We explore the application of LLMs on various financial tasks, focusing on their potential to transform traditional practices and drive innovation. We highlight this survey for categorizing the existing literature into key application areas, including linguistic tasks, sentiment analysis, financial time series, financial reasoning, agent-based modeling, and other applications.
  arXiv  Detail & Related papers  (2024-06-15T16:11:35Z)
• Apprentices to Research Assistants: Advancing Research with Large Language Models [0.0]
  Large Language Models (LLMs) have emerged as powerful tools in various research domains. This article examines their potential through a literature review and firsthand experimentation.
  arXiv  Detail & Related papers  (2024-04-09T15:53:06Z)
• LLM Inference Unveiled: Survey and Roofline Model Insights [62.92811060490876]
  Large Language Model (LLM) inference is rapidly evolving, presenting a unique blend of opportunities and challenges. Our survey stands out from traditional literature reviews by not only summarizing the current state of research but also by introducing a framework based on roofline model. This framework identifies the bottlenecks when deploying LLMs on hardware devices and provides a clear understanding of practical problems.
  arXiv  Detail & Related papers  (2024-02-26T07:33:05Z)
• Can Large Language Models Serve as Data Analysts? A Multi-Agent Assisted Approach for Qualitative Data Analysis [6.592797748561459]
  Large Language Models (LLMs) have enabled collaborative human-bot interactions in Software Engineering (SE) We introduce a new dimension of scalability and accuracy in qualitative research, potentially transforming data interpretation methodologies in SE.
  arXiv  Detail & Related papers  (2024-02-02T13:10:46Z)
• A Case Study on Test Case Construction with Large Language Models: Unveiling Practical Insights and Challenges [2.7029792239733914]
  This paper examines the application of Large Language Models in the construction of test cases within the context of software engineering. Through a blend of qualitative and quantitative analyses, this study assesses the impact of LLMs on test case comprehensiveness, accuracy, and efficiency.
  arXiv  Detail & Related papers  (2023-12-19T20:59:02Z)
• The Efficiency Spectrum of Large Language Models: An Algorithmic Survey [54.19942426544731]
  The rapid growth of Large Language Models (LLMs) has been a driving force in transforming various domains. This paper examines the multi-faceted dimensions of efficiency essential for the end-to-end algorithmic development of LLMs.
  arXiv  Detail & Related papers  (2023-12-01T16:00:25Z)
• Information Extraction in Low-Resource Scenarios: Survey and Perspective [56.5556523013924]
  Information Extraction seeks to derive structured information from unstructured texts. This paper presents a review of neural approaches to low-resource IE from emphtraditional and emphLLM-based perspectives.
  arXiv  Detail & Related papers  (2022-02-16T13:44:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.