# From Inductive to Deductive: LLMs-Based Qualitative Data Analysis in Requirements Engineering

arXiv: https://arxiv.org/abs/2504.19384 · HTML: https://mlsanity.com/paper/2504.19384/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 31 references that are in the map.
- First posted on arXiv 27 Apr 2025.

## Builds on (most cited first, 15 of 15)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting](https://mlsanity.com/paper/2305.04388/) (2023)
- [Benchmarking Large Language Models for News Summarization](https://mlsanity.com/paper/2301.13848/) (2023)
- [Prompting Large Language Model for Machine Translation: A Case Study](https://mlsanity.com/paper/2301.07069/) (2023)
- [Large Language Models for Software Engineering: Survey and Open Problems](https://mlsanity.com/paper/2310.03533/) (2023)
- [Text Classification via Large Language Models](https://mlsanity.com/paper/2305.08377/) (2023)
- [LLM-Assisted Content Analysis: Using Large Language Models to Support Deductive Coding](https://mlsanity.com/paper/2306.14924/) (2023)
- [Using LLMs in Software Requirements Specifications: An Empirical Evaluation](https://mlsanity.com/paper/2404.17842/) (2024)
- [Zero-Shot Learning for Requirements Classification: An Exploratory Study](https://mlsanity.com/paper/2302.04723/) (2023)
- [Using Large Language Models for Natural Language Processing Tasks in Requirements Engineering: A Systematic Guideline](https://mlsanity.com/paper/2402.13823/) (2024)
- [An experiment exploring the theoretical and methodological challenges in developing a semi-automated approach to analysis of small-N qualitative data](https://mlsanity.com/paper/2002.04513/) (2020)
- [Qualitative Data Analysis in Software Engineering: Techniques and Teaching Insights](https://mlsanity.com/paper/2406.08228/) (2024)

## Abstract (arXiv)

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.