Large Language Models for Requirements Engineering: A Cross-Task Empirical Evaluation

2026-08-21

AI Summary

研究通过两个实证研究评估了大型语言模型在需求工程中的应用，覆盖了从需求分类到可追溯性链接识别等五项活动，旨在解决需求信息提取的难题。

Abstract

Requirements-related information is scattered across heterogeneous artefacts such as user feedback, developer discussions, and software repositories, making the extraction of actionable requirements knowledge labour-intensive and hard to scale. Large Language Models (LLMs) can support many Requirements Engineering (RE) activities, from classification and traceability identification to specification and explanation generation, but existing evidence is fragmented across tasks, artefact types, and evaluation settings, and studies rarely offer cross-task evaluations or replication packages. We present two complementary empirical studies evaluating LLMs across five RE-related activities. The first is a controlled experiment on five lightweight open-source LLMs for feedback-driven requirements classification and specification generation. The second is an exploratory industrial case study on two frontier LLMs for traceability link identification and traceability explanation generation using real project artefacts. Classification and traceability identification were assessed with quantitative metrics, and generation tasks through human evaluation. LLM performance is strongly task-dependent, ranging from moderate to high, and no single model consistently outperformed the others, indicating that effective adoption depends on selecting models and prompting strategies per task. Our contributions are: (i) the first cross-task empirical evaluation of LLMs spanning five RE-related activities, (ii) replication materials supporting reproducibility, and (iii) a broader understanding of the capabilities, limitations, and practical readiness of current LLMs for RE.

Problem

• Requirements Engineering
• Large Language Models
• Cross-Task Evaluation
• Heterogeneous Artefacts
• Actionable Requirements Knowledge

Innovation

• Cross-Task Evaluation
• Requirements Engineering (RE)
• Large Language Models (LLMs)
• Replication Materials
• Empirical Studies