一句话：提出 LiSSA：用 RAG 增强 LLM 实现通用可追溯性链接恢复，在代码相关任务上显著优于 SOTA。
软件开发与维护中，需求、文档、代码、架构模型等制品之间存在复杂关联，自动化可追溯性链接恢复（TLR）对多项任务至关重要。现有方法多针对需求-代码等特定制品对，难以通用。本文提出 LiSSA 框架，利用大语言模型（LLM）并通过检索增强生成（RAG）为模型提供执行 TLR 所需的特定信息。作者在需求-代码、文档-代码、架构文档-架构模型三个 TLR 任务上进行了实证评估，并与当前最优方法比较。结果表明，基于 RAG 的方法在代码相关任务上显著优于现有方法，但要在实践中广泛应用仍需进一步研究。
There are a multitude of software artifacts which need to be handled during the development and maintenance of a software system. These artifacts interrelate in multiple, complex ways. Therefore, many software engineering tasks are enabled - and even empowered - by a clear understanding of artifact interrelationships and also by the continued advancement of techniques for automated artifact linking. However, current approaches in automatic Traceability Link Recovery (TLR) target mostly the links between specific sets of artifacts, such as those between requirements and code. Fortu-nately, recent advancements in Large Language Models (LLMs) can enable TLR approaches to achieve broad applicability. Still, it is a nontrivial problem how to provide the LLMs with the specific information needed to perform TLR. In this paper, we present LiSSA, a framework that har-nesses LLM performance and enhances them through Retrieval-Augmented Generation (RAG). We empirically evaluate LiSSA on three different TLR tasks, requirements to code, documentation to code, and architecture documentation to architecture models, and we compare our approach to state-of-the-art approaches. Our results show that the RAG-based approach can signifi-cantly outperform the state-of-the-art on the code-related tasks. However, further research is required to improve the performance of RAG-based approaches to be applicable in practice.
DOI 原文 ·
@inproceedings{paperbot8890,
title = {LiSSA: Toward Generic Traceability Link Recovery Through Retrieval-Augmented Generation},
author = {Dominik Fuchß and Tobias Hey and Jan Keim and Haoyu Liu and Niklas Ewald and Tobias Thirolf and Anne Koziolek},
booktitle = {ICSE},
year = {2025},
doi = {10.1109/icse55347.2025.00186}
}