LiSSA: Toward Generic Traceability Link Recovery Through Retrieval-Augmented Generation

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...

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Vydáno v:Proceedings / International Conference on Software Engineering s. 1396 - 1408
Hlavní autoři: FuchB, Dominik, Hey, Tobias, Keim, Jan, Liu, Haoyu, Ewald, Niklas, Thirolf, Tobias, Koziolek, Anne
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 26.04.2025
Témata:
Codes
Computer architecture
Documentation
Large language models
Maintenance
Replicability
Retrieval augmented generation
Software engineering
Software systems
Source coding
traceability link recovery
ISSN:1558-1225
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Shrnutí: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.
ISSN:1558-1225
DOI:10.1109/ICSE55347.2025.00186