Add ICSE Paper w/o preprint yet

Author: dfuchss

_pages/conferences.md

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+  - title: ICSE 2025
+    permalink: /c/icse25
   - title: ICSA 2025
     permalink: /c/icsa25
   - title: SE 2024

_pages/conferences/icse25.md

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+---
+layout: page
+permalink: /c/icse25
+title: "LiSSA: Toward Generic Traceability Link Recovery through Retrieval-Augmented Generation"
+description:
+publication:
+---
+
+by Dominik Fuchß <a href="https://orcid.org/0000-0001-6410-6769"><i class="fa-brands fa-orcid"></i></a>, Tobias Hey <a href="https://orcid.org/0000-0003-0381-1020"><i class="fa-brands fa-orcid"></i></a>, Jan Keim <a href="https://orcid.org/0000-0002-8899-7081"><i class="fa-brands fa-orcid"></i></a>, Haoyu Liu <a href="https://orcid.org/0009-0002-7676-5010"><i class="fa-brands fa-orcid"></i></a>, Niklas Ewald <a href="https://orcid.org/0009-0000-8868-0562"><i class="fa-brands fa-orcid"></i></a>, Tobias Thirolf <a href="https://orcid.org/0009-0006-7052-4020"><i class="fa-brands fa-orcid"></i></a>, and Anne Koziolek <a href="https://orcid.org/0000-0002-1593-3394"><i class="fa-brands fa-orcid"></i></a>
+
+To be published at the [47th IEEE/ACM International Conference on Software Engineering (ICSE 2025), April 27 - May 03 2025](https://conf.researchr.org/home/icse-2025/).
+
+![Approach Overview](/assets/img/icse25-approach.svg){:width="100%" style="background-color: white; border-radius: 8px; padding: 10px; display: block; margin: 0 auto;"}
+
+## Abstract
+
+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.
+Fortunately, 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 harnesses 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 significantly 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.
+
+## Links
+
+- Paper on KITopen: soon
+- Replication Package on Zenodo (soon) and the corresponding GitHub repository (soon).