Add REFSQ25 Paper

Author: dfuchss

_pages/conferences.md

@@ -7,6 +7,8 @@ dropdown: true
 children:
   - title: ICSE 2025
     permalink: /c/icse25
+  - title: REFSQ 2025
+    permalink: /c/refsq25
   - title: ICSA 2025
     permalink: /c/icsa25
   - title: SE 2024

_pages/conferences/refsq25.md

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+---
+layout: page
+permalink: /c/refsq25
+title: "Requirements Traceability Link Recovery via Retrieval-Augmented Generation"
+description:
+publication: # hey_requirements_2025
+---
+
+by Tobias Hey <a href="https://orcid.org/0000-0003-0381-1020"><i class="fa-brands fa-orcid"></i></a>, Dominik Fuchß <a href="https://orcid.org/0000-0001-6410-6769"><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>, 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 [31st International Working Conference on Requirements Engineering: Foundation for Software Quality](https://2025.refsq.org/).
+
+![Approach Overview](/assets/img/refsq25-approach.svg){:width="100%" style="background-color: white; border-radius: 8px; padding: 10px; display: block; margin: 0 auto;"}
+
+## Abstract
+
+**[Context and Motivation]**
+In software development, various interrelated artifacts are created.
+Access to information on the relation between these artifacts eases understanding of the system and enables tasks such as change impact and software reusability analyses.
+Manual trace link creation is labor-intensive and costly, and thus is often missing in projects.
+Automation could enhance the development and maintenance efficiency.
+
+**[Question/Problem]**
+Current methods for automatically recovering traceability links between different types of requirements do not achieve the necessary performance to be applied in practice, or require pre-existing links for machine learning.
+
+**[Principal Ideas and Results]**
+We propose to address this limitation by \method{leveraging large language models (LLMs) with retrieval-augmented generation (RAG) for inter-requirements traceability link recovery.}
+In an empirical evaluation on six benchmark datasets, we show that chain-of-thought prompting can be beneficial, open-source models perform comparably to proprietary ones, and that the approach can outperform state-of-the-art and baseline approaches.
+
+**[Contribution]** This work presents an approach for inter-requirements traceability link recovery using RAG and provides the first empirical evidence of its performance.
+
+## Links
+
+- Paper on [KITopen](https://publikationen.bibliothek.kit.edu/1000178589)
+- Replication Package on [Zenodo](https://doi.org/10.5281/zenodo.14779457) and the corresponding [GitHub repository](https://github.com/ArDoCo/ReplicationPackage-REFSQ25_Requirements-TLR-via-RAG)