Add ICSA25 to ardoco website

Author: dfuchss

_bibliography/papers.bib

@@ -85,3 +85,19 @@ @incollection{keim_detecting_2024
   bibtex_show = {true},
   html        = {/c/se24}
 }
+
+@inproceedings{fuchss_enabling_2025,
+  author          = {Fuchß, Dominik and Liu, Haoyu and Hey, Tobias and Keim, Jan and Koziolek, Anne},
+  year            = {2025},
+  title           = {Enabling Architecture Traceability by LLM-based Architecture Component Name Extraction},
+  eventtitle      = {22nd IEEE International Conference on Software Architecture},
+  eventtitleaddon = {ICSA 2025},
+  eventdate       = {2025-03-31/2025-04-04},
+  venue           = {Odense, Denmark},
+  booktitle       = {2025 IEEE 22nd International Conference on Software Architecture (ICSA)},
+  publisher       = {{Institute of Electrical and Electronics Engineers (IEEE)}},
+  keywords        = {Traceability Link Recovery, Large Language Models, Software Architecture, Model Extraction},
+  language        = {english},
+  bibtex_show     = {true},
+  html            = {/c/icsa25}
+}

_pages/conferences.md

@@ -5,6 +5,8 @@ nav: true
 nav_order: 1
 dropdown: true
 children:
+  - title: ICSA 2025
+    permalink: /c/icsa25
   - title: SE 2024
     permalink: /c/se24
   - title: "GI-Fachgruppe Architekturen 2024"

_pages/conferences/icsa25.md

@@ -0,0 +1,32 @@
+---
+layout: page
+permalink: /c/icsa25
+title: Enabling Architecture Traceability by LLM-based Architecture Component Name Extraction
+description:
+publication: fuchss_enabling_2025
+---
+
+by Dominik Fuchß <a href="https://orcid.org/0000-0001-6410-6769"><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>, 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>, 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 [22nd IEEE International Conference on Software Architecture (ICSA 2025), March 31 - April 04 2025](https://conf.researchr.org/home/icsa-2025/).
+
+![Approach Overview](/assets/img/icsa25-approach.svg){:width="100%"}
+
+## Abstract
+Traceability Link Recovery (TLR) is an enabler for various software engineering tasks.
+One important task is the recovery of trace links between Software Architecture Documentation (SAD) and source code.
+Here, the main challenge is the semantic gap between the two artifact types.
+Recent research has shown that this semantic gap can be bridged by using Software Architecture Models (SAMs) as intermediates.
+However, the creation of SAMs is a manual and time-consuming task.
+This paper investigates the use of Large Language Models (LLMs) to extract component names as simple SAMs for TLR based on SAD and source code.
+By doing so, we aim to bridge the semantic gap between SAD and source code without the need for manual SAM creation.
+We compare our approach to the state-of-the-art TLR approaches TransArC and ArDoCode.
+TransArC is the currently best-performing approach for TLR between SAD and source code, but it requires SAMs as an additional artifact.
+Our evaluation shows that our approach performs comparable to TransArC (weighted average F1 with GPT-4o: 0.86 vs. TransArC's 0.87), while only needing the SAD and source code.
+Moreover, our approach significantly outperforms the best baseline that does not need SAMs (weighted average F1 with GPT-4o: 0.86 vs. ArDoCode's 0.62).
+In summary, our approach shows that LLMs can be used to make TLR between SAD and source code more applicable by extracting component names and omitting the need for manually created SAMs.
+
+## Links
+
+- Paper on [KITopen](https://publikationen.bibliothek.kit.edu/1000177521)
+- Replication Package on [Zenodo](https://doi.org/10.5281/zenodo.14506935) and the corresponding [GitHub repository](https://github.com/ArDoCo/ReplicationPackage-EnablingArchitectureTraceabilitybyLLM-basedArchitectureComponentNameExtraction)