Add configuration-based links system for conference pages

_conferences/aire25.md

@@ -11,6 +11,19 @@ authors:
   - tobias_hey
 approaches:
   - LiSSA
+links:
+  - name: Paper (KITopen)
+    url: https://publikationen.bibliothek.kit.edu/1000183058
+  - name: Paper (IEEE Xplore)
+    url: https://ieeexplore.ieee.org/document/11190238
+  - name: Replication Package (Zenodo)
+    url: https://doi.org/10.5281/zenodo.15837231
+  - name: Replication Package (GitHub)
+    url: https://github.com/ardoco/Replication-Package-AIRE25_Beyond-Retrieval-Using-LLM-Ensembles-for-Candidate-Filtering-in-Req-TLR
+  - name: Slides (PPTX)
+    url: /assets/pdf/presentation_aire25.pptx
+  - name: Slides (PDF)
+    url: /assets/pdf/presentation_aire25.pdf
 ---
 
 Published at the [33rd International Requirements Engineering Conference Workshops (REW)](https://aire-ws.github.io/aire25/).
@@ -37,9 +50,3 @@ While our LLM-based ensemble approach achieves comparable F2-scores to IR method
 **[Conclusion]**
 This work provides insights into the capabilities of small LLMs as a filter in inter-requirements TLR.
 Moreover, it provides insights into the performance of traditional IR techniques for TLR and their dependency on hyperparameters.
-
-## Links
-
-- Paper on [KITopen](https://publikationen.bibliothek.kit.edu/1000183058) and [IEEE Xplore](https://ieeexplore.ieee.org/document/11190238)
-- Replication Package on [Zenodo](https://doi.org/10.5281/zenodo.15837231) and the corresponding [GitHub repository](https://github.com/ardoco/Replication-Package-AIRE25_Beyond-Retrieval-Using-LLM-Ensembles-for-Candidate-Filtering-in-Req-TLR)
-- Slides as [pptx](/assets/pdf/presentation_aire25.pptx) or [pdf](/assets/pdf/presentation_aire25.pdf)

_conferences/ecsa21.md

@@ -13,6 +13,15 @@ authors:
   - anne_koziolek
 approaches:
   - SWATTR
+links:
+  - name: Paper (Springer Link)
+    url: https://doi.org/10.1007/978-3-030-86044-8_7
+  - name: Paper (KITopen)
+    url: https://doi.org/10.5445/IR/1000138399
+  - name: Replication Package (Zenodo)
+    url: https://doi.org/10.5281/zenodo.4730621
+  - name: Replication Package (GitHub)
+    url: https://github.com/ardoco/SWATTR
 ---
 
 Published at the [15th European Conference on Software Architecture (ECSA 2021), September 13-17 2021](https://conf.researchr.org/home/ecsa-2021)
@@ -32,9 +41,3 @@ In each stage, multiple agents can be used to capture necessary information to a
 We evaluate the performance of our approach with three case studies and compare our results to baseline approaches.
 The results for our approach are good to excellent with a weighted average F1-Score of 0.72 over all case studies.
 Moreover, our approach outperforms the baseline approaches on non-weighted average by at least 0.24 (weighted 0.31).
-
-## Links
-
-- Paper on [Springer Link](https://doi.org/10.1007/978-3-030-86044-8_7) and on [KITopen](https://doi.org/10.5445/IR/1000138399)
-- Replication Package on [Zenodo](https://doi.org/10.5281/zenodo.4730621) and the corresponding [GitHub repository](https://github.com/ardoco/SWATTR)
-<!-- - [Slides](/assets/pdf/presentation_21_ecsa_TLR.pdf) -->

_conferences/icsa23.md

@@ -12,6 +12,19 @@ authors:
 approaches:
   - SWATTR
   - "Inconsistency Detection"
+links:
+  - name: Paper (IEEE Xplore)
+    url: https://doi.org/10.1109/ICSA56044.2023.00021
+  - name: Paper (KITopen)
+    url: https://doi.org/10.5445/IR/1000158208
+  - name: Replication Package (Zenodo)
+    url: https://doi.org/10.5281/zenodo.7555194
+  - name: Replication Package (GitHub)
+    url: https://github.com/ardoco/DetectingInconsistenciesInSoftwareArchitectureDocumentationUsingTraceabilityLinkRecovery
+  - name: Slides ICSA23 (PDF)
+    url: /assets/pdf/presentation_23_ICSA_InconsistencyDetection.pdf
+  - name: Slides SE24 (PDF)
+    url: /assets/pdf/presentation_24_SE_InconsistencyDetection.pdf
 ---
 
 Published at the [20th IEEE International Conference on Software Architecture (ICSA 2023), March 13-17 2023](https://icsa-conferences.org/2023/).
@@ -23,10 +36,3 @@ Additional presentation at the [Software Engineering 2024 (SE24)](https://se2024
 ## Abstract
 
 Documenting software architecture is important for a system’s success. Software architecture documentation (SAD) makes information about the system available and eases comprehensibility. There are different forms of SADs like natural language texts and formal models with different benefits and different purposes. However, there can be inconsistent information in different SADs for the same system. Inconsistent documentation then can cause flaws in development and maintenance. To tackle this, we present an approach for inconsistency detection in natural language SAD and formal architecture models. We make use of traceability link recovery (TLR) and extend an existing approach. We utilize the results from TLR to detect unmentioned (i.e., model elements without natural language documentation) and missing model elements (i.e., described but not modeled elements). In our evaluation, we measure how the adaptations on TLR affected its performance. Moreover, we evaluate the inconsistency detection. We use a benchmark with multiple open source projects and compare the results with existing and baseline approaches. For TLR, we achieve an excellent F1-score of 0.81, significantly outperforming the other approaches by at least 0.24. Our approach also achieves excellent results (accuracy: 0.93) for detecting unmentioned model elements and good results for detecting missing model elements (accuracy: 0.75). These results also significantly outperform competing baselines. Although we see room for improvements, the results show that detecting inconsistencies using TLR is promising.
-
-## Links
-
-- Paper on [IEEE Xplore](https://doi.org/10.1109/ICSA56044.2023.00021) and on [KITopen](https://doi.org/10.5445/IR/1000158208)
-- Replication Package on [Zenodo](https://doi.org/10.5281/zenodo.7555194) and the corresponding [GitHub repository](https://github.com/ardoco/DetectingInconsistenciesInSoftwareArchitectureDocumentationUsingTraceabilityLinkRecovery)
-- [Slides (ICSA23)](/assets/pdf/presentation_23_ICSA_InconsistencyDetection.pdf)
-- [Slides (SE24)](/assets/pdf/presentation_24_SE_InconsistencyDetection.pdf)

_conferences/icsa25.md

@@ -13,6 +13,19 @@ authors:
 approaches:
   - ExArch
   - TransArC
+links:
+  - name: Paper (IEEE Xplore)
+    url: https://doi.org/10.1109/ICSA65012.2025.00011
+  - name: Paper (KITopen)
+    url: https://publikationen.bibliothek.kit.edu/1000179830
+  - name: Replication Package (Zenodo)
+    url: https://doi.org/10.5281/zenodo.14506935
+  - name: Replication Package (GitHub)
+    url: https://github.com/ardoco/ReplicationPackage-EnablingArchitectureTraceabilitybyLLM-basedArchitectureComponentNameExtraction
+  - name: Slides (PPTX)
+    url: /assets/pdf/presentation_icsa25.pptx
+  - name: Slides (PDF)
+    url: /assets/pdf/presentation_icsa25.pdf
 ---
 
 Published at the [22nd IEEE International Conference on Software Architecture (ICSA 2025), March 31 - April 04 2025](https://conf.researchr.org/home/icsa-2025/).
@@ -33,9 +46,3 @@ TransArC is the currently best-performing approach for TLR between SAD and sourc
 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 [IEEE Xplore](https://doi.org/10.1109/ICSA65012.2025.00011) or [KITopen](https://publikationen.bibliothek.kit.edu/1000179830)
-- Replication Package on [Zenodo](https://doi.org/10.5281/zenodo.14506935) and the corresponding [GitHub repository](https://github.com/ardoco/ReplicationPackage-EnablingArchitectureTraceabilitybyLLM-basedArchitectureComponentNameExtraction)
-- Slides as [pptx](/assets/pdf/presentation_icsa25.pptx) or [pdf](/assets/pdf/presentation_icsa25.pdf)

_conferences/icse24.md

@@ -16,6 +16,21 @@ approaches:
   - ArCoTL
   - SWATTR
   - ArDoCode
+links:
+  - name: Paper (ACM Open Access)
+    url: https://doi.org/10.1145/3597503.3639130
+  - name: Paper (KITopen)
+    url: https://doi.org/10.5445/IR/1000165692
+  - name: Replication Package (Zenodo)
+    url: https://doi.org/10.5281/zenodo.10411853
+  - name: Replication Package (GitHub)
+    url: https://github.com/ardoco/Replication-Package-ICSE24_Recovering-Trace-Links-Between-Software-Documentation-And-Code
+  - name: Slides ICSE24 (PPTX)
+    url: /assets/pdf/presentation_icse24.pptx
+  - name: Slides ICSE24 (PDF)
+    url: /assets/pdf/presentation_icse24.pdf
+  - name: Slides SE25 (PDF)
+    url: /assets/pdf/presentation_25_SE_TransArC.pdf
 ---
 
 Published at the [46th International Conference on Software Engineering (ICSE 2024), April 14-20 2024](https://conf.researchr.org/home/icse-2024).
@@ -43,10 +58,3 @@ The model-to-code TLR approach achieves an average F1-score of 0.98, while the d
 _Conclusion_
 Combining two specialized approaches with an intermediate artifact shows promise for bridging the semantic gap.
 In future research, we will explore further possibilities for such transitive approaches.
-
-## Links
-
-- Paper (Open Access) on [ACM](https://doi.org/10.1145/3597503.3639130) or [KITopen](https://doi.org/10.5445/IR/1000165692)
-- Replication Package on [Zenodo](https://doi.org/10.5281/zenodo.10411853) and the corresponding [GitHub repository](https://github.com/ardoco/Replication-Package-ICSE24_Recovering-Trace-Links-Between-Software-Documentation-And-Code)
-- Slides as [pptx](/assets/pdf/presentation_icse24.pptx) or [pdf](/assets/pdf/presentation_icse24.pdf)
-- [Slides (SE25)](/assets/pdf/presentation_25_SE_TransArC.pdf)

_conferences/icse25.md

@@ -14,6 +14,19 @@ authors:
   - anne_koziolek
 approaches:
   - LiSSA
+links:
+  - name: Paper (IEEE Xplore)
+    url: https://doi.org/10.1109/ICSE55347.2025.00186
+  - name: Paper (KITopen)
+    url: https://publikationen.bibliothek.kit.edu/1000179816
+  - name: Replication Package (Zenodo)
+    url: https://doi.org/10.5281/zenodo.14714706
+  - name: Replication Package (GitHub)
+    url: https://github.com/ardoco/ReplicationPackage-ICSE25_LiSSA-Toward-Generic-Traceability-Link-Recovery-through-RAG/tree/main
+  - name: Slides (PPTX)
+    url: /assets/pdf/presentation_icse25.pptx
+  - name: Slides (PDF)
+    url: /assets/pdf/presentation_icse25.pdf
 ---
 
 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/).
@@ -34,9 +47,3 @@ We empirically evaluate LiSSA on three different TLR tasks, requirements to code
 
 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 [IEEE Xplore](https://doi.org/10.1109/ICSE55347.2025.00186) or [KITopen](https://publikationen.bibliothek.kit.edu/1000179816)
-- Replication Package on [Zenodo](https://doi.org/10.5281/zenodo.14714706) and the corresponding [GitHub repository](https://github.com/ardoco/ReplicationPackage-ICSE25_LiSSA-Toward-Generic-Traceability-Link-Recovery-through-RAG/tree/main)
-- Slides as [pptx](/assets/pdf/presentation_icse25.pptx) or [pdf](/assets/pdf/presentation_icse25.pdf)

_conferences/refsq25.md

@@ -11,6 +11,15 @@ authors:
   - anne_koziolek
 approaches:
   - LiSSA
+links:
+  - name: Paper (KITopen)
+    url: https://publikationen.bibliothek.kit.edu/1000179817
+  - name: Paper (Springer Nature)
+    url: https://doi.org/10.1007/978-3-031-88531-0_27
+  - name: Replication Package (Zenodo)
+    url: https://doi.org/10.5281/zenodo.14779457
+  - name: Replication Package (GitHub)
+    url: https://github.com/ardoco/ReplicationPackage-REFSQ25_Requirements-TLR-via-RAG
 ---
 
 Published at the [31st International Working Conference on Requirements Engineering: Foundation for Software Quality](https://2025.refsq.org/).
@@ -33,8 +42,3 @@ We propose to address this limitation by leveraging large language models (LLMs)
 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/1000179817) or [Springer Nature](https://doi.org/10.1007/978-3-031-88531-0_27)
-- 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)

_layouts/publication.liquid

@@ -56,6 +56,49 @@ layout: default
       </div>
     {% endif %}
     {{ content }}
+
+    {% if page.links %}
+      <h2>Links</h2>
+
+      {% comment %} Group links by type (extracted from name before parenthesis) {% endcomment %}
+      {% assign grouped_links = page.links | group_by_exp: 'link', "link.name | split: ' (' | first" %}
+
+      <ul>
+        {% for group in grouped_links %}
+          {% assign group_name = group.name %}
+          {% assign links = group.items %}
+
+          {% if links.size > 1 %}
+            {% comment %} Multiple links of same type {% endcomment %}
+            {% if group_name contains 'Slides' %}
+              <li>
+                {{ group_name }} as
+                {% for link in links -%}
+                  {%- assign link_label = link.name | split: '(' | last | split: ')' | first -%}
+                  <a href="{{ link.url }}">{{ link_label }}</a>
+                  {%- unless forloop.last %} or {% endunless -%}
+                {%- endfor %}
+              </li>
+            {% else %}
+              <li>
+                {{ group_name }} on
+                {% for link in links -%}
+                  {%- assign link_label = link.name | split: '(' | last | split: ')' | first -%}
+                  <a href="{{ link.url }}">{{ link_label }}</a>
+                  {%- unless forloop.last %} and {% endunless -%}
+                {%- endfor %}
+              </li>
+            {% endif %}
+          {% else %}
+            {% comment %} Single link {% endcomment %}
+            {% assign link = links[0] %}
+            <li>
+              <a href="{{ link.url }}">{{ link.name }}</a>
+            </li>
+          {% endif %}
+        {% endfor %}
+      </ul>
+    {% endif %}
   </article>
 
   {% if page.related_publications %}