From 2a42023269a8fb1cf9e5983b1995c4c88a096ce9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Dominik=20Fuch=C3=9F?= Date: Tue, 1 Jul 2025 16:29:43 +0200 Subject: [PATCH 1/6] Add first version of approaches --- _approaches/arcotl.md | 16 ++++++++++++++++ _approaches/ardocode.md | 9 +++++++++ _approaches/domina.md | 13 +++++++++++++ _approaches/lissa.md | 13 +++++++++++++ _approaches/secdragon.md | 9 +++++++++ _approaches/swattr.md | 12 ++++++++++++ _approaches/transarc.md | 15 +++++++++++++++ _approaches/transarcai.md | 13 +++++++++++++ _approaches/tv.md | 11 +++++++++++ _config.yml | 2 ++ _pages/approaches.md | 34 ++++++++++++++++++++++++++++++++++ 11 files changed, 147 insertions(+) create mode 100644 _approaches/arcotl.md create mode 100644 _approaches/ardocode.md create mode 100644 _approaches/domina.md create mode 100644 _approaches/lissa.md create mode 100644 _approaches/secdragon.md create mode 100644 _approaches/swattr.md create mode 100644 _approaches/transarc.md create mode 100644 _approaches/transarcai.md create mode 100644 _approaches/tv.md create mode 100644 _pages/approaches.md diff --git a/_approaches/arcotl.md b/_approaches/arcotl.md new file mode 100644 index 00000000..9c181f6f --- /dev/null +++ b/_approaches/arcotl.md @@ -0,0 +1,16 @@ +--- +title: ArCoTL +description: ArCoTL – TLR between Software Architecture Models and Code. +permalink: /approaches/arcotl/ +importance: 2 +layout: page +--- + +

+ ArCoTL Approach Overview +

+ +🚧 This page is work in progress. + +## References +- [ICSE 2024 publication page](/c/icse24) \ No newline at end of file diff --git a/_approaches/ardocode.md b/_approaches/ardocode.md new file mode 100644 index 00000000..d0227d42 --- /dev/null +++ b/_approaches/ardocode.md @@ -0,0 +1,9 @@ +--- +title: ArDoCode +description: ArDoCode – TLR between Software Architecture Documentation and Code. +permalink: /approaches/ardocode/ +importance: 5 +layout: page +--- + +🚧 This page is work in progress. diff --git a/_approaches/domina.md b/_approaches/domina.md new file mode 100644 index 00000000..e9a6da38 --- /dev/null +++ b/_approaches/domina.md @@ -0,0 +1,13 @@ +--- +title: DoMInA +description: DoMInA – Documentation-Model-Inconsistency-Analysis pipeline. +permalink: /approaches/domina/ +importance: 8 +layout: page +--- + +![Approach Overview](/assets/img/approach_overview_icsa23.svg){:width="100%"} + +DoMInA (formerly ArDoCo) is a pipeline for detecting inconsistencies between natural language software architecture documentation and architecture models using TLR. It identifies unmentioned and missing model elements. + +See our [ICSA 2023 publication page](/c/icsa23) for details, links, and resources. diff --git a/_approaches/lissa.md b/_approaches/lissa.md new file mode 100644 index 00000000..e7c32bbe --- /dev/null +++ b/_approaches/lissa.md @@ -0,0 +1,13 @@ +--- +title: LiSSA +description: LiSSA – LLM/RAG-based TLR. +permalink: /approaches/lissa/ +importance: 6 +layout: page +--- + +![LiSSA Overview](/assets/img/icse25-approach.svg){:width="100%" style="background-color: white; border-radius: 8px; padding: 10px; display: block; margin: 0 auto;"} + +LiSSA is a framework that leverages Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) for generic traceability link recovery. It is empirically evaluated on requirements-to-code, documentation-to-code, and documentation-to-model TLR tasks, outperforming state-of-the-art on code-related tasks. + +LiSSA is primarily associated with our [ICSE 2025 publication page](/c/icse25), but is also related to our [REFSQ 2025 publication page](/c/refsq25). See these pages for details, links, and resources. diff --git a/_approaches/secdragon.md b/_approaches/secdragon.md new file mode 100644 index 00000000..09b1b4e0 --- /dev/null +++ b/_approaches/secdragon.md @@ -0,0 +1,9 @@ +--- +title: SecDragon +description: SecDragon – TLR for Security Requirements. +permalink: /approaches/secdragon/ +importance: 7 +layout: page +--- + +🚧 This approach is not available yet. diff --git a/_approaches/swattr.md b/_approaches/swattr.md new file mode 100644 index 00000000..f27bfaaf --- /dev/null +++ b/_approaches/swattr.md @@ -0,0 +1,12 @@ +--- +title: SWATTR +description: SWATTR – TLR between Software Architecture Documentation and Software Architecture Models. +permalink: /approaches/swattr/ +importance: 1 +layout: page +--- + + +SWATTR is an extendable, agent-based framework for creating trace links between textual software architecture documentation and models. It extracts text and model information, identifies elements in text, and connects these elements to model elements. SWATTR outperforms baseline approaches and is evaluated on multiple case studies. + +See our [ECSA 2021 publication page](/c/ecsa21) for details, links, and resources. diff --git a/_approaches/transarc.md b/_approaches/transarc.md new file mode 100644 index 00000000..9a1b4ee2 --- /dev/null +++ b/_approaches/transarc.md @@ -0,0 +1,15 @@ +--- +title: TransArC +description: TransArC – TLR between Software Architecture Documentation, Models, and Code. +permalink: /approaches/transarc/ +importance: 3 +layout: page +--- + +

+ Approach Overview +

+ +TransArC combines ArDoCo (documentation-to-model) and ArCoTL (model-to-code) to create transitive trace links, bridging the semantic gap between documentation and code. It significantly outperforms baselines. + +See our [ICSE 2024 publication page](/c/icse24) for details, links, and resources. diff --git a/_approaches/transarcai.md b/_approaches/transarcai.md new file mode 100644 index 00000000..ed5969cd --- /dev/null +++ b/_approaches/transarcai.md @@ -0,0 +1,13 @@ +--- +title: "TransArC-AI" +description: "TransArC-AI – LLM-based TLR between Software Architecture Documentation, Models, and Code." +permalink: /approaches/transarcai/ +importance: 4 +layout: page +--- + +![TransArC-AI Overview](/assets/img/icsa25-approach.svg){:width="100%" style="background-color: white; border-radius: 8px; padding: 10px; display: block; margin: 0 auto;"} + +TransArC AI is an LLM-based approach for traceability link recovery between software architecture documentation, models, and code. It leverages Large Language Models to extract component names as simple architecture models, bridging the semantic gap between documentation and code without the need for manual model creation. + +See our [ICSA 2025 publication page](/c/icsa25) for details, links, and resources. diff --git a/_approaches/tv.md b/_approaches/tv.md new file mode 100644 index 00000000..b53ad056 --- /dev/null +++ b/_approaches/tv.md @@ -0,0 +1,11 @@ +--- +title: ArDoCo-TV +description: "Trace View: a viewer for trace links." +permalink: /approaches/tv/ +importance: 9 +layout: page +--- + +ArDoCo-TV is a tool for visualizing trace links between software artifacts, supporting the analysis and understanding of traceability in software projects. + +See our [ArDoCo TV](https://ardoco.de/TraceView) for more information. diff --git a/_config.yml b/_config.yml index 91b37306..9a65802f 100644 --- a/_config.yml +++ b/_config.yml @@ -146,6 +146,8 @@ collections: output: true projects: output: true + approaches: + output: true # ----------------------------------------------------------------------------- # Jekyll settings diff --git a/_pages/approaches.md b/_pages/approaches.md new file mode 100644 index 00000000..ffd5953c --- /dev/null +++ b/_pages/approaches.md @@ -0,0 +1,34 @@ +--- +layout: page +title: approaches +permalink: /approaches/ +description: Approaches within ArDoCo +nav: true +nav_order: 0.25 +horizontal: false +--- + + +
+ +{% assign sorted_projects = site.approaches | sort: "importance" %} + + + +{% if page.horizontal %} + +
+
+ {% for project in sorted_projects %} + {% include projects_horizontal.liquid %} + {% endfor %} +
+
+ {% else %} +
+ {% for project in sorted_projects %} + {% include projects.liquid %} + {% endfor %} +
+ {% endif %} +
\ No newline at end of file From 2ceabcfab26e360a2f40b844305523f59fad7b3f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Dominik=20Fuch=C3=9F?= Date: Tue, 1 Jul 2025 16:35:34 +0200 Subject: [PATCH 2/6] Move conferences to own directory --- {_pages/conferences => _conferences}/ecsa21.md | 0 {_pages/conferences => _conferences}/fg-arch24.md | 0 {_pages/conferences => _conferences}/icsa23.md | 0 {_pages/conferences => _conferences}/icsa25.md | 0 {_pages/conferences => _conferences}/icse24.md | 0 {_pages/conferences => _conferences}/icse25.md | 0 {_pages/conferences => _conferences}/refsq25.md | 0 {_pages/conferences => _conferences}/se24.md | 0 {_pages/conferences => _conferences}/se25.md | 0 _config.yml | 2 ++ 10 files changed, 2 insertions(+) rename {_pages/conferences => _conferences}/ecsa21.md (100%) rename {_pages/conferences => _conferences}/fg-arch24.md (100%) rename {_pages/conferences => _conferences}/icsa23.md (100%) rename {_pages/conferences => _conferences}/icsa25.md (100%) rename {_pages/conferences => _conferences}/icse24.md (100%) rename {_pages/conferences => _conferences}/icse25.md (100%) rename {_pages/conferences => _conferences}/refsq25.md (100%) rename {_pages/conferences => _conferences}/se24.md (100%) rename {_pages/conferences => _conferences}/se25.md (100%) diff --git a/_pages/conferences/ecsa21.md b/_conferences/ecsa21.md similarity index 100% rename from _pages/conferences/ecsa21.md rename to _conferences/ecsa21.md diff --git a/_pages/conferences/fg-arch24.md b/_conferences/fg-arch24.md similarity index 100% rename from _pages/conferences/fg-arch24.md rename to _conferences/fg-arch24.md diff --git a/_pages/conferences/icsa23.md b/_conferences/icsa23.md similarity index 100% rename from _pages/conferences/icsa23.md rename to _conferences/icsa23.md diff --git a/_pages/conferences/icsa25.md b/_conferences/icsa25.md similarity index 100% rename from _pages/conferences/icsa25.md rename to _conferences/icsa25.md diff --git a/_pages/conferences/icse24.md b/_conferences/icse24.md similarity index 100% rename from _pages/conferences/icse24.md rename to _conferences/icse24.md diff --git a/_pages/conferences/icse25.md b/_conferences/icse25.md similarity index 100% rename from _pages/conferences/icse25.md rename to _conferences/icse25.md diff --git a/_pages/conferences/refsq25.md b/_conferences/refsq25.md similarity index 100% rename from _pages/conferences/refsq25.md rename to _conferences/refsq25.md diff --git a/_pages/conferences/se24.md b/_conferences/se24.md similarity index 100% rename from _pages/conferences/se24.md rename to _conferences/se24.md diff --git a/_pages/conferences/se25.md b/_conferences/se25.md similarity index 100% rename from _pages/conferences/se25.md rename to _conferences/se25.md diff --git a/_config.yml b/_config.yml index 9a65802f..7d6d719d 100644 --- a/_config.yml +++ b/_config.yml @@ -148,6 +148,8 @@ collections: output: true approaches: output: true + conferences: + output: true # ----------------------------------------------------------------------------- # Jekyll settings From 6d3a92d46c3f4f2b8cd961d26df5a6b8a911fe0b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Dominik=20Fuch=C3=9F?= Date: Tue, 1 Jul 2025 16:38:51 +0200 Subject: [PATCH 3/6] prettier --- _approaches/arcotl.md | 3 ++- _approaches/swattr.md | 1 - _pages/approaches.md | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/_approaches/arcotl.md b/_approaches/arcotl.md index 9c181f6f..419f18b9 100644 --- a/_approaches/arcotl.md +++ b/_approaches/arcotl.md @@ -13,4 +13,5 @@ layout: page 🚧 This page is work in progress. ## References -- [ICSE 2024 publication page](/c/icse24) \ No newline at end of file + +- [ICSE 2024 publication page](/c/icse24) diff --git a/_approaches/swattr.md b/_approaches/swattr.md index f27bfaaf..e1eab92b 100644 --- a/_approaches/swattr.md +++ b/_approaches/swattr.md @@ -6,7 +6,6 @@ importance: 1 layout: page --- - SWATTR is an extendable, agent-based framework for creating trace links between textual software architecture documentation and models. It extracts text and model information, identifies elements in text, and connects these elements to model elements. SWATTR outperforms baseline approaches and is evaluated on multiple case studies. See our [ECSA 2021 publication page](/c/ecsa21) for details, links, and resources. diff --git a/_pages/approaches.md b/_pages/approaches.md index ffd5953c..ce1ed386 100644 --- a/_pages/approaches.md +++ b/_pages/approaches.md @@ -31,4 +31,4 @@ horizontal: false {% endfor %} {% endif %} - \ No newline at end of file + From c3108febd313956be622950c4ac134d214ec80d3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Dominik=20Fuch=C3=9F?= Date: Mon, 14 Jul 2025 13:26:23 +0200 Subject: [PATCH 4/6] use a proper name for id --- _approaches/domina.md | 13 ------------- _approaches/inconsistency-detection.md | 13 +++++++++++++ 2 files changed, 13 insertions(+), 13 deletions(-) delete mode 100644 _approaches/domina.md create mode 100644 _approaches/inconsistency-detection.md diff --git a/_approaches/domina.md b/_approaches/domina.md deleted file mode 100644 index e9a6da38..00000000 --- a/_approaches/domina.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: DoMInA -description: DoMInA – Documentation-Model-Inconsistency-Analysis pipeline. -permalink: /approaches/domina/ -importance: 8 -layout: page ---- - -![Approach Overview](/assets/img/approach_overview_icsa23.svg){:width="100%"} - -DoMInA (formerly ArDoCo) is a pipeline for detecting inconsistencies between natural language software architecture documentation and architecture models using TLR. It identifies unmentioned and missing model elements. - -See our [ICSA 2023 publication page](/c/icsa23) for details, links, and resources. diff --git a/_approaches/inconsistency-detection.md b/_approaches/inconsistency-detection.md new file mode 100644 index 00000000..c7a65086 --- /dev/null +++ b/_approaches/inconsistency-detection.md @@ -0,0 +1,13 @@ +--- +title: Inconsistency Detection +description: Documentation-Model-Inconsistency-Analysis pipeline. +permalink: /approaches/inconsistency-detection/ +importance: 8 +layout: page +--- + +![Approach Overview](/assets/img/approach_overview_icsa23.svg){:width="100%"} + +ArDoCo Inconsistency Detection is a pipeline for detecting inconsistencies between natural language software architecture documentation and architecture models using TLR. It identifies unmentioned and missing model elements. + +See our [ICSA 2023 publication page](/c/icsa23) for details, links, and resources. From c555b556d7cee97e39024130947e14153cda635c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Dominik=20Fuch=C3=9F?= Date: Sat, 19 Jul 2025 16:15:51 +0200 Subject: [PATCH 5/6] more information about the approaches --- _approaches/arcotl.md | 12 ++++++++---- _approaches/ardocode.md | 10 +++++++++- _approaches/inconsistency-detection.md | 12 ++++++++++-- _approaches/lissa.md | 8 +++++++- _approaches/swattr.md | 11 ++++++++++- _approaches/transarc.md | 12 ++++++++---- _approaches/transarcai.md | 10 +++++++++- assets/img/approach_ardocode_icse24.svg | 4 ++++ assets/img/ecsa21-approach.svg | 4 ++++ 9 files changed, 69 insertions(+), 14 deletions(-) create mode 100644 assets/img/approach_ardocode_icse24.svg create mode 100644 assets/img/ecsa21-approach.svg diff --git a/_approaches/arcotl.md b/_approaches/arcotl.md index 419f18b9..d5b98cd6 100644 --- a/_approaches/arcotl.md +++ b/_approaches/arcotl.md @@ -6,11 +6,15 @@ importance: 2 layout: page --- -

- ArCoTL Approach Overview -

+![ArCoTL Overview](/assets/img/approach_overview_icse24.svg){:width="100%" style="background-color: white; border-radius: 8px; padding: 10px; display: block; margin: 0 auto;"} -🚧 This page is work in progress. +ArCoTL (Architecture–Code Trace Links) focuses on linking a given architecture model (SAM) to the source code. +It assumes you have a formal model of the system's components and interfaces, and wants to find the corresponding code. +ArCoTL transforms both the architecture model and the code into intermediate representations (e.g. simplified graphs) and then applies various heuristics to match elements +These heuristics include standalone rules and dependent rules (which consider relationships) plus filters to refine the links. + +* How it works: Starting from a SAM and the codebase, ArCoTL builds simplified model and code representations. It then uses text similarity, naming conventions, and dependency heuristics to propose links between each model component and code artifact. +* Effectiveness: ArCoTL turned out to be very effective on its own. In experiments, the model-to-code step (ArCoTL) achieved an average F1 of ~0.98. ## References diff --git a/_approaches/ardocode.md b/_approaches/ardocode.md index d0227d42..69270721 100644 --- a/_approaches/ardocode.md +++ b/_approaches/ardocode.md @@ -6,4 +6,12 @@ importance: 5 layout: page --- -🚧 This page is work in progress. +![ArCoTL Overview](/assets/img/approach_ardocode_icse24.svg){:width="100%" style="border-radius: 8px; padding: 10px; display: block; margin: 0 auto;"} + +ArDoCode is a simpler variant of trace recovery that treats source code itself as the "model". +Instead of first building a formal model, ArDoCode directly matches architecture document content with code elements using the same heuristics designed for linking docs to models. +In practice, it extracts key terms from the documentation and tries to align them with names in the code (e.g. class or module names) as if the code were the model. +* Key idea: Apply the SWATTR approach without an explicit SAM by interpreting the codebase as a model. For example, if the doc mentions a component "WebUI" and there is a WebUI package in code, ArDoCode will link them. +* Effectiveness: Because it skips the formal modeling step, ArDoCode is easier to apply but less precise. In evaluations, ArDoCode achieved a weighted F1 of only ~0.62, substantially lower than the full TransArC method. It serves mainly as a baseline and demonstrates that without structured models, the TLR performance drops. + +See our [ICSE 2024 publication page](/c/icse24) for details, links, and resources. \ No newline at end of file diff --git a/_approaches/inconsistency-detection.md b/_approaches/inconsistency-detection.md index c7a65086..3ad00a98 100644 --- a/_approaches/inconsistency-detection.md +++ b/_approaches/inconsistency-detection.md @@ -6,8 +6,16 @@ importance: 8 layout: page --- -![Approach Overview](/assets/img/approach_overview_icsa23.svg){:width="100%"} +![Approach Overview](/assets/img/approach_overview_icsa23.svg){:width="100%" style="background-color: white; border-radius: 8px; padding: 10px; display: block; margin: 0 auto;"} -ArDoCo Inconsistency Detection is a pipeline for detecting inconsistencies between natural language software architecture documentation and architecture models using TLR. It identifies unmentioned and missing model elements. +The ArDoCo inconsistency detection approach uses trace link recovery to detect inconsistencies between natural-language architecture documentation and formal models. +It identifies two kinds of issues: + +(a) Unmentioned Model Elements (UMEs): components or interfaces that appear in the model but are never described in the documentation; +(b) Missing Model Elements (MMEs): elements mentioned in the text that do not exist in the model. + +The method runs a TLR procedure (namely SWATTR) and then flags any model element with no corresponding text link (a UME) or any sentence that refers to a non-modeled item (an MME). +* Detection strategy: Use the TLR results as a bridge. After linking as many sentences to model elements as possible, any "orphan" model nodes or text mentions indicate a consistency gap. For example, if the model has a "Cache" component with no sentence linked, that is an UME; if the doc talks about "Common" but the model lacks it, that is an MME. +* Results: The approach achieved an excellent F1 (0.81) for the underlying trace recovery. For inconsistency detection, it attained ~93% accuracy in identifying UMEs and ~75% for MMEs, significantly better than naive baselines. These results suggest that using trace links is a promising way to find documentation-model mismatches. See our [ICSA 2023 publication page](/c/icsa23) for details, links, and resources. diff --git a/_approaches/lissa.md b/_approaches/lissa.md index e7c32bbe..b8cef86f 100644 --- a/_approaches/lissa.md +++ b/_approaches/lissa.md @@ -8,6 +8,12 @@ layout: page ![LiSSA Overview](/assets/img/icse25-approach.svg){:width="100%" style="background-color: white; border-radius: 8px; padding: 10px; display: block; margin: 0 auto;"} -LiSSA is a framework that leverages Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) for generic traceability link recovery. It is empirically evaluated on requirements-to-code, documentation-to-code, and documentation-to-model TLR tasks, outperforming state-of-the-art on code-related tasks. +LiSSA (Linking Software System Artifacts) is a retrieval-augmented, LLM-based approach that aims to be generic across artifact types. +The key idea is to use a Large Language Model (LLM) together with information retrieval (IR) to find trace links. +For a given source artifact (e.g. a requirement or a sentence in documentation), LiSSA first uses IR techniques to retrieve a small set of potentially relevant target artifacts (code files, model elements, etc.). +It then queries the LLM with the retrieved context to generate or suggest the most likely trace link. + +* Scope: LiSSA was tested on multiple tasks including requirements→code, documentation→code, and architecture-docs→models. The same RAG process is applied in each case, making it a one-size-fits-many solution. +* Effectiveness: In experiments, LiSSA significantly outperformed state-of-the-art tools on the code-centric tasks. For example, it showed much higher accuracy when linking requirements to code than prior methods. LiSSA is primarily associated with our [ICSE 2025 publication page](/c/icse25), but is also related to our [REFSQ 2025 publication page](/c/refsq25). See these pages for details, links, and resources. diff --git a/_approaches/swattr.md b/_approaches/swattr.md index e1eab92b..14bd99da 100644 --- a/_approaches/swattr.md +++ b/_approaches/swattr.md @@ -6,6 +6,15 @@ importance: 1 layout: page --- -SWATTR is an extendable, agent-based framework for creating trace links between textual software architecture documentation and models. It extracts text and model information, identifies elements in text, and connects these elements to model elements. SWATTR outperforms baseline approaches and is evaluated on multiple case studies. +![SWATTR Overview](/assets/img/ecsa21-approach.svg){:width="100%" style="border-radius: 8px; padding: 10px; display: block; margin: 0 auto;"} + +SWATTR (SoftWare Architecture TexT TRace link recovery) is an agent-based framework for linking textual architecture documentation (SAD) and formal models (SAM). +Rather than focusing on a single algorithm, SWATTR defines a pipeline with multiple stages where different "agents" can operate. +First it extracts and preprocesses text from the SAD and components from the architecture model. +Next, it uses NLP and heuristics to identify architecture elements (like component names) mentioned in the text. +Finally, it connects these identified text elements to model elements to form trace links. + +* Pipeline stages: The framework is extendable, meaning you can plug in different strategies at each step. For example, one agent might use term matching to find components in sentences, while another uses more advanced similarity measures. All results are aggregated to produce the final links. +* Results: SWATTR was evaluated on three case studies and achieved a weighted average F1-score of about 0.72 for trace recovery. This was a strong performance (outperforming simple baselines by ~0.24 F1) and demonstrated the benefit of the multi-stage approach. See our [ECSA 2021 publication page](/c/ecsa21) for details, links, and resources. diff --git a/_approaches/transarc.md b/_approaches/transarc.md index 9a1b4ee2..b954c7eb 100644 --- a/_approaches/transarc.md +++ b/_approaches/transarc.md @@ -6,10 +6,14 @@ importance: 3 layout: page --- -

- Approach Overview -

+![TransArC Overview](/assets/img/approach_overview_icse24.svg){:width="100%" style="background-color: white; border-radius: 8px; padding: 10px; display: block; margin: 0 auto;"} -TransArC combines ArDoCo (documentation-to-model) and ArCoTL (model-to-code) to create transitive trace links, bridging the semantic gap between documentation and code. It significantly outperforms baselines. +TransArC is a transitive trace link recovery approach that connects architecture documents to code via an intermediate architecture model. +It first uses an existing method (SWATTR) to connect the textual architecture documentation and component-based architecture model (SAM), then applies a new method (ArCoTL) to link the model elements to code. +In other words, TransArC builds a bridge: document ⟶ model ⟶ code. +This two-step strategy helps bridge the semantic gap between informal text and code. + +* How it works: TransArC extracts combines the two link sets of trace links, namely SWATTR and ArCoTL, to produce trace links transitively from documentation to code. +* Results: In experiments on five systems, TransArC achieved a high average F1 score (~0.82) for recovering documentation-to-code links, significantly outperforming baseline methods. This shows that combining the two specialized steps yields much more accurate links than simpler approaches. See our [ICSE 2024 publication page](/c/icse24) for details, links, and resources. diff --git a/_approaches/transarcai.md b/_approaches/transarcai.md index ed5969cd..3cc7ae99 100644 --- a/_approaches/transarcai.md +++ b/_approaches/transarcai.md @@ -8,6 +8,14 @@ layout: page ![TransArC-AI Overview](/assets/img/icsa25-approach.svg){:width="100%" style="background-color: white; border-radius: 8px; padding: 10px; display: block; margin: 0 auto;"} -TransArC AI is an LLM-based approach for traceability link recovery between software architecture documentation, models, and code. It leverages Large Language Models to extract component names as simple architecture models, bridging the semantic gap between documentation and code without the need for manual model creation. +TransArC-AI extends the TransArC idea by using an LLM to generate a simple architecture mode (SAM). +In this approach, instead of requiring a hand-made SAM, a large language model (such as GPT-4) is prompted to extract or invent the main component names from the SAD (and optionally from code). +These names serve as a minimal architecture model (i.e. a list of components). +Then, as in TransArC, these LLM-derived components are matched to code. +The goal is to bridge the SAD–code gap without manual modeling. + +* How it works: Given the software architecture text and the codebase, the system asks the LLM to list likely component names. That list of names forms a "Simple Software Architecture Model" (SSAM). Finally, code elements with matching names or descriptions are linked to the documentation. This pipeline avoids needing an explicit UML model. +* Effectiveness: TransArC-AI achieved very competitive results. Using GPT-4o, it obtained a weighted F1 of about 0.86, nearly as good as the original TransArC with a hand-made model (F1 0.87). It also substantially outperformed the ArDoCode baseline (which scored ~0.62). This shows that LLMs can automatically infer the key architectural components. + See our [ICSA 2025 publication page](/c/icsa25) for details, links, and resources. diff --git a/assets/img/approach_ardocode_icse24.svg b/assets/img/approach_ardocode_icse24.svg new file mode 100644 index 00000000..937ceaf3 --- /dev/null +++ b/assets/img/approach_ardocode_icse24.svg @@ -0,0 +1,4 @@ + + + +
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Architecture Documentation
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Trace Links
Trace...Text is not SVG - cannot display \ No newline at end of file From 940f6eaad242ea7ac9870eb213d5dd18c3068478 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Dominik=20Fuch=C3=9F?= Date: Sat, 19 Jul 2025 16:36:35 +0200 Subject: [PATCH 6/6] Update location of images. --- _approaches/arcotl.md | 6 +++--- _approaches/ardocode.md | 9 +++++---- _approaches/inconsistency-detection.md | 7 ++++--- _approaches/lissa.md | 6 +++--- _approaches/swattr.md | 6 +++--- _approaches/transarc.md | 6 +++--- _approaches/transarcai.md | 7 +++---- _conferences/aire25.md | 2 +- _conferences/ecsa21.md | 2 ++ _conferences/fg-arch24.md | 4 +--- _conferences/icsa23.md | 2 +- _conferences/icsa25.md | 2 +- _conferences/icse24.md | 4 +--- _conferences/icse25.md | 2 +- _conferences/refsq25.md | 2 +- _pages/poster_2019.md | 2 +- _pages/profiles.md | 12 ++++++------ .../aire25-aire.svg} | 0 .../ecsa21-swattr.svg} | 0 .../fgarch24-titleslide.png} | Bin .../icsa2019-poster.png} | Bin .../icsa23-inconsistency.svg} | 0 .../icsa25-transarc.svg} | 0 .../icse24-ardocode.svg} | 0 .../icse24-transarc.svg} | 0 .../icse25-lissa.svg} | 0 .../refsq25-refsq.svg} | 0 .../img/people/{corallo.jpg => corallo_sophie.jpg} | Bin .../img/people/{fuchss.jpg => fuchss_dominik.jpg} | Bin assets/img/people/{hey.jpg => hey_tobias.jpg} | Bin assets/img/people/{keim.jpg => keim_jan.jpg} | Bin .../img/people/{koziolek.jpg => koziolek_anne.jpg} | Bin assets/img/people/{liu.jpg => liu_haoyu.jpg} | Bin 33 files changed, 40 insertions(+), 41 deletions(-) rename assets/img/{aire-approach.svg => approaches/aire25-aire.svg} (100%) rename assets/img/{ecsa21-approach.svg => approaches/ecsa21-swattr.svg} (100%) rename assets/img/{titleslide-fg-arch24.png => approaches/fgarch24-titleslide.png} (100%) rename assets/img/{icsa2019_poster.png => approaches/icsa2019-poster.png} (100%) rename assets/img/{approach_overview_icsa23.svg => approaches/icsa23-inconsistency.svg} (100%) rename assets/img/{icsa25-approach.svg => approaches/icsa25-transarc.svg} (100%) rename assets/img/{approach_ardocode_icse24.svg => approaches/icse24-ardocode.svg} (100%) rename assets/img/{approach_overview_icse24.svg => approaches/icse24-transarc.svg} (100%) rename assets/img/{icse25-approach.svg => approaches/icse25-lissa.svg} (100%) rename assets/img/{refsq25-approach.svg => approaches/refsq25-refsq.svg} (100%) rename assets/img/people/{corallo.jpg => corallo_sophie.jpg} (100%) rename assets/img/people/{fuchss.jpg => fuchss_dominik.jpg} (100%) rename assets/img/people/{hey.jpg => hey_tobias.jpg} (100%) rename assets/img/people/{keim.jpg => keim_jan.jpg} (100%) rename assets/img/people/{koziolek.jpg => koziolek_anne.jpg} (100%) rename assets/img/people/{liu.jpg => liu_haoyu.jpg} (100%) diff --git a/_approaches/arcotl.md b/_approaches/arcotl.md index d5b98cd6..7384ad9f 100644 --- a/_approaches/arcotl.md +++ b/_approaches/arcotl.md @@ -6,15 +6,15 @@ importance: 2 layout: page --- -![ArCoTL Overview](/assets/img/approach_overview_icse24.svg){:width="100%" style="background-color: white; border-radius: 8px; padding: 10px; display: block; margin: 0 auto;"} +![ArCoTL Overview](/assets/img/approaches/icse24-transarc.svg){:width="100%" style="background-color: white; border-radius: 8px; padding: 10px; display: block; margin: 0 auto;"} ArCoTL (Architecture–Code Trace Links) focuses on linking a given architecture model (SAM) to the source code. It assumes you have a formal model of the system's components and interfaces, and wants to find the corresponding code. ArCoTL transforms both the architecture model and the code into intermediate representations (e.g. simplified graphs) and then applies various heuristics to match elements These heuristics include standalone rules and dependent rules (which consider relationships) plus filters to refine the links. -* How it works: Starting from a SAM and the codebase, ArCoTL builds simplified model and code representations. It then uses text similarity, naming conventions, and dependency heuristics to propose links between each model component and code artifact. -* Effectiveness: ArCoTL turned out to be very effective on its own. In experiments, the model-to-code step (ArCoTL) achieved an average F1 of ~0.98. +- How it works: Starting from a SAM and the codebase, ArCoTL builds simplified model and code representations. It then uses text similarity, naming conventions, and dependency heuristics to propose links between each model component and code artifact. +- Effectiveness: ArCoTL turned out to be very effective on its own. In experiments, the model-to-code step (ArCoTL) achieved an average F1 of ~0.98. ## References diff --git a/_approaches/ardocode.md b/_approaches/ardocode.md index 69270721..dca09637 100644 --- a/_approaches/ardocode.md +++ b/_approaches/ardocode.md @@ -6,12 +6,13 @@ importance: 5 layout: page --- -![ArCoTL Overview](/assets/img/approach_ardocode_icse24.svg){:width="100%" style="border-radius: 8px; padding: 10px; display: block; margin: 0 auto;"} +![ArCoTL Overview](/assets/img/approaches/icse24-ardocode.svg){:width="100%" style="border-radius: 8px; padding: 10px; display: block; margin: 0 auto;"} ArDoCode is a simpler variant of trace recovery that treats source code itself as the "model". Instead of first building a formal model, ArDoCode directly matches architecture document content with code elements using the same heuristics designed for linking docs to models. In practice, it extracts key terms from the documentation and tries to align them with names in the code (e.g. class or module names) as if the code were the model. -* Key idea: Apply the SWATTR approach without an explicit SAM by interpreting the codebase as a model. For example, if the doc mentions a component "WebUI" and there is a WebUI package in code, ArDoCode will link them. -* Effectiveness: Because it skips the formal modeling step, ArDoCode is easier to apply but less precise. In evaluations, ArDoCode achieved a weighted F1 of only ~0.62, substantially lower than the full TransArC method. It serves mainly as a baseline and demonstrates that without structured models, the TLR performance drops. -See our [ICSE 2024 publication page](/c/icse24) for details, links, and resources. \ No newline at end of file +- Key idea: Apply the SWATTR approach without an explicit SAM by interpreting the codebase as a model. For example, if the doc mentions a component "WebUI" and there is a WebUI package in code, ArDoCode will link them. +- Effectiveness: Because it skips the formal modeling step, ArDoCode is easier to apply but less precise. In evaluations, ArDoCode achieved a weighted F1 of only ~0.62, substantially lower than the full TransArC method. It serves mainly as a baseline and demonstrates that without structured models, the TLR performance drops. + +See our [ICSE 2024 publication page](/c/icse24) for details, links, and resources. diff --git a/_approaches/inconsistency-detection.md b/_approaches/inconsistency-detection.md index 3ad00a98..b1886f11 100644 --- a/_approaches/inconsistency-detection.md +++ b/_approaches/inconsistency-detection.md @@ -6,7 +6,7 @@ importance: 8 layout: page --- -![Approach Overview](/assets/img/approach_overview_icsa23.svg){:width="100%" style="background-color: white; border-radius: 8px; padding: 10px; display: block; margin: 0 auto;"} +![Approach Overview](/assets/img/approaches/icsa23-inconsistency.svg){:width="100%" style="background-color: white; border-radius: 8px; padding: 10px; display: block; margin: 0 auto;"} The ArDoCo inconsistency detection approach uses trace link recovery to detect inconsistencies between natural-language architecture documentation and formal models. It identifies two kinds of issues: @@ -15,7 +15,8 @@ It identifies two kinds of issues: (b) Missing Model Elements (MMEs): elements mentioned in the text that do not exist in the model. The method runs a TLR procedure (namely SWATTR) and then flags any model element with no corresponding text link (a UME) or any sentence that refers to a non-modeled item (an MME). -* Detection strategy: Use the TLR results as a bridge. After linking as many sentences to model elements as possible, any "orphan" model nodes or text mentions indicate a consistency gap. For example, if the model has a "Cache" component with no sentence linked, that is an UME; if the doc talks about "Common" but the model lacks it, that is an MME. -* Results: The approach achieved an excellent F1 (0.81) for the underlying trace recovery. For inconsistency detection, it attained ~93% accuracy in identifying UMEs and ~75% for MMEs, significantly better than naive baselines. These results suggest that using trace links is a promising way to find documentation-model mismatches. + +- Detection strategy: Use the TLR results as a bridge. After linking as many sentences to model elements as possible, any "orphan" model nodes or text mentions indicate a consistency gap. For example, if the model has a "Cache" component with no sentence linked, that is an UME; if the doc talks about "Common" but the model lacks it, that is an MME. +- Results: The approach achieved an excellent F1 (0.81) for the underlying trace recovery. For inconsistency detection, it attained ~93% accuracy in identifying UMEs and ~75% for MMEs, significantly better than naive baselines. These results suggest that using trace links is a promising way to find documentation-model mismatches. See our [ICSA 2023 publication page](/c/icsa23) for details, links, and resources. diff --git a/_approaches/lissa.md b/_approaches/lissa.md index b8cef86f..ae9cd408 100644 --- a/_approaches/lissa.md +++ b/_approaches/lissa.md @@ -6,14 +6,14 @@ importance: 6 layout: page --- -![LiSSA Overview](/assets/img/icse25-approach.svg){:width="100%" style="background-color: white; border-radius: 8px; padding: 10px; display: block; margin: 0 auto;"} +![LiSSA Overview](/assets/img/approaches/icse25-lissa.svg){:width="100%" style="background-color: white; border-radius: 8px; padding: 10px; display: block; margin: 0 auto;"} LiSSA (Linking Software System Artifacts) is a retrieval-augmented, LLM-based approach that aims to be generic across artifact types. The key idea is to use a Large Language Model (LLM) together with information retrieval (IR) to find trace links. For a given source artifact (e.g. a requirement or a sentence in documentation), LiSSA first uses IR techniques to retrieve a small set of potentially relevant target artifacts (code files, model elements, etc.). It then queries the LLM with the retrieved context to generate or suggest the most likely trace link. -* Scope: LiSSA was tested on multiple tasks including requirements→code, documentation→code, and architecture-docs→models. The same RAG process is applied in each case, making it a one-size-fits-many solution. -* Effectiveness: In experiments, LiSSA significantly outperformed state-of-the-art tools on the code-centric tasks. For example, it showed much higher accuracy when linking requirements to code than prior methods. +- Scope: LiSSA was tested on multiple tasks including requirements→code, documentation→code, and architecture-docs→models. The same RAG process is applied in each case, making it a one-size-fits-many solution. +- Effectiveness: In experiments, LiSSA significantly outperformed state-of-the-art tools on the code-centric tasks. For example, it showed much higher accuracy when linking requirements to code than prior methods. LiSSA is primarily associated with our [ICSE 2025 publication page](/c/icse25), but is also related to our [REFSQ 2025 publication page](/c/refsq25). See these pages for details, links, and resources. diff --git a/_approaches/swattr.md b/_approaches/swattr.md index 14bd99da..e72528a9 100644 --- a/_approaches/swattr.md +++ b/_approaches/swattr.md @@ -6,7 +6,7 @@ importance: 1 layout: page --- -![SWATTR Overview](/assets/img/ecsa21-approach.svg){:width="100%" style="border-radius: 8px; padding: 10px; display: block; margin: 0 auto;"} +![SWATTR Overview](/assets/img/approaches/ecsa21-swattr.svg){:width="100%" style="border-radius: 8px; padding: 10px; display: block; margin: 0 auto;"} SWATTR (SoftWare Architecture TexT TRace link recovery) is an agent-based framework for linking textual architecture documentation (SAD) and formal models (SAM). Rather than focusing on a single algorithm, SWATTR defines a pipeline with multiple stages where different "agents" can operate. @@ -14,7 +14,7 @@ First it extracts and preprocesses text from the SAD and components from the arc Next, it uses NLP and heuristics to identify architecture elements (like component names) mentioned in the text. Finally, it connects these identified text elements to model elements to form trace links. -* Pipeline stages: The framework is extendable, meaning you can plug in different strategies at each step. For example, one agent might use term matching to find components in sentences, while another uses more advanced similarity measures. All results are aggregated to produce the final links. -* Results: SWATTR was evaluated on three case studies and achieved a weighted average F1-score of about 0.72 for trace recovery. This was a strong performance (outperforming simple baselines by ~0.24 F1) and demonstrated the benefit of the multi-stage approach. +- Pipeline stages: The framework is extendable, meaning you can plug in different strategies at each step. For example, one agent might use term matching to find components in sentences, while another uses more advanced similarity measures. All results are aggregated to produce the final links. +- Results: SWATTR was evaluated on three case studies and achieved a weighted average F1-score of about 0.72 for trace recovery. This was a strong performance (outperforming simple baselines by ~0.24 F1) and demonstrated the benefit of the multi-stage approach. See our [ECSA 2021 publication page](/c/ecsa21) for details, links, and resources. diff --git a/_approaches/transarc.md b/_approaches/transarc.md index b954c7eb..e61cf193 100644 --- a/_approaches/transarc.md +++ b/_approaches/transarc.md @@ -6,14 +6,14 @@ importance: 3 layout: page --- -![TransArC Overview](/assets/img/approach_overview_icse24.svg){:width="100%" style="background-color: white; border-radius: 8px; padding: 10px; display: block; margin: 0 auto;"} +![TransArC Overview](/assets/img/approaches/icse24-transarc.svg){:width="100%" style="background-color: white; border-radius: 8px; padding: 10px; display: block; margin: 0 auto;"} TransArC is a transitive trace link recovery approach that connects architecture documents to code via an intermediate architecture model. It first uses an existing method (SWATTR) to connect the textual architecture documentation and component-based architecture model (SAM), then applies a new method (ArCoTL) to link the model elements to code. In other words, TransArC builds a bridge: document ⟶ model ⟶ code. This two-step strategy helps bridge the semantic gap between informal text and code. -* How it works: TransArC extracts combines the two link sets of trace links, namely SWATTR and ArCoTL, to produce trace links transitively from documentation to code. -* Results: In experiments on five systems, TransArC achieved a high average F1 score (~0.82) for recovering documentation-to-code links, significantly outperforming baseline methods. This shows that combining the two specialized steps yields much more accurate links than simpler approaches. +- How it works: TransArC extracts combines the two link sets of trace links, namely SWATTR and ArCoTL, to produce trace links transitively from documentation to code. +- Results: In experiments on five systems, TransArC achieved a high average F1 score (~0.82) for recovering documentation-to-code links, significantly outperforming baseline methods. This shows that combining the two specialized steps yields much more accurate links than simpler approaches. See our [ICSE 2024 publication page](/c/icse24) for details, links, and resources. diff --git a/_approaches/transarcai.md b/_approaches/transarcai.md index 3cc7ae99..4a5aba16 100644 --- a/_approaches/transarcai.md +++ b/_approaches/transarcai.md @@ -6,7 +6,7 @@ importance: 4 layout: page --- -![TransArC-AI Overview](/assets/img/icsa25-approach.svg){:width="100%" style="background-color: white; border-radius: 8px; padding: 10px; display: block; margin: 0 auto;"} +![TransArC-AI Overview](/assets/img/approaches/icsa25-transarc.svg){:width="100%" style="background-color: white; border-radius: 8px; padding: 10px; display: block; margin: 0 auto;"} TransArC-AI extends the TransArC idea by using an LLM to generate a simple architecture mode (SAM). In this approach, instead of requiring a hand-made SAM, a large language model (such as GPT-4) is prompted to extract or invent the main component names from the SAD (and optionally from code). @@ -14,8 +14,7 @@ These names serve as a minimal architecture model (i.e. a list of components). Then, as in TransArC, these LLM-derived components are matched to code. The goal is to bridge the SAD–code gap without manual modeling. -* How it works: Given the software architecture text and the codebase, the system asks the LLM to list likely component names. That list of names forms a "Simple Software Architecture Model" (SSAM). Finally, code elements with matching names or descriptions are linked to the documentation. This pipeline avoids needing an explicit UML model. -* Effectiveness: TransArC-AI achieved very competitive results. Using GPT-4o, it obtained a weighted F1 of about 0.86, nearly as good as the original TransArC with a hand-made model (F1 0.87). It also substantially outperformed the ArDoCode baseline (which scored ~0.62). This shows that LLMs can automatically infer the key architectural components. - +- How it works: Given the software architecture text and the codebase, the system asks the LLM to list likely component names. That list of names forms a "Simple Software Architecture Model" (SSAM). Finally, code elements with matching names or descriptions are linked to the documentation. This pipeline avoids needing an explicit UML model. +- Effectiveness: TransArC-AI achieved very competitive results. Using GPT-4o, it obtained a weighted F1 of about 0.86, nearly as good as the original TransArC with a hand-made model (F1 0.87). It also substantially outperformed the ArDoCode baseline (which scored ~0.62). This shows that LLMs can automatically infer the key architectural components. See our [ICSA 2025 publication page](/c/icsa25) for details, links, and resources. diff --git a/_conferences/aire25.md b/_conferences/aire25.md index eb8b3d55..7c7a4574 100644 --- a/_conferences/aire25.md +++ b/_conferences/aire25.md @@ -13,7 +13,7 @@ authors: To be published at the [33rd International Requirements Engineering Conference Workshops (REW)](https://aire-ws.github.io/aire25/). -![Approach Overview](/assets/img/aire-approach.svg){:width="100%" style="background-color: white; border-radius: 8px; padding: 10px; display: block; margin: 0 auto;"} +![AIRE25 Overview](/assets/img/approaches/aire25-aire.svg){:width="100%" style="background-color: white; border-radius: 8px; padding: 10px; display: block; margin: 0 auto;"} ## Abstract diff --git a/_conferences/ecsa21.md b/_conferences/ecsa21.md index d1f9b7fb..18e9b907 100644 --- a/_conferences/ecsa21.md +++ b/_conferences/ecsa21.md @@ -15,6 +15,8 @@ authors: Published at the [15th European Conference on Software Architecture (ECSA 2021), September 13-17 2021](https://conf.researchr.org/home/ecsa-2021) +![SWATTR Overview](/assets/img/approaches/ecsa21-swattr.svg){:width="100%" style="border-radius: 8px; padding: 10px; display: block; margin: 0 auto;"} + ## Abstract Software Architecture Documentation often consists of different artifacts. diff --git a/_conferences/fg-arch24.md b/_conferences/fg-arch24.md index bcbe7760..70c65dbe 100644 --- a/_conferences/fg-arch24.md +++ b/_conferences/fg-arch24.md @@ -8,9 +8,7 @@ authors: - tobias_hey --- -

- ArDoCo -

+![FGARCH24 Titleslide](/assets/img/approaches/fgarch24-titleslide.png){:width="100%" style="background-color: white; border-radius: 8px; padding: 10px; display: block; margin: 0 auto;"} Vortrag bei der Jahrestagung der GI-Fachgruppe "Architekturen" am 24. und 25. Oktober 2024 in Paderborn. diff --git a/_conferences/icsa23.md b/_conferences/icsa23.md index f13ecea3..6e76fa59 100644 --- a/_conferences/icsa23.md +++ b/_conferences/icsa23.md @@ -15,7 +15,7 @@ Published at the [20th IEEE International Conference on Software Architecture (I Additional presentation at the [Software Engineering 2024 (SE24)](https://se2024.se.jku.at/), the symposium of the German Computer Science Society (Gesellschaft fĂĽr Informatik (GI)) together with the Austrian Computer Society. -![Approach Overview](/assets/img/approach_overview_icsa23.svg){:width="100%"} +![Inconsistency Detection Overview](/assets/img/approaches/icsa23-inconsistency.svg){:width="100%" style="background-color: white; border-radius: 8px; padding: 10px; display: block; margin: 0 auto;"} ## Abstract diff --git a/_conferences/icsa25.md b/_conferences/icsa25.md index ebb69321..fcce2481 100644 --- a/_conferences/icsa25.md +++ b/_conferences/icsa25.md @@ -14,7 +14,7 @@ authors: 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%" style="background-color: white; border-radius: 8px; padding: 10px; display: block; margin: 0 auto;"} +![TransArC-AI Overview](/assets/img/approaches/icsa25-transarc.svg){:width="100%" style="background-color: white; border-radius: 8px; padding: 10px; display: block; margin: 0 auto;"} ## Abstract diff --git a/_conferences/icse24.md b/_conferences/icse24.md index 9240adda..4c34e3fd 100644 --- a/_conferences/icse24.md +++ b/_conferences/icse24.md @@ -17,9 +17,7 @@ Published at the [46th International Conference on Software Engineering (ICSE 20 Additional presentation at the [Software Engineering 2025 (SE25)](https://se2025.sdq.kastel.kit.edu/), the symposium of the German Computer Science Society (Gesellschaft fĂĽr Informatik (GI)). -

- Approach Overview -

+![TransArC Overview](/assets/img/approaches/icse24-transarc.svg){:width="100%" style="background-color: white; border-radius: 8px; padding: 10px; display: block; margin: 0 auto;"} ## Abstract diff --git a/_conferences/icse25.md b/_conferences/icse25.md index 6d26c778..bb3a42c1 100644 --- a/_conferences/icse25.md +++ b/_conferences/icse25.md @@ -16,7 +16,7 @@ authors: 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;"} +![LiSSA Overview](/assets/img/approaches/icse25-lissa.svg){:width="100%" style="background-color: white; border-radius: 8px; padding: 10px; display: block; margin: 0 auto;"} ## Abstract diff --git a/_conferences/refsq25.md b/_conferences/refsq25.md index cf909070..29128b17 100644 --- a/_conferences/refsq25.md +++ b/_conferences/refsq25.md @@ -13,7 +13,7 @@ authors: 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;"} +![REFSQ25 Overview](/assets/img/approaches/refsq25-refsq.svg){:width="100%" style="background-color: white; border-radius: 8px; padding: 10px; display: block; margin: 0 auto;"} ## Abstract diff --git a/_pages/poster_2019.md b/_pages/poster_2019.md index 3d3e9ba4..d01e34a8 100644 --- a/_pages/poster_2019.md +++ b/_pages/poster_2019.md @@ -8,4 +8,4 @@ description: Although slightly outdated, the poster below from the [ICSA2019 New and Emerging Ideas (NEMI) Track](https://icsa-conferences.org/2019/call-for-papers/new-and-emerging-ideas/index.html) underlines our main goals for checking consistency between formal architecture artefacts like [Palladio Component Models](https://www.palladio-simulator.com/science/palladio_component_model/) and informal software architecture artefacts in the form of textual software architecture documentation. For more details, check the [paper](https://publikationen.bibliothek.kit.edu/1000096077) as well as the [publications page](https://mcse.kastel.kit.edu/projects_ardoco.php?tab=%5B577%5D#tabpanel-577). -![Poster](/assets/img/icsa2019_poster.png "Poster"){:width="100%"} +![Poster](/assets/img/approaches/icsa2019-poster.png "Poster"){:width="100%"} diff --git a/_pages/profiles.md b/_pages/profiles.md index c2ba1796..fb1c83d0 100644 --- a/_pages/profiles.md +++ b/_pages/profiles.md @@ -8,32 +8,32 @@ nav_order: 7 profiles: - align: right - image: /people/koziolek.jpg + image: /people/koziolek_anne.jpg content: about/koziolek.md image_circular: false more_info: - align: right - image: /people/corallo.jpg + image: /people/corallo_sophie.jpg content: about/corallo.md image_circular: false more_info: - align: right - image: /people/fuchss.jpg + image: /people/fuchss_dominik.jpg content: about/fuchss.md image_circular: false more_info: - align: right - image: /people/keim.jpg + image: /people/keim_jan.jpg content: about/keim.md image_circular: false more_info: - align: right - image: /people/hey.jpg + image: /people/hey_tobias.jpg content: about/hey.md image_circular: false more_info: - align: right - image: /people/liu.jpg + image: /people/liu_haoyu.jpg content: about/liu.md image_circular: false more_info: diff --git a/assets/img/aire-approach.svg b/assets/img/approaches/aire25-aire.svg similarity index 100% rename from assets/img/aire-approach.svg rename to assets/img/approaches/aire25-aire.svg diff --git a/assets/img/ecsa21-approach.svg b/assets/img/approaches/ecsa21-swattr.svg similarity index 100% rename from assets/img/ecsa21-approach.svg rename to assets/img/approaches/ecsa21-swattr.svg diff --git a/assets/img/titleslide-fg-arch24.png b/assets/img/approaches/fgarch24-titleslide.png similarity index 100% rename from assets/img/titleslide-fg-arch24.png rename to assets/img/approaches/fgarch24-titleslide.png diff --git a/assets/img/icsa2019_poster.png b/assets/img/approaches/icsa2019-poster.png similarity index 100% rename from assets/img/icsa2019_poster.png rename to assets/img/approaches/icsa2019-poster.png diff --git a/assets/img/approach_overview_icsa23.svg b/assets/img/approaches/icsa23-inconsistency.svg similarity index 100% rename from assets/img/approach_overview_icsa23.svg rename to assets/img/approaches/icsa23-inconsistency.svg diff --git a/assets/img/icsa25-approach.svg b/assets/img/approaches/icsa25-transarc.svg similarity index 100% rename from assets/img/icsa25-approach.svg rename to assets/img/approaches/icsa25-transarc.svg diff --git a/assets/img/approach_ardocode_icse24.svg b/assets/img/approaches/icse24-ardocode.svg similarity index 100% rename from assets/img/approach_ardocode_icse24.svg rename to assets/img/approaches/icse24-ardocode.svg diff --git a/assets/img/approach_overview_icse24.svg b/assets/img/approaches/icse24-transarc.svg similarity index 100% rename from assets/img/approach_overview_icse24.svg rename to assets/img/approaches/icse24-transarc.svg diff --git a/assets/img/icse25-approach.svg b/assets/img/approaches/icse25-lissa.svg similarity index 100% rename from assets/img/icse25-approach.svg rename to assets/img/approaches/icse25-lissa.svg diff --git a/assets/img/refsq25-approach.svg b/assets/img/approaches/refsq25-refsq.svg similarity index 100% rename from assets/img/refsq25-approach.svg rename to assets/img/approaches/refsq25-refsq.svg diff --git a/assets/img/people/corallo.jpg b/assets/img/people/corallo_sophie.jpg similarity index 100% rename from assets/img/people/corallo.jpg rename to assets/img/people/corallo_sophie.jpg diff --git a/assets/img/people/fuchss.jpg b/assets/img/people/fuchss_dominik.jpg similarity index 100% rename from assets/img/people/fuchss.jpg rename to assets/img/people/fuchss_dominik.jpg diff --git a/assets/img/people/hey.jpg b/assets/img/people/hey_tobias.jpg similarity index 100% rename from assets/img/people/hey.jpg rename to assets/img/people/hey_tobias.jpg diff --git a/assets/img/people/keim.jpg b/assets/img/people/keim_jan.jpg similarity index 100% rename from assets/img/people/keim.jpg rename to assets/img/people/keim_jan.jpg diff --git a/assets/img/people/koziolek.jpg b/assets/img/people/koziolek_anne.jpg similarity index 100% rename from assets/img/people/koziolek.jpg rename to assets/img/people/koziolek_anne.jpg diff --git a/assets/img/people/liu.jpg b/assets/img/people/liu_haoyu.jpg similarity index 100% rename from assets/img/people/liu.jpg rename to assets/img/people/liu_haoyu.jpg