Spell check

Author: dfuchss

_approaches/arcotl.md

@@ -10,7 +10,7 @@ layout: approach
 
 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
+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.

_approaches/exarch.md

@@ -8,7 +8,7 @@ layout: approach
 
 ![ExArch Overview](/assets/img/approaches/icsa25-transarc.svg){:width="100%" style="background-color: white; border-radius: 8px; padding: 10px; display: block; margin: 0 auto;"}
 
-ExArch extends the TransArC idea by using an LLM to generate a simple architecture mode (SAM).
+ExArch extends the TransArC idea by using an LLM to generate a simple architecture model (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.

_approaches/inconsistency-detection.md

@@ -16,5 +16,5 @@ It identifies two kinds of issues:
 
 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.
+- 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 a 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.

_approaches/transarc.md

@@ -13,5 +13,5 @@ It first uses an existing method (SWATTR) to connect the textual architecture do
 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.
+- How it works: TransArC 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.

_pages/poster_2019.md

@@ -5,7 +5,7 @@ title: Initial Idea Poster (2019)
 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.
+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 artifacts like [Palladio Component Models](https://www.palladio-simulator.com/science/palladio_component_model/) and informal software architecture artifacts 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/approaches/icsa2019-poster.png "Poster"){:width="100%"}