Addressing Visual Impairments with Model-Driven Engineering: A Systematic Literature Review
- URL: http://arxiv.org/abs/2510.06483v1
- Date: Tue, 07 Oct 2025 21:46:26 GMT
- Title: Addressing Visual Impairments with Model-Driven Engineering: A Systematic Literature Review
- Authors: Judith Michael, Lukas Netz, Bernhard Rumpe, Ingo Müller, John Grundy, Shavindra Wickramathilaka, Hourieh Khalajzadeh,
- Abstract summary: This paper presents a systematic literature review on how MDE addresses accessibility for vision impairments.<n>About two-thirds reference the Web Content Accessibility Guidelines (WCAG), yet their project-specific adaptions and end-user validations hinder wider adoption in MDE.<n>The analyzed studies model user interface structures, interaction and navigation, user capabilities, requirements, and context information.
- Score: 3.877019086698171
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Software applications often pose barriers for users with accessibility needs, e.g., visual impairments. Model-driven engineering (MDE), with its systematic nature of code derivation, offers systematic methods to integrate accessibility concerns into software development while reducing manual effort. This paper presents a systematic literature review on how MDE addresses accessibility for vision impairments. From 447 initially identified papers, 30 primary studies met the inclusion criteria. About two-thirds reference the Web Content Accessibility Guidelines (WCAG), yet their project-specific adaptions and end-user validations hinder wider adoption in MDE. The analyzed studies model user interface structures, interaction and navigation, user capabilities, requirements, and context information. However, only few specify concrete modeling techniques on how to incorporate accessibility needs or demonstrate fully functional systems. Insufficient details on MDE methods, i.e., transformation rules or code templates, hinder the reuse, generalizability, and reproducibility. Furthermore, limited involvement of affected users and limited developer expertise in accessibility contribute to weak empirical validation. Overall, the findings indicate that current MDE research insufficiently supports vision-related accessibility. Our paper concludes with a research agenda outlining how support for vision impairments can be more effectively embedded in MDE processes.
Related papers
- Data-Driven Methods and AI in Engineering Design: A Systematic Literature Review Focusing on Challenges and Opportunities [0.2545763876632975]
Machine learning and statistical methods dominate current practice, whereas deep learning exhibits a clear upward trend in adoption.<n>Key challenges in existing applications include limited model interpretability, poor cross-stage traceability, and insufficient validation under real-world conditions.<n>This review is a first step toward design-stage guidelines; a follow-up synthesis should map computer science algorithms to engineering design problems and activities.
arXiv Detail & Related papers (2025-11-25T11:16:38Z) - LTD-Bench: Evaluating Large Language Models by Letting Them Draw [57.237152905238084]
LTD-Bench is a breakthrough benchmark for large language models (LLMs)<n>It transforms LLM evaluation from abstract scores to directly observable visual outputs by requiring models to generate drawings through dot matrices or executable code.<n> LTD-Bench's visual outputs enable powerful diagnostic analysis, offering a potential approach to investigate model similarity.
arXiv Detail & Related papers (2025-11-04T08:11:23Z) - Scaling Beyond Context: A Survey of Multimodal Retrieval-Augmented Generation for Document Understanding [61.36285696607487]
Document understanding is critical for applications from financial analysis to scientific discovery.<n>Current approaches, whether OCR-based pipelines feeding Large Language Models (LLMs) or native Multimodal LLMs (MLLMs) face key limitations.<n>Retrieval-Augmented Generation (RAG) helps ground models in external data, but documents' multimodal nature, combining text, tables, charts, and layout, demands a more advanced paradigm: Multimodal RAG.
arXiv Detail & Related papers (2025-10-17T02:33:16Z) - A Systematic Survey of Model Extraction Attacks and Defenses: State-of-the-Art and Perspectives [65.3369988566853]
Recent studies have demonstrated that adversaries can replicate a target model's functionality.<n>Model Extraction Attacks pose threats to intellectual property, privacy, and system security.<n>We propose a novel taxonomy that classifies MEAs according to attack mechanisms, defense approaches, and computing environments.
arXiv Detail & Related papers (2025-08-20T19:49:59Z) - Multi-Modal Requirements Data-based Acceptance Criteria Generation using LLMs [17.373348983049176]
We propose RAGcceptance M2RE, a novel approach to generate acceptance criteria from multi-modal requirements data.<n>We show that our approach effectively reduces manual effort, captures nuanced stakeholder intent, and provides valuable criteria.<n>This research underscores the potential of multi-modal RAG techniques in streamlining software validation processes and improving development efficiency.
arXiv Detail & Related papers (2025-08-09T08:35:40Z) - Toward Inclusive Low-Code Development: Detecting Accessibility Issues in User Reviews [4.116734692256577]
Low-code applications may unintentionally exclude users with visual impairments, such as color blindness and low vision.<n>We construct a comprehensive dataset of low-code application reviews, consisting of accessibility-related reviews and non-accessibility-related reviews.<n>Our proposed hybrid model achieves an accuracy and F1-score of 78% in detecting accessibility-related issues.
arXiv Detail & Related papers (2025-04-27T02:54:28Z) - User Modeling in Model-Driven Engineering: A Systematic Literature Review [1.7450893625541586]
We conduct a systematic literature review to analyze existing proposals for user modeling in model-driven engineering (MDE) approaches.<n>The results showcase that there is a lack of a unified and complete user modeling perspective.<n>This limits the implementation of richer user interfaces able to better support the user-specific needs.
arXiv Detail & Related papers (2024-12-20T13:19:57Z) - Unsupervised Model Diagnosis [49.36194740479798]
This paper proposes Unsupervised Model Diagnosis (UMO) to produce semantic counterfactual explanations without any user guidance.
Our approach identifies and visualizes changes in semantics, and then matches these changes to attributes from wide-ranging text sources.
arXiv Detail & Related papers (2024-10-08T17:59:03Z) - CELA: Cost-Efficient Language Model Alignment for CTR Prediction [70.65910069412944]
Click-Through Rate (CTR) prediction holds a paramount position in recommender systems.<n>Recent efforts have sought to mitigate these challenges by integrating Pre-trained Language Models (PLMs)<n>We propose textbfCost-textbfEfficient textbfLanguage Model textbfAlignment (textbfCELA) for CTR prediction.
arXiv Detail & Related papers (2024-05-17T07:43:25Z) - ACCESS: Prompt Engineering for Automated Web Accessibility Violation
Corrections [0.0]
This paper presents a novel approach to correcting accessibility violations on the web by modifying the document object model (DOM) in real time with foundation models.
We achieved greater than a 51% reduction in accessibility violation errors after corrections on our novel benchmark: ACCESS.
arXiv Detail & Related papers (2024-01-28T22:49:33Z) - Language Models as a Service: Overview of a New Paradigm and its
Challenges [47.75762014254756]
Some of the most powerful language models currently are proprietary systems, accessible only via (typically restrictive) web or programming.
This paper has two goals: on the one hand, we delineate how the aforementioned challenges act as impediments to the accessibility, replicability, reliability, and trustworthiness of LM interfaces.
On the other hand, it serves as a comprehensive resource for existing knowledge on current, major LM, offering a synthesized overview of the licences and capabilities their interfaces offer.
arXiv Detail & Related papers (2023-09-28T16:29:52Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.