[logseq-plugin-git:commit] 2025-12-27T19:07:25.270Z

pages/PAPERS___2024-TOSEM-FUTURE-SE.md

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 	   The proliferation of domain-specific modeling languages (DSMLs) and modeling paradigms has led to a diverse landscape of formalisms and languages within MDE. This diversity poses challenges for automation, as each modeling language may have its own syntax, semantics, and transformation rules. Ensuring interoperability and tooling support across diverse formalisms and languages requires developing standardized interfaces, transformation mechanisms, and model interchange formats. Moreover, accommodating new modeling paradigms, such as graph-based modeling or aspect-oriented modeling, necessitates continuous adaptation of automation techniques and tools.
 	- 3. **The Allware/Low-Code Objective of MDE:**
 	   MDE aims to democratize software development by enabling non-computer science and software engineering specialists to participate in modeling and code generation activities. However, achieving this objective requires overcoming several challenges. One challenge is designing intuitive modeling tools that abstract away technical complexities and provide user-friendly interfaces for creating and manipulating models. Additionally, ensuring that automated code generation processes produce high-quality, maintainable code requires addressing issues related to code efficiency, readability, and adherence to coding standards. Moreover, integrating MDE capabilities into low-code platforms and environments necessitates developing standardized interfaces and interoperability protocols.
-	- 4. **Scarcity of Datasets and Benchmarks for AI Models:**
-	   The advancement of AI-driven automation in MDE is hindered by the scarcity of high-quality datasets and benchmarks for training and evaluating AI models. Unlike domains like natural language processing or computer vision, where large-scale datasets are readily available, MDE lacks comprehensive datasets that capture the diverse range of modeling tasks and domains. Moreover, concerns regarding data privacy and the ethical use of data further complicate the acquisition and sharing of modeling datasets. Addressing these challenges requires collaboration between researchers, practitioners, and stakeholders to curate datasets, define evaluation metrics, and establish best practices for data collection and sharing in MDE contexts.
+	- 4. **Scarcity of Datasets and [[Benchmarks]] for AI Models:**
+	   The advancement of AI-driven automation in MDE is hindered by the scarcity of high-quality datasets and [[benchmarks]] for training and evaluating AI models. Unlike domains like natural language processing or computer vision, where large-scale datasets are readily available, MDE lacks comprehensive datasets that capture the diverse range of modeling tasks and domains. Moreover, concerns regarding data privacy and the ethical use of data further complicate the acquisition and sharing of modeling datasets. Addressing these challenges requires collaboration between researchers, practitioners, and stakeholders to curate datasets, define evaluation metrics, and establish best practices for data collection and sharing in MDE contexts.
 	- 5. **Better Modeling Tools:**
 	   Despite significant advancements in modeling tools, many existing tools in MDE are tailored towards expert users and lack intuitive interfaces for non-specialists. This poses challenges for broadening the adoption of MDE methodologies across diverse domains and user communities. Improving the accessibility and usability of modeling tools requires incorporating user-centered design principles, conducting usability studies, and providing comprehensive documentation and training materials. Additionally, ensuring compatibility and interoperability between different modeling tools and environments is essential for facilitating seamless collaboration and exchange of models among users with varying expertise levels.
 - ### Perspectives