Extending Model-to-Model Transformations via Dynamic Insertion
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Solution Overview
Problem
Conventional model-driven development (MDD) requires manual and resource-intensive efforts for model-to-model transformations, which are not easily adaptable to changing user requirements, leading to inefficiencies as new transformation specifications are needed for each scenario.
Innovation Solution
A method and system for extending existing model-to-model transformations by identifying insertion points for specified transform elements and dynamically applying them to produce an extended transformation, allowing for the merging of extensions with base transformations to accommodate specific user needs without recreating the entire transformation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual model-to-model transformations are used, then transformation accuracy can be maintained, but substantial expertise and development resources are required
Solution Approach 1:
The transformation process is segmented into reusable transformation templates and customizable extensions. The base transformation handles common scenarios while extensions handle specific user requirements, dividing the work between automated templates and targeted customizations.
Solution Approach 2:
Pre-defined transformation templates serve as reusable copies that can be applied across multiple scenarios. Instead of manually creating transformations each time, the system copies and adapts existing templates through extensions, reducing repetitive manual work while maintaining accuracy.
2Productivity
If pre-specified transformations are used, then automation efficiency is improved, but adaptability to changing user requirements deteriorates
Solution Approach 1:
The transformation system is made dynamic by allowing runtime configuration through extensions. Users can modify transformation behavior by adding or adjusting extension elements without changing the core transformation logic, enabling adaptation to new requirements while preserving automated execution.
Solution Approach 2:
The transformation template serves multiple functions: it provides the base automated transformation logic and acts as a foundation for multiple specialized transformations through extensions. This multi-functionality allows a single template to handle diverse scenarios by combining automation with customizable extensions.
3Manufacturing precision
If complete transformation recreation is done for new scenarios, then transformation accuracy is maintained, but development time increases
Solution Approach 1:
Common transformation patterns are prepared in advance as reusable templates. When new scenarios arise, developers start with pre-prepared templates rather than creating transformations from scratch, significantly reducing development time while maintaining quality through the template's proven structure.
Solution Approach 2:
The system merges the stability of pre-defined templates with the flexibility of custom extensions. This combination allows developers to leverage existing accurate transformations while adding only the necessary customizations for new scenarios, avoiding complete recreation.
Data Source
AI summary
A method, system and apparatus for extending an existing model-to-model transformation. A method for extending a model-to-model transformation can include merging an extension to the model-to-model transformation with the model-to-model transformation to produce an extended model-to-model transformation. Specifically, the merging step can include identifying insertion points for specified transform elements in the model-to-model transformation and inserting the specified transform elements at the identified insertion points in the model-to-model transformation to produce an extended model-to-model transformation. Subsequently, the extended model-to-model transformation can be dynamically applied to a source model in order to produce a target model.