Tri-affinity Model Platform for Cross-Platform Application Development
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Solution Overview
Problem
Current cross-platform application development methods are inefficient due to the need for skilled developers to handle multiple platforms, high development time, and costs associated with manual coding, with applications being 'black boxes' until used, lacking analysis during development and runtime, and not scaling well with the rapid evolution of devices and user engagement modes.
Innovation Solution
The tri-affinity model driven method and platform (TAMDP) employs human, machine, and analysis affinities to author, realize, and analyze cross-platform applications, using domain-specific modeling languages like Alfa for human affinity models, model transformations for machine affinity models, and analysis affinity models for development and runtime analysis, enabling automated code generation and machine learning-based recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual coding methods are used for cross-platform application development, then developers can create applications with full control over functionality, but development time and costs increase significantly
Solution Approach 1:
The patent segments the application development process into distinct model components (human affinity model, machine affinity model, analysis affinity model) that can be independently created, transformed, and analyzed. This segmentation allows automated code generation from models, significantly reducing manual coding time while maintaining development control.
Solution Approach 2:
The patent performs preliminary actions by creating comprehensive application models before actual code generation. The models capture all application aspects (UI, data, logic, platform requirements) in advance, enabling automated transformation into platform-specific code and reducing subsequent development time.
2Reliability
If skilled developers manually code cross-platform applications, then application functionality can be optimized, but development costs increase due to the need for multiple platform expertise
Solution Approach 1:
The patent creates a universal modeling framework that works across multiple platforms (mobile, web, desktop, IoT) through a single set of affinity models. The model-to-model and model-to-code transformations handle platform-specific adaptations automatically, reducing development costs while maintaining functionality across diverse platforms.
Solution Approach 2:
The patent introduces affinity models as intermediary representations between developer intent and platform-specific implementation. These models serve as mediators that capture application requirements independently of target platforms, enabling automated generation of platform-appropriate code while preserving functional requirements.
3Adaptability or versatility
If traditional development methods are used, then applications can be built with existing tools, but applications remain 'black boxes' lacking analysis capability during development and runtime
Solution Approach 1:
The patent implements feedback mechanisms through the analysis affinity model that continuously analyzes application behavior during development and runtime. This model provides feedback on performance, resource usage, and compliance with requirements, enabling adaptive optimization without significantly increasing system complexity.
Solution Approach 2:
The patent adds an analysis dimension to traditional development by introducing the analysis affinity model that operates alongside human and machine affinity models. This additional dimension enables comprehensive analysis capabilities while maintaining the existing development workflow through model transformations.
4Adaptability or versatility
If developers target multiple client runtime platforms, then application ubiquity increases, but the complexity of managing platform-specific requirements increases
Solution Approach 1:
The patent creates universal affinity models that represent application requirements independently of target platforms. The model transformation framework automatically adapts these universal models to platform-specific implementations, enabling broad platform coverage while managing complexity through automated transformations rather than manual platform-specific development.
Solution Approach 2:
The patent segments platform-specific requirements into distinct transformation rules within the model-to-model and model-to-code processes. Each target platform has its own transformation configuration, allowing comprehensive platform support while managing complexity through organized, modular transformation logic rather than monolithic platform management.
Data Source
AI summary
A tri-affinity model driven platform (TAMDP) employs a tri-affinity model driven method using a human affinity model (HAM), a machine affinity model (MAM), and an analysis affinity model (AAM), to generate an application specific instance of predefined meta-models for building a cross-platform application. A developer authors an application in the HAM which is compiled to the MAM by a compiler, which is transformed to the AAM by a model-to-model transformer. A translator optionally translates a HAM to another HAM. A generator generates source code from MAM. Build tooling builds application binaries for different rendering types from a source code generated for the application. A development time analyzer and visualizer (DTAV) enables development time analyses using the AAM. After prototyping and introspection, a TAMDP runtime subsystem executes the generated application and a machine learning based recommendation engine enhances the application using the AAM after analysis by the DTAV and a runtime analyzer and visualizer.