Feature Model Integration of ML Components in Software Products
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Solution Overview
Problem
Current integration of machine learning (ML) components in software products is complex and requires ML expertise not available to most development teams, leading to standalone development and black-box integration, which complicates configuration management and maintenance.
Innovation Solution
A feature model-based method and system for integrating ML components, utilizing a product feature model to specify ML requirements, auto-generate ML code, and integrate ML models into software products, enabling seamless integration and configuration management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If ML components are integrated into software products using traditional methods, then ML functionality is provided, but integration complexity and dependency management difficulty increase significantly
Solution Approach 1:
The patent introduces a feature model as an intermediary layer between software product features and ML components. This feature model captures dependencies and relationships, allowing ML components to be integrated through feature-based specifications rather than direct complex integration, thereby reducing integration complexity while maintaining adaptability
Solution Approach 2:
The patent segments the integration process into distinct phases: feature model creation, dependency specification, code generation, and model integration. This segmentation allows each aspect to be managed independently, reducing overall integration complexity while enabling versatile ML component integration
2Ease of operation
If ML components are integrated with full control over processing, then development flexibility improves, but configuration management and maintenance complexity increase
Solution Approach 1:
The patent creates a universal feature model framework that can represent various ML components and their dependencies in a unified manner. This universal approach allows different ML components to be managed through the same feature-based interface, improving ease of operation while reducing configuration management complexity through standardization
3Reliability
If ML development is performed in standalone manner by ML experts, then ML model quality improves, but integration speed and product development efficiency decrease
Solution Approach 1:
The patent performs preliminary actions by creating the feature model and specifying dependencies before actual ML component integration. This preliminary structuring enables automated code generation and streamlines the integration process, maintaining ML model quality through careful upfront planning while significantly improving integration speed
Solution Approach 2:
The system enables self-service through automated code generation from feature models. Once the feature model is created, the system can automatically generate the necessary code and integrate ML components without requiring constant ML expert intervention, thereby maintaining model quality while improving integration productivity
Data Source
AI summary
This disclosure relates generally to a system and method for feature model-based integration of machine learning (ML) components in software products. Currently, formulation of ML solution and integrating it into a software product is performed by ML experts. The present disclosure provides a framework for automating ML development and integration into the software product. The method utilizes a product feature model and a ML meta-model to specify integration of ML capabilities into the software product. The disclosed method integrates various steps in the ML development process such as mapping of the business use case as an ML problem, pre-processing raw data, identifying metrics to measure model performance, defining training and test data sets, training multiple ML models, tuning their parameters and making predictions on test data, to the software product. Dependency relationships are mapped between learning features and product features for including necessary features of the software product configurations.