ML-Based Technology Migration for Mixed Code and Data
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Solution Overview
Problem
Existing technology migration methods face challenges due to the lack of one-to-one mapping between source and target technologies, requiring manual intervention and high costs, especially when dealing with structured data and mixed-format data, and lack of accurate end-to-end transformation using large language models (LLMs).
Innovation Solution
A system and method utilizing machine learning models to dynamically transform unstructured and structured data from a source technology into a target technology, including classification and optimization of nodes using a self-evolving application model trained on unique variations, and generating a hierarchy through a hierarchical mapping module.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional technology migration methods are used, then manual intervention is required to handle the lack of one-to-one mapping, but this increases time consumption and cost
Solution Approach 1:
The patent introduces an intermediary system comprising a hierarchical mapping module and multiple machine learning models that act as a mediator between source and target technology stacks. This intermediary automatically performs the complex transformation task, eliminating the need for manual intervention while maintaining high accuracy in the migration process.
Solution Approach 2:
The patent replaces the mechanical manual process of technology migration with an automated machine learning-based system. The ML models automatically analyze, classify, and transform code structures, substituting human manual effort with intelligent automated processing that is both faster and more consistent.
2Extent of automation
If large language models are used for end-to-end transformation, then automation is improved, but cost and infrastructure requirements increase
Solution Approach 1:
The patent segments the monolithic large language model approach into multiple specialized machine learning models, each handling specific aspects of the migration process (e.g., hierarchical mapping, node classification, transformation). This segmentation reduces the computational burden and infrastructure requirements while maintaining high automation levels.
Solution Approach 2:
The patent employs a dynamic, multi-stage processing approach where different ML models are applied at different stages of the migration process. This dynamic allocation of computational resources optimizes energy efficiency by only applying the necessary transformation logic for each specific migration scenario, rather than using a single large model for all tasks.
3Productivity
If existing machine learning models are used for text translation, then some automation is achieved, but they fail to provide accurate results for multiple data formats and structured code
Solution Approach 1:
The patent applies local quality by using different specialized ML models for different types of data and code structures. Instead of a single generic model, the system employs hierarchical mapping for structured data, semantic analysis for code nodes, and format-specific transformation rules, ensuring high accuracy for each particular data type while maintaining overall productivity.
Data Source
AI summary
The system and method for automated technology migration is disclosed. The method involves receiving source code associated with a source technology comprising unstructured data and structured data. The unstructured data is transformed by generating a hierarchy comprising source set of nodes. Each node among the source set of nodes is classified, by a first machine learning (ML) model, into a corresponding category from among plurality of predefined categories based on a semantic search. In response to the classification, a plurality of ML models optimizes transformation of the source set of nodes into a target set of nodes associated with a target technology. The structured data is transformed into a self-evolving application model and then to target structure using hierarchical mapping module. The self-evolving application model is trained and updated using the plurality of ML models based on at least one sample per unique variation of the source set of nodes.