Hybrid Neural Network Code Migration With Automated Conflict Resolution
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Solution Overview
Problem
The challenge of merging and migrating software code developed in multiple programming languages is complicated by differing syntaxes, libraries, and performance optimization issues, leading to quality and reliability problems in software systems.
Innovation Solution
A system using hybrid neural networks, specifically a transformer-based sequence-to-sequence model and Siamese neural network, translates, merges, and deploys unified code across platforms, detecting and resolving conflicts to optimize the process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If software code from multiple programming languages is merged using traditional methods, then manual intervention and conflict resolution are required, but this increases time consumption and reduces productivity
Solution Approach 1:
The patent introduces an AI-based intermediary system that acts as a mediator between multiple programming languages during the merging process. This system automatically detects conflicts, analyzes them using machine learning models, and proposes resolutions, eliminating the need for manual intervention and significantly reducing the time required for code merging while maintaining high productivity
Solution Approach 2:
The patent replaces manual mechanical processes of code review and conflict resolution with automated AI-based systems. Machine learning models analyze code patterns, detect conflicts, and generate resolutions automatically, substituting human effort with intelligent automation that improves efficiency and reduces time loss
2Reliability
If code translation between programming languages is performed manually, then quality control is difficult, but this leads to reliability problems in the merged software system
Solution Approach 1:
The patent implements feedback mechanisms where AI models continuously learn from translation outcomes and conflict resolution results. The system analyzes successful translations and conflict resolutions to improve future translations, ensuring consistent quality and reliability while maintaining high translation accuracy across different programming languages
Solution Approach 2:
The patent replaces manual code translation with automated AI-based translation systems that use machine learning models trained on programming language patterns. This substitution ensures consistent translation quality, reduces human error, and improves both reliability and manufacturing precision of the code translation process
3Manufacturing precision
If comprehensive code analysis is performed to detect all conflicts, then merge quality improves, but the complexity of the merging system increases
Solution Approach 1:
The patent segments the code analysis process into multiple specialized AI models, each trained to detect specific types of conflicts and patterns in different programming languages. This segmentation allows comprehensive conflict detection while managing system complexity by dividing the analysis task into manageable, specialized components that can be independently optimized
Solution Approach 2:
The patent dynamically adjusts analysis parameters and depth based on the specific code being merged. The AI system evaluates code characteristics and selectively applies different levels of analysis intensity, maintaining high detection accuracy while reducing unnecessary computational complexity for straightforward merge scenarios
4Adaptability or versatility
If multiple programming languages with different syntaxes and libraries are integrated, then platform versatility improves, but performance optimization becomes difficult
Solution Approach 1:
The patent optimizes performance by dynamically adjusting code parameters and translation strategies based on the target platform and programming language characteristics. The AI system selects optimal translation approaches and optimization techniques for each language pair, maintaining high performance while supporting multiple platforms and languages
Data Source
AI summary
Embodiments of the present invention provide a system for performing multi-platform software code merging and migration using hybrid neural networks. The system is configured for identifying source files in a software code development environment, wherein each of the source files comprise software code associated with entity applications that is developed by one or more users in one or more software programming languages, translating the software code in each of the source files that is developed in the one or more software programming languages into a translated code that is in a first software programming language, merging the translated code associated with the software code in each of the source files into a unified code, wherein the unified code is in the first software programming language, automatically performing build and packaging of the unified code for deployment, and automatically deploying the packaged unified code into a software code production environment.