Generative AI Code Analysis for Legacy Software Conversion

The patent introduces an intermediary system consisting of code analysis tools and AI models that act as a mediator between the legacy code and developers. This intermediary automatically performs static analysis, generates code documentation, and explains code functionality, thereby reducing the time developers need to spend on manual code comprehension while maintaining high accuracy through multiple analysis layers including syntax parsing, semantic analysis, and AI-based interpretation.

The patent segments the code analysis process into multiple independent modules: syntax analysis module, semantic analysis module, dependency analysis module, and AI-based comprehension module. Each module handles a specific aspect of code analysis separately, processing different dimensions of code understanding independently. This segmentation reduces processing complexity by breaking down the overwhelming task of comprehensive analysis into manageable, specialized components that can be executed in sequence or parallel.

The patent implements a feedback mechanism where an AI model analyzes the original legacy code, generates the converted code in modern programming languages, and then re-analyzes the converted code to verify functionality preservation. The system compares the behavioral characteristics, input-output relationships, and logical structures of both codes, providing feedback for iterative refinement. This closed-loop feedback process ensures that the converted code maintains the original functionality while achieving improved adaptability to modern environments.

The patent enables legacy code to generate its own documentation automatically through self-service mechanisms. The system performs automated static analysis, code parsing, and semantic understanding to generate comprehensive documentation including architecture diagrams, function descriptions, dependency maps, and maintenance guidelines directly from the code itself. This eliminates the need for separate manual documentation efforts, as the code serves its own documentation needs through automated analysis and generation processes.