LLM-Based Multi-Language Code Flow Mapping With Unified Dependency Graphs
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Solution Overview
Problem
Complex software systems comprising multiple programming languages pose challenges in understanding program and data flows, requiring resource-intensive language-specific expertise and inefficient maintenance.
Innovation Solution
Employing Generative AI Large Language Models (LLMs) to analyze multi-language software applications by converting code into a unified graph format, utilizing programming-language-specific prompts to extract functional components and visualize dependencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional language-specific expertise is used to analyze multi-language software systems, then understanding accuracy is maintained, but resource consumption and time requirements increase significantly
Solution Approach 1:
The patent applies universality by creating a single LLM-based analysis system that can handle multiple programming languages (Java, Python, C++, JavaScript, etc.) through a unified architecture. Instead of requiring separate analysis tools and experts for each language, the system uses language-specific prompts within one LLM framework to achieve multi-language support, thereby improving resource efficiency while maintaining analysis accuracy
Solution Approach 2:
The patent introduces language-specific prompts as intermediaries between the LLM and the source code. These prompts act as mediators that enable the LLM to understand and analyze code in different programming languages without requiring language-specific expertise in the core model, thus resolving the contradiction between maintaining accuracy and improving efficiency
2Loss of information
If manual analysis of complex software systems is performed, then detailed insights are obtained, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The patent implements self-service by enabling the LLM to automatically analyze software systems without requiring manual intervention from experts. The system autonomously processes source code, generates dependency graphs, and produces analysis results by itself, dramatically reducing both time and labor requirements while maintaining comprehensive detail through the LLM's capabilities
Solution Approach 2:
The patent applies preliminary action by having the LLM perform code analysis and graph generation in advance, before human review is needed. The system proactively processes the entire codebase, identifies dependencies, and creates visualizations automatically, eliminating the need for time-consuming manual analysis while preserving complete information
3Measurement precision
If custom static analysis tools are developed for each language, then language-specific accuracy is improved, but system complexity and development costs increase
Solution Approach 1:
The patent merges multiple language-specific analysis capabilities into a single unified LLM-based system. Instead of maintaining separate static analysis tools for each programming language, the system combines them all within one LLM framework that uses language-specific prompts to achieve the same level of accuracy as specialized tools would provide, thereby reducing overall system complexity
Solution Approach 2:
The patent creates a universal analysis platform that handles multiple languages through a common architecture. The LLM serves multiple functions across different languages without requiring separate tool development, eliminating the complexity of maintaining multiple specialized systems while preserving language-specific analysis accuracy through prompt engineering
Data Source
AI summary
A computer-implemented system analyzes program and data flows in a software system comprising code written in multiple programming languages using a generative large language model (LLM) directed by programming-language-specific prompts. The LLM identifies functional components within the code, generating labeled graph nodes that include a node type, a node name, and dependency information. A graph construction computer system processes the labeled graph nodes to generate a directed graph, where nodes represent functional components and directed edges represent dependencies. The system stores the graph in a database and provides a web-based interface for visualization, allowing users to explore, query, and analyze program and data flows across the software system. The system enables automated, language-agnostic dependency mapping, facilitating software analysis, debugging, and modernization.