AI Refactoring Models for Legacy Code Performance Bottlenecks
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Solution Overview
Problem
Legacy software often contains inefficient and outdated code that is difficult to maintain, inefficiently utilizes hardware resources, and may not be compatible with modern systems, leading to performance issues and security vulnerabilities.
Innovation Solution
An automated profiling and refactoring (APR) system that includes an automated runtime performance profiler and a source code analysis tool to identify inefficient functions and code violations, providing automated refactoring options to optimize and modernize legacy software.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual optimization and refactoring of legacy software is performed, then code quality and performance are improved, but the process becomes expensive and time-consuming due to the large code base and need for original authors' expertise
Solution Approach 1:
The patent replaces manual mechanical refactoring processes with automated AI-based systems. The AI model automatically analyzes legacy code, identifies inefficiencies, and generates refactoring options without requiring human developers to manually examine millions of lines of code, thus resolving the contradiction between improving code quality and reducing time loss.
Solution Approach 2:
The AI-powered refactoring system enables the software to self-diagnose and self-improve by automatically identifying code violations and generating optimization suggestions. This self-service capability eliminates the need for expensive manual intervention while maintaining continuous improvement of code quality.
2Measurement precision
If manual refactoring by original authors is used, then accurate understanding of original intent is achieved, but the process becomes expensive due to difficulty in finding such expertise
Solution Approach 1:
The patent introduces an AI model as an intermediary between the legacy code and the refactoring process. This AI intermediary learns from the existing codebase and documentation to understand original intent, replacing the need for original authors while maintaining accurate comprehension of the code's purpose and reducing refactoring costs.
Solution Approach 2:
The system creates a digital copy or representation of the original code's intent and structure through AI analysis, allowing refactoring to proceed based on this copied understanding rather than requiring the original human creators, thus reducing costs while preserving intent accuracy.
3Stability of the object's composition
If legacy software is maintained as-is, then existing functionality is preserved, but the software becomes inefficient and incompatible with modern systems
Solution Approach 1:
The patent implements dynamic refactoring where the AI system continuously monitors and adapts the legacy code to modern standards while preserving core functionality. This dynamic approach allows the software to evolve and become compatible with modern systems without completely rewriting the existing codebase, thus maintaining stability while improving adaptability.
Solution Approach 2:
The AI-powered system makes targeted parameter changes to the legacy code, modifying specific aspects such as coding standards, data structures, and interfaces to improve compatibility with modern systems while preserving the fundamental functionality and behavior of the original software.
4Measurement precision
If comprehensive manual analysis of large code base is performed, then all code violations are identified, but the process becomes prohibitively expensive and time-consuming
Solution Approach 1:
The patent replaces complex manual analysis mechanisms with automated AI-based analysis systems. The AI model can process millions of lines of code efficiently, identifying violations that would be impossible to detect manually, thus improving detection precision while reducing the complexity burden on human analysts.
Solution Approach 2:
The AI analysis system serves multiple functions simultaneously: it detects various types of code violations, understands code intent, generates refactoring suggestions, and prioritizes issues. This multi-functional capability achieves comprehensive violation detection without requiring separate complex processes for each analysis task.
Data Source
AI summary
Disclosed herein are embodiments of systems, methods, and products for modernizing and optimizing legacy software. A computing device may perform an automated runtime performance profiling process. The performance profiler may automatically profile the legacy software at runtime, monitor the memory usage and module activities of the legacy software, and pinpoint/identify a subset of inefficient functions in the legacy software that scale poorly or otherwise inefficient. The computing device may further perform a source code analysis and refactoring process. The computing device may parse the source code of the subset of inefficient functions and identify code violations within the source code. The computing device may provide one or more refactoring options to optimize the source code. Each refactoring option may comprise a change to the source code configured to correct the code violations. The computing device may refactor the source code based on a selected refactoring option.