Generative Code Refactoring for Legacy Runtime Bottlenecks
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Solution Overview
Problem
Legacy software often exhibits poor code quality, inefficiency, and incompatibility with modern hardware and software architectures, making it difficult and costly to maintain and modernize, with manual refactoring requiring expertise and being prone to errors.
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 and assistance in transitioning to new languages and architectures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual refactoring is performed by original authors, then code quality improves, but cost and time increase significantly
Solution Approach 1:
The system enables self-service automated refactoring where the refactoring tool autonomously analyzes legacy code, identifies issues, and applies corrections without requiring original authors or expert developers. The system serves itself by using AI models to perform refactoring tasks that would traditionally require human expertise.
Solution Approach 2:
The patent replaces manual mechanical refactoring processes with automated AI-based systems. Instead of relying on human developers to manually review and refactor code, the system uses machine learning models and automated analysis tools to detect code quality issues and apply refactoring transformations automatically.
2Ease of manufacture
If legacy code is maintained as-is, then development cost is reduced, but code readability and maintainability deteriorate
Solution Approach 1:
The system performs preliminary automated refactoring actions on legacy code before new development work begins. By proactively identifying and correcting code quality issues, outdated patterns, and maintenance problems in advance, the system prepares the codebase for easier future maintenance without incurring continuous refactoring costs.
Solution Approach 2:
The system changes key parameters of legacy code including updating programming languages, modernizing code structures, improving naming conventions, and refactoring outdated patterns. These parameter changes enhance code readability and maintainability while controlling the overall cost through automated processes.
3Measurement precision
If comprehensive manual analysis is performed, then code violations are accurately identified, but time and resource consumption increase
Solution Approach 1:
The patent replaces manual code analysis with automated AI-based analysis systems. Machine learning models and static analysis tools automatically scan legacy code to detect violations, outdated patterns, and quality issues with high accuracy, eliminating the need for time-consuming manual review by developers.
Solution Approach 2:
The system introduces an intermediary automated analysis layer between the legacy code and human developers. This intermediary uses AI models and analysis tools to pre-process the code, identify issues, and present findings to developers, thereby filtering and prioritizing problems before human review and reducing overall analysis time.
Data Source
AI summary
Presented herein are systems and methods for optimizing legacy source code using generative models. A computing system may generate, for each function of a plurality of functions of a software program, a performance profile comprising a performance attribute during runtime. The computing system may identify, from the plurality of functions of the software program, a function based on the performance attribute of the performance profile for the function being lower than a threshold. The computing system may select first source code of the function for refactoring, responsive to identifying the function. The computing system may provide the first source code of the function to a generative model to determine refactoring opportunities within the first source code. The computing system may generate, based on providing the first source code to the generative model, second source code for the function corresponding to the refactoring opportunities within the first source code.