Legacy Source Code Runtime Profiling for Inefficient Functions
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Solution Overview
Problem
Legacy software often contains inefficient and outdated code that is difficult to maintain and optimize, leading to poor performance and security vulnerabilities, due to outdated programming techniques, inefficient algorithms, and lack of compatibility with modern hardware and software architectures.
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 improve code readability and transition to modern architectures.
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 an automated computer-implemented system. The system uses machine learning models trained on code metrics to automatically identify, prioritize, and apply refactoring opportunities, substituting human expert labor with automated computational analysis and decision-making.
Solution Approach 2:
The system enables the legacy code base to self-diagnose and self-improve through automated analysis. By training machine learning models on the specific code base's metrics and patterns, the system allows the code to identify its own deficiencies and apply appropriate refactoring without external human intervention for each issue.
2Reliability
If the entire legacy code base is analyzed and refactored, then comprehensive performance improvement is achieved, but the complexity and cost of the process increases significantly
Solution Approach 1:
The patent divides the large legacy code base into smaller manageable units (functions, methods, classes) and analyzes them individually using code metrics. The machine learning model processes these segmented units separately, identifying refactoring opportunities in each, which can then be prioritized and implemented in manageable batches rather than attempting to refactor the entire code base simultaneously.
Solution Approach 2:
The system changes the parameters of analysis by using trained machine learning models that evaluate code based on specific metrics (cyclomatic complexity, lines of code, nesting depth, etc.). These parameter-based evaluations allow the system to objectively identify and prioritize refactoring opportunities based on quantifiable measures rather than subjective assessment.
3Productivity
If automated tools are used to refactor legacy code, then the process becomes faster and less expensive, but the ability to understand original code intent and apply appropriate refactoring may be reduced
Solution Approach 1:
The system performs preliminary training actions by collecting code metrics from the legacy code base and training machine learning models on this specific code base before deployment. This preliminary training phase allows the model to learn the specific patterns, conventions, and intent indicators of the target code base, ensuring that subsequent automated refactoring decisions are context-aware and appropriate.
Solution Approach 2:
The system incorporates feedback mechanisms where the machine learning model continuously learns from the code metrics and refactoring outcomes. By analyzing the results of refactoring applications and their impact on code quality metrics, the system refines its understanding of what constitutes appropriate refactoring for this specific code base, improving its ability to preserve original intent while achieving performance improvements.
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.