Machine Learning Model for Executable Code Refactoring
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Solution Overview
Problem
Complex database management systems with many interconnected parts are difficult to debug and optimize due to the sheer volume of operations performed, making it challenging for even experienced programmers and administrators to identify and fix issues such as incorrect API calls or unbuffered I/O.
Innovation Solution
A method and system that utilize a machine learning model to analyze quantitative and qualitative information related to the execution of application code, generating actionable corrections and code refactorings in real-time, by incorporating a trace into the application code and its runtime environment to collect metrics and events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional debugging tools (print statements, debuggers, profilers, runtime logs) are used to analyze database management system operations, then some insight into system operations is provided, but the tools provide little insight into which components are running unexpectedly and fail to identify the errors responsible for unexpected behavior
Solution Approach 1:
The patent introduces an intermediary machine learning model that acts as a mediator between the complex database management system and the user. This model analyzes execution traces, metrics, and events to identify unexpected behavior and pinpoint specific errors, bridging the gap between raw system data and actionable debugging insights without requiring users to manually analyze overwhelming amounts of data
Solution Approach 2:
The patent replaces manual debugging mechanisms (print statements, debuggers, profilers) with an automated machine learning-based analysis system. This substitution transforms the debugging process from a manual, experience-dependent mechanical process into an automated intelligent system that can identify errors and provide corrective actions without human intervention
2Productivity
If the database management system performs thousands of queries per second with many interconnected parts, then the system achieves high processing capacity, but it becomes difficult to understand the various concepts underlying all the related parts and to debug system anomalies
Solution Approach 1:
The patent extracts the complexity of understanding the database management system by separating the analysis function into an independent machine learning model. This model takes execution traces, metrics, and events as input and outputs simplified interpretations of system behavior, effectively extracting the difficult-to-understand intermediate concepts from the high-speed query processing system
Solution Approach 2:
The machine learning model serves as an intermediary layer between the complex high-performance database system and the user. It translates the complex interactions of thousands of queries per second into understandable patterns and identifies specific components running unexpectedly, making the complex system comprehensible without reducing its processing capacity
3Reliability
If experienced programmers and system administrators use training and experience to debug system anomalies, then some issues can be identified, but obscure system issues such as unbuffered input/output remain difficult to debug even for the most experienced professionals
Solution Approach 1:
The patent replaces human debugging expertise with an automated machine learning system that specializes in detecting obscure issues like unbuffered I/O. This substitution eliminates the limitations of human experience and training, providing consistent detection capability for all types of errors including those that are traditionally difficult to detect even by experienced professionals
4Manufacturing precision
If manual code analysis and refactoring are performed to improve application performance, then code can be optimized, but it is time-consuming and difficult to improve code in ways that make it faster, smaller and more resourceful
Solution Approach 1:
The patent replaces manual code analysis and refactoring with an automated machine learning system. This system analyzes execution traces and metrics to automatically generate optimized code refactoring suggestions, eliminating the time-consuming manual process while maintaining or improving the quality of optimization. The system can identify performance bottlenecks and suggest refactoring actions without human intervention
Solution Approach 2:
The machine learning model performs preliminary analysis of code execution patterns before refactoring is needed. By continuously monitoring and learning from execution traces, the system prepares optimization suggestions in advance, so when performance improvement is needed, the refactoring actions are already identified and ready to be applied, reducing the overall time required
Data Source
AI summary
A method, system, and computer-readable storage medium for providing actionable corrections to, and code refactoring of, executable code. The method comprising: utilizing input to generate a machine learning model that analyzes quantitative information and qualitative information of an execution of an application code; executing the application code within a runtime environment of the application code, either of which incorporates a trace; during the executing, utilizing the trace to collect, at least one metric and/or at least one event; during the executing, ascertaining, by the machine learning model, the quantitative information and the qualitative information, by analyzing the at least one metric and/or the at least one event; and determining, by the machine learning model, at least one correction to the application code and/or at least one refactoring of the application code, by analyzing the quantitative information and the qualitative information.