AI Code Replacement Framework for Pre-Production Performance Bottlenecks
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Solution Overview
Problem
The back-and-forth process between development and pre-production environments to rectify performance deficiencies in software is tedious and time-consuming, limited by developer knowledge, and consumes system resources.
Innovation Solution
A code performance optimizer framework uses Generative Artificial Intelligence (GenAI) to generate code replacements in the pre-production environment, comparing performance with original code to identify and implement optimized code directly in the pre-production environment, minimizing iterations between environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If software is tested and refined through multiple iterations between development and pre-production environments, then performance quality is improved, but time consumption and system resource usage increase
Solution Approach 1:
The system performs preliminary performance testing and code optimization directly in the pre-production environment using AI models before full deployment. This preliminary action identifies and fixes performance issues early, reducing the need for multiple back-and-forth iterations between development and pre-production environments, thereby saving time while maintaining performance quality.
Solution Approach 2:
The patent replaces the manual mechanical process of developers repeatedly transferring and testing code between environments with an automated AI-driven system. The AI model automatically analyzes performance data, generates optimized code, and implements improvements, substituting the tedious manual iteration process with intelligent automation that achieves the same performance refinement goals more efficiently.
2Manufacturing precision
If manual code optimization is performed through multiple environment iterations, then performance issues are resolved, but developer workload and complexity increase
Solution Approach 1:
The system enables self-service code optimization by deploying an AI model in the pre-production environment that autonomously analyzes performance bottlenecks, generates optimized code alternatives, and implements improvements without requiring continuous developer intervention. The AI model serves itself by automatically iterating through optimization cycles, reducing both developer workload and the perceived complexity of the optimization process.
Solution Approach 2:
The patent introduces an AI model as an intermediary between the pre-production environment and the development environment. This intermediary automatically handles performance analysis and code optimization tasks, acting as a mediator that translates performance issues into optimized code solutions without requiring direct developer involvement in each iteration, thereby simplifying the overall process.
3Manufacturing precision
If code is frequently transferred between development and pre-production environments for testing, then performance deficiencies are addressed, but network bandwidth and processing resources are consumed
Solution Approach 1:
The system extracts the core performance testing and optimization functionality directly into the pre-production environment by deploying an AI model there. This extraction eliminates the need to frequently transfer code back and forth between environments, as the AI model performs testing and optimization locally in pre-production. Only the final optimized code needs to be transferred to development, significantly reducing network bandwidth consumption and system resource usage while maintaining performance validation effectiveness.
Data Source
AI summary
According to some embodiments, systems and methods are provided including receiving a block of code including a code candidate; inserting at least one break point in the block; executing a debugger tool with the code candidate, wherein the debugger tool is included in a pre-production environment; generating a code replacement for the code candidate; replacing the code candidate with the code replacement; automatically embedding a code replacement script in the code block with the code replacement; executing the code block including the code replacement script in the debugger tool, wherein execution of the code block outputs at least a code replacement trace result; comparing the code replacement trace result to a code candidate trace result; and based on the comparison, transmitting the code block including the code replacement to a development environment. Numerous other aspects are provided.