Code Patch Ranking Model for Manageable AI Fix Options
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Solution Overview
Problem
Existing generative AI systems struggle to balance generating numerous code alternatives while providing a manageable set of high-quality options for software patching, overwhelming users with choices.
Innovation Solution
A generative AI-driven code fixing pipeline using a transformer-based LLM is fine-tuned with a training dataset of prompts and reference code pairs, integrated with a code cybersecurity scanner, prompt generator, and refiner to rank and refine candidate patches based on predicted quality measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a generative AI system generates numerous code alternatives, then the quantity of code options increases, but the user becomes overwhelmed with choices and cannot manage them effectively
Solution Approach 1:
The patent introduces an intermediary ranking model that acts as a mediator between the generative AI system and the user. This model automatically evaluates and ranks the generated code alternatives based on multiple criteria (quality metrics, security scan results, code review feedback), presenting only the top-ranked options to users. This intermediary layer filters the large number of generated alternatives into a manageable subset, resolving the contradiction between providing quantity and ensuring ease of operation.
2Adaptability or versatility
If the system generates and presents multiple code modifications to users, then the variety of options increases, but the time required for users to review and select patches increases
Solution Approach 1:
The patent applies preliminary action by having the ranking model evaluate and prioritize code alternatives before presenting them to users. The system pre-ranks all generated code modifications based on quality metrics, security assessments, and other criteria, so that when users receive the options, they are already ordered by expected effectiveness. This preliminary ranking process reduces the time users need to spend reviewing alternatives, as they can focus on the pre-sorted top options rather than evaluating all alternatives equally.
3Reliability
If the system provides comprehensive code analysis and ranking, then the quality of selected patches improves, but the computational complexity and processing time increase
Solution Approach 1:
The patent segments the comprehensive code analysis process into multiple independent components: quality metric evaluation, security scanning, code review feedback, and ranking model processing. Each component handles a specific aspect of the analysis independently, allowing parallel processing and modular implementation. This segmentation reduces the apparent computational complexity by breaking down the monolithic analysis task into manageable, specialized sub-tasks that can be executed concurrently and combined through the ranking model.
Data Source
AI summary
A generative AI based pipeline has been created that ranks generated responses that are candidate software patches. The ranking is based on predicted quality measures of code fragments within a corresponding prompt to a generated AI model. The predicted quality measures are generated by a machine learning model that has been trained based on features that are values/measures of similarity metrics between code fragments, between code fragment changes, between code structures, and/or between changes of code structures.