Generative AI Code Repair for Self-Healing Software Updates
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Solution Overview
Problem
The complexity and frequency of updates in modern software applications pose challenges for efficient development and maintenance, particularly in ensuring compatibility and fixing bugs, which current systems struggle to address effectively.
Innovation Solution
A self-healing service that utilizes a Large Language Model (LLM) to generate corrected code based on detected errors, iteratively rebuilding and executing applications until error-free, then automatically merging the corrected code into the repository.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual code review and debugging processes are used to ensure code quality, then code accuracy is improved, but development time and system downtime increase
Solution Approach 1:
The system implements automated code repair where the codebase serves itself by detecting errors and generating corrections without human intervention. The self-healing service automatically identifies bugs, generates repair code using LLMs, and applies fixes, enabling the software to maintain itself autonomously and reducing dependency on manual code review processes
Solution Approach 2:
The patent replaces manual mechanical code review processes with an automated intelligent system. Human developers no longer need to manually review and debug code; instead, an AI-based system with LLMs performs error detection, analysis, and repair generation, substituting the mechanical human review process with an automated intelligent workflow that operates continuously and efficiently
2Productivity
If frequent updates are implemented to fix bugs and add features, then software functionality is improved, but system stability and compatibility worsen
Solution Approach 1:
The system implements continuous feedback loops where code changes are automatically tested, error patterns are monitored, and corrections are validated before deployment. The self-healing service continuously monitors the codebase, detects emerging errors, and applies repairs in real-time, creating a feedback mechanism that maintains system stability even during frequent updates by immediately addressing compatibility issues as they arise
Solution Approach 2:
The system performs preliminary error detection and correction before issues manifest in production. By continuously analyzing code changes and generating repairs in advance, the system prevents compatibility problems and stability issues from occurring, allowing frequent updates to be implemented safely with pre-validated corrections in place
3Adaptability or versatility
If complex codebases with millions of lines are maintained by multiple developers, then software functionality is improved, but code consistency and error rates worsen
Solution Approach 1:
The self-healing service acts as an intermediary layer between multiple developers and the codebase. Instead of developers directly modifying code and risking inconsistencies, the automated service mediates all changes by detecting errors, generating consistent repairs, and applying standardized corrections across the entire codebase, ensuring uniformity regardless of how many developers are working on the project
Solution Approach 2:
The system applies uniform error detection and repair standards across the entire codebase regardless of which developer made the change. By using consistent LLM-based analysis and standardized repair generation, the system ensures homogeneous code quality and consistency throughout millions of lines of code, eliminating variations that typically arise from multiple developers using different coding styles and practices
Data Source
AI summary
Systems and methods include detection of a new version of software code in a version control system and, in response to the detection of the new version of software code, automatically build an application from the new version of software code, execute the application, detect an error in the execution of the application, in response to detection of the error, instruct a text generation model to generate corrected software code based on the error and the new version of software code, build a second version of the application from the corrected software code, execute the second version of the application, and, based on the execution of the second version of the application, save the corrected software code to the version control system.