AI Code Gatekeeper for Repository Compliance Screening
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Solution Overview
Problem
Large enterprises face challenges in managing code repositories due to unlicensed code, inadvertent violations of company code standards and policies, and unintentional infections of code with violations, which can lead to compliance issues.
Innovation Solution
An apparatus utilizing a processor system configured with large language models (LLMs) to analyze code for compliance with licensing and policy rules, providing indications of compliance or non-compliance, and suggesting alternative solutions for non-compliant code, which can be automatically implemented or manually applied.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual code review processes are used to ensure compliance with licensing and policy standards, then code quality and compliance can be verified, but the process becomes time-consuming and inefficient
Solution Approach 1:
The patent replaces manual mechanical code review processes with an automated AI-based system that uses machine learning models to analyze code compliance. The processor system automatically inputs code to LLMs, receives compliance indications, and either commits compliant code or generates alternative solutions for non-compliant code, eliminating the need for time-consuming human review while maintaining compliance verification reliability
Solution Approach 2:
The system enables self-service compliance checking where the AI gatekeeper automatically analyzes code submissions, determines compliance status, and even generates corrected code versions. The processor system autonomously handles the entire compliance verification workflow without requiring manual intervention, allowing the code repository system to self-regulate compliance issues
2Productivity
If automated AI-based compliance checking is implemented, then code review efficiency is improved, but system complexity increases due to integration of multiple LLMs and processing layers
Solution Approach 1:
The patent segments the compliance checking system into distinct functional modules: a gatekeeper LLM that receives code and determines compliance status, and a coder LLM that generates alternative solutions for non-compliant code. This segmentation allows each component to specialize in specific tasks, improving overall efficiency while organizing complexity into manageable, independent units that can be maintained and updated separately
3Reliability
If multiple LLMs are used to provide both compliance checking and alternative solutions, then the system can handle non-compliant code more effectively, but processing time and computational resources increase
Solution Approach 1:
The system applies partial action by only invoking the coder LLM when non-compliance is detected, rather than running both models for all code submissions. The gatekeeper LLM performs compliance checking first, and only if violations are found does the system proceed to generate alternative solutions. This conditional approach ensures reliable compliance enforcement while minimizing unnecessary processing time for already-compliant code
Data Source
AI summary
Techniques are provided for configuring artificial intelligence (AI) components to prevent inadvertent commits of unlicensed code, prevent inadvertent violations of company code standards, policies, and licenses, to prevent inadvertent violations of code/library/dataset use, and to suggest alternative solutions for noted violations.