Pattern-Based Configuration Verification From Commit Histories
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Solution Overview
Problem
The challenge of ensuring correct configuration settings in large systems is exacerbated by the increasing number of configuration files and parameters, with few tools available to check for correctness, leading to frequent misconfigurations that cause service disruptions and data breaches.
Innovation Solution
A configuration verification system that uses program synthesis and pattern recognition to learn configuration rules from file and commit histories, employing file-based and history-based rules to automate correctness checks and suggest corrections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the number of configuration files and parameters increases to support service scaling and new features, then service flexibility and scalability are improved, but configuration management complexity and risk of misconfiguration increase
Solution Approach 1:
The system automatically generates configuration rules by analyzing commit histories and detecting patterns in parameter values, eliminating the need for manual rule creation and maintenance. The configuration verification system self-updates its rulebase through automated pattern mining from version control data.
Solution Approach 2:
The system transforms static configuration values into dynamic pattern-based rules by analyzing changes across commit histories. It detects patterns in parameter values and generates rules that adapt to evolving configuration requirements, enabling the system to handle increasing complexity automatically.
2Reliability
If manual configuration verification is performed to ensure correctness, then configuration accuracy is improved, but time consumption and labor requirements increase
Solution Approach 1:
The patent replaces manual configuration verification with an automated system that uses pattern recognition and machine learning algorithms. The system automatically analyzes commit histories, detects patterns in parameter values, and generates verification rules without human intervention, significantly reducing verification time while maintaining high accuracy.
Solution Approach 2:
The system continuously learns from configuration commit histories and updates its pattern recognition models. By analyzing actual configuration changes and their outcomes, the system refines its rules and improves verification accuracy over time, creating a feedback loop that enhances reliability.
3Measurement precision
If configuration rules are manually created and maintained, then verification precision is improved, but device complexity and maintenance effort increase
Solution Approach 1:
The system automatically generates and maintains configuration rules by analyzing commit histories and detecting patterns in parameter values. It self-updates its rulebase through automated pattern mining, eliminating the need for manual rule creation and maintenance while maintaining high verification precision.
Solution Approach 2:
The system transforms static manual rules into dynamic pattern-based rules that automatically adapt to configuration changes. By analyzing patterns in parameter values across commits, it generates rules that maintain precision while reducing maintenance complexity through automated updates.
Data Source
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AI summary
A computer implemented method includes obtaining multiple configuration files that include configuration commit histories, detecting patterns in parameter values in the configuration files to generate file-based rules for configuration parameters, detecting patterns in parameter values in the configuration files to generate history-based rules using commit histories for the configuration parameters, and exposing the rules to calling programs.