Tree-Structure Root Cause Analysis for Complex Software Changes
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Solution Overview
Problem
The rapid development and deployment of software in modern businesses, coupled with complex CI/CD pipelines, poses challenges in effectively identifying and mitigating software development issues and cyber threats, leading to increased business risk and employee burnout.
Innovation Solution
A method and system for root cause analysis using tree structure analysis, which compares metadata-based tree structures to identify matching patterns without accessing underlying data, thereby reducing resource consumption and enabling efficient identification of root causes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual management of interactions between resources in software environment is used, then human operators can understand and control the system, but it becomes ineffective as software complexity increases and leads to employee burnout
Solution Approach 1:
The patent introduces an automated root cause analysis system that acts as an intermediary between human operators and complex software environments. The system uses tree structure analysis to automatically compare system states, identify changes, and determine root causes of issues, replacing manual management with automated intelligence while keeping the human operator in the loop for decision-making.
2Productivity
If automated tools are introduced to manage complex software environments, then management efficiency improves, but resource consumption increases
Solution Approach 1:
The patent extracts only the essential metadata information needed for root cause analysis from the complex software environment, rather than processing entire system states. By focusing on specific tree structure metadata (file paths, configuration changes, deployment information), the system achieves efficient comparison with minimal resource consumption while maintaining high productivity.
Solution Approach 2:
The patent segments the software environment into hierarchical tree structures organized by system components, applications, and configurations. This segmentation allows the analysis system to process discrete, manageable units rather than overwhelming monolithic data sets, improving both efficiency and resource utilization.
3Measurement precision
If comprehensive system monitoring is implemented to identify root causes accurately, then measurement precision improves, but the complexity of the analysis system increases
Solution Approach 1:
The patent transforms the root cause analysis problem from a complex multi-dimensional search through system states into a simpler tree structure comparison problem. By representing system configurations as hierarchical trees and comparing their structures, the system achieves high measurement precision through a dimensionality reduction that simplifies the analysis mechanism.
4Reliability
If traditional root cause analysis methods are used that access underlying system data, then comprehensive analysis is possible, but resource consumption and analysis time increase
Solution Approach 1:
The patent performs preliminary actions by continuously monitoring and maintaining tree structure metadata representations of system states before issues occur. When problems arise, the system can immediately compare current tree structures against historical baselines without needing to access or process underlying system data, enabling rapid root cause identification while maintaining analysis completeness.
Data Source
AI summary
A system and method for root cause analysis. A method includes defining tree structures based on metadata of interconnected systems. Each tree structure represents at least a portion of one of the interconnected systems and includes subtrees. Tree structure pairs are identified based on the tree structures. Each tree structure pair includes first and second tree structures. Graph data structures are generated based on the tree structures. Each graph data structure corresponds to one of the tree structures and includes nodes. Each node of each graph data structure represents a respective subtree of the corresponding tree structure. A comparison is performed between graph data structures, where a first graph data structure representing the first tree structure of each tree structure pair is compared to a second graph data structure representing the second tree structure of the tree structure pair in the comparison. Tree structures are matched based on the comparison.