ML Anomaly Detection for DevOps Testing Efficiency
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Solution Overview
Problem
Current DevOps methodologies face challenges in detecting implicit and explicit anomalies during software testing, as traditional test automation focuses on predefined expected results, missing hidden issues that may only occur in production environments.
Innovation Solution
A computer-implemented method using machine learning implicit-anomaly and explicit-anomaly models, combined with artificial intelligence, to analyze system metrics and text data from test cases, identifying potential incidents and initiating corrective actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional test automation is used to focus on predefined expected results, then testing efficiency is improved, but anomaly detection capability deteriorates
Solution Approach 1:
The patent replaces traditional mechanical test automation with machine learning-based anomaly detection. Instead of relying on predefined test cases and expected results, the system uses ML models to automatically detect anomalies in system behavior, substituting the mechanical testing process with intelligent pattern recognition and statistical analysis to identify both explicit and implicit anomalies.
Solution Approach 2:
The system enables self-service anomaly detection by automatically monitoring system metrics and generating test cases without human intervention. The machine learning models continuously learn from system behavior patterns and autonomously identify anomalies, allowing the system to self-diagnose and self-monitor without requiring manual test case design or execution.
2Reliability
If machine learning models are deployed to detect implicit anomalies, then anomaly detection capability is improved, but system complexity increases
Solution Approach 1:
The patent segments the anomaly detection system into distinct components: explicit anomaly detection module, implicit anomaly detection module, and test case generation module. Each module handles specific aspects of anomaly detection independently, allowing the complex system to be divided into manageable segments that can be developed, trained, and maintained separately while working together to provide comprehensive anomaly detection.
Data Source
AI summary
Processing within a computing environment is facilitated by using a machine learning implicit-anomaly model to determine a possibility of an implicit anomaly within a system based on system metrics data obtained during running of one or more test cases on the system. The process further includes determining, using artificial intelligence, occurrence of an incident within the system associated with running of the one or more test cases. Determining the occurrence of the incident uses the determined possibility of the implicit anomaly within the system based on the system metrics data, and the process further includes initiating an action based on the occurrence of the incident within the system with running of the one or more test cases.