Automated Error Triage Scoring via Deployment Time Correlation
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Solution Overview
Problem
Existing computer maintenance systems face challenges in efficiently prioritizing error reports due to their first-in-first-out approach, which does not adequately manage different levels of importance, impact, or risk associated with hardware or software problems within computing systems.
Innovation Solution
The system automatically manages error reports by grouping them based on root causes and assigning a severity score that inversely correlates with the time since deployment, allowing for ranking and prioritization of error reports, enabling better resource allocation and quicker resolution of critical issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If error reports are processed on a first-in-first-out basis, then processing simplicity is maintained, but error prioritization based on importance and impact is inadequate
Solution Approach 1:
The system changes the parameter of error report processing from simple chronological ordering to a multi-parameter severity scoring system that considers deployment time, error type, and impact metrics. This allows errors to be prioritized based on multiple factors rather than just arrival time, resolving the contradiction between processing simplicity and prioritization accuracy.
Solution Approach 2:
The system segments error reports into different severity levels (critical, high, medium, low) based on calculated severity scores. This segmentation allows support personnel to focus on high-severity errors first while maintaining a structured approach to processing all errors, balancing simplicity with effective prioritization.
2Device complexity
If all error reports are treated equally without prioritization, then resource allocation is straightforward, but critical issues may not be addressed promptly
Solution Approach 1:
The system performs preliminary action by automatically calculating severity scores and assigning priority levels to error reports before they reach support personnel. This preliminary triage ensures that critical issues are identified and prioritized in advance, allowing resources to be allocated efficiently and critical problems to be addressed promptly without increasing overall system complexity.
3Productivity
If error reports are grouped by root cause, then analysis efficiency is improved, but the complexity of determining root causes increases
Solution Approach 1:
The system implements self-service by automatically analyzing error reports, determining root causes, and grouping errors accordingly without requiring manual intervention. The automated root cause determination uses algorithms to analyze error patterns and classify them, improving analysis efficiency while keeping the complexity management automated rather than transferring it to human operators.
Data Source
AI summary
A computing system automatically manages error reports. Each error report specifies an error that occurred within a subsystem of the computing system. A received error report is added into a root cause grouping. Each root cause grouping contains error reports having error types traceable to a same root cause. A deployment time at which the subsystem corresponding to the error report was deployed within the computing system is determined. A severity score for the root cause grouping is generated as a function of the deployment time. The severity score inversely correlates to a time period length between the deployment time and the occurrence time of the error. The root cause grouping is assigned to a ranked error container of a plurality of ranked error containers based on the generated severity score. Each ranked error container contains root cause groupings having severity scores within a specified score range.