Impact-Based User Flow Testing for Prioritized Incident Handling
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Solution Overview
Problem
Existing IT service management systems struggle to effectively prioritize incident handling and monitoring based on the impact of user flows, leading to inefficient resource allocation and delayed issue resolution.
Innovation Solution
A method that tracks and analyzes user interactions to identify impactful user flows, assigns impact metrics, and prioritizes synthetic tests to simulate user interactions, optimizing monitoring and remediation efforts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all user flows are monitored and tested with synthetic tests, then system reliability is improved, but resource consumption and system complexity increase significantly
Solution Approach 1:
The patent applies local quality by differentiating monitoring intensity across different user flows based on their business impact. Critical user flows receive intensive monitoring and synthetic testing, while less important flows receive minimal or no synthetic testing. This selective approach maintains system reliability for critical functions while reducing overall system complexity and resource consumption.
Solution Approach 2:
The patent changes the parameter of monitoring intensity from a uniform state to a variable state based on business impact metrics. By dynamically adjusting the level of synthetic testing and monitoring resources allocated to different user flows according to their measured business impact, the system optimizes the balance between reliability and complexity.
2Measurement precision
If comprehensive synthetic testing is performed on all user flows, then incident detection capability is improved, but productivity and response time deteriorate due to resource constraints
Solution Approach 1:
The patent implements local quality by concentrating incident detection resources on critical user flows that have the highest business impact. Synthetic tests are prioritized for these critical flows, ensuring high incident detection capability where it matters most, while reducing or eliminating testing for less critical flows to maintain productivity.
Solution Approach 2:
The patent extracts and removes synthetic testing from non-critical user flows, focusing testing resources only on critical flows. This extraction approach maintains high incident detection capability for important functions while eliminating wasteful testing that would reduce overall productivity.
3Measurement precision
If impact metrics are assigned to all user flows, then incident prioritization accuracy is improved, but computational overhead and system complexity increase
Solution Approach 1:
The patent applies local quality by calculating and assigning impact metrics only to critical user flows rather than all user flows. This selective metric assignment maintains high incident prioritization accuracy for critical incidents while reducing computational overhead by avoiding calculations for less important flows.
4Reliability
If more synthetic tests are executed, then coverage of user flows is improved, but system performance and operational efficiency decrease
Solution Approach 1:
The patent implements local quality by providing comprehensive synthetic test coverage only for critical user flows with high business impact, while reducing or eliminating testing for less critical flows. This approach maintains high reliability for important functions while preserving operational efficiency by avoiding excessive testing overhead.
Data Source
AI summary
Prioritized flow testing and incident handling includes tracking computerized interactions of users interacting with system(s), identifying user flows, each being a sequence of one or more of the tracked computerized interactions, determining a collection of user flows of the user flows, each being associated with a measured beneficial impact, assigning impact metrics to the collection of user flows, each being assigned an impact metric commensurate with the measured beneficial impact associated with that user flow, and based on the assigned impact metrics, selecting synthetic tests of common flows represented by the collection of user flows and a prioritization of the synthetic tests, each synthetic test simulating a sequence of user interactions to progress through a common flow of the common flows.