Adaptive Test Stimuli Generation via Closed-Loop Analytics
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Generating test stimuli for complex systems like networks and IoT deployments is laborious and inefficient, often becoming obsolete due to rapid system changes, and existing automation techniques lack connection with analytical systems, making them inadequate for dynamic and real-time adjustments.
Innovation Solution
A closed-loop system that uses analytics data to automatically and adaptively generate test stimuli, integrating machine learning techniques to continuously tune and improve stimuli based on real-time feedback from analytical systems, ensuring dynamic and focused testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual generation of test stimuli is used, then test coverage can be tailored to specific needs, but the process is laborious and time-consuming
Solution Approach 1:
The system performs self-testing by automatically generating test stimuli based on its own operational data and performance metrics. The network device monitors its own responses and uses this feedback to create subsequent test stimuli, eliminating the need for external manual intervention while maintaining comprehensive test coverage.
Solution Approach 2:
The system implements a closed-loop feedback mechanism where analytics data from system responses is continuously fed back into the stimulus generation process. This feedback enables automatic adaptation of test stimuli to current system states, ensuring both comprehensive coverage and reduced generation time through iterative refinement.
2Adaptability or versatility
If test stimuli are generated manually for specific test tasks, then testing can be focused on particular aspects, but the stimuli become obsolete as the system evolves
Solution Approach 1:
The stimulus generation system transitions from static manually-created stimuli to dynamic automated generation. The system continuously adapts test stimuli based on real-time analytics data and evolving system characteristics, ensuring stimuli remain relevant and effective as the network evolves while maintaining focus on specific test objectives.
Solution Approach 2:
The network device autonomously monitors its own evolution and automatically updates test stimuli to match current system states. This self-service capability ensures stimuli remain relevant without requiring external manual updates, maintaining both adaptability to specific test needs and reliability through continuous alignment with the actual system.
3Productivity
If analytical techniques are automated, then analysis efficiency improves, but stimulus generation remains inefficient and laborious
Solution Approach 1:
The system merges the automated analytical processing capabilities with the stimulus generation function into a unified closed-loop system. The analytics engine that efficiently processes system data is directly integrated with the stimulus generation mechanism, allowing both functions to operate automatically and efficiently without the bottleneck of manual stimulus creation.
Solution Approach 2:
The system establishes continuous feedback loops where analytics results automatically inform and drive stimulus generation. The efficient automated analysis of system responses feeds directly into the stimulus creation process, enabling fully automated adaptation of tests based on real-time system performance without manual intervention at either stage.
Data Source
AI summary
Automatic, adaptive stimulus generation includes receiving, at a network device that is associated with a network or system, analytics data that provides an indication of how the network or system is responding to a set of test stimuli introduced into the network or system to facilitate an analysis operation. The network device analyzes the analytics data based on an intended objective for the analysis operation and generates control settings based on the analyzing. The control settings control creation of a subsequent stimulus to be introduced into the network or system during subsequent execution of the analysis operation.