ML-Based Test Configuration Selection for System Testing

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Solution Overview

Problem

Conventional testing methods for product configurations are resource-intensive and error-prone, leading to inefficiencies and reduced product quality due to manual selection by subject matter experts.

Innovation Solution

Utilize machine learning techniques to automatically determine optimal product configurations by collecting, filtering, and processing configuration information from multiple data sources, employing clustering algorithms to group similar configurations, and generating prioritized lists for testing.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual configuration selection by subject matter experts is used, then configuration expertise can be applied, but the process becomes resource-intensive and error-prone

Engineering Contradiction:

Improveconfiguration selection accuracyVSAvoidtesting efficiency

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system performs self-service by automatically selecting test configurations using machine learning algorithms. The ML model independently analyzes configuration data, identifies patterns, and selects optimal configurations without human intervention, thereby eliminating manual errors while maintaining high efficiency

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical human decision-making process with an automated machine learning system. The ML model substitutes subject matter experts by processing configuration data algorithmically, removing human limitations such as fatigue and bias while improving both reliability and productivity

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If the number of test configurations is increased to cover more scenarios, then testing completeness improves, but resource consumption increases

Engineering Contradiction:

Improvetesting completenessVSAvoidtesting resource consumption

Core Design Contradiction:

ReliabilityVSLoss of energy

Solution Approach 1:

The system applies partial action by selecting only the most critical and representative configurations for testing. The ML model identifies and prioritizes configurations that provide maximum testing value, avoiding unnecessary tests while maintaining comprehensive coverage of essential scenarios

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The patent changes the parameter of configuration selection from exhaustive enumeration to intelligent sampling. By transforming how configurations are selected (using ML-based prioritization), the system achieves high testing completeness with reduced resource consumption

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS12499023B2Determining configurations to be used in system testing processes using machine learning techniques

Publication Date: 2025.12.16 DELL PROD LP

AI summary

Methods, apparatus, and processor-readable storage media for determining configurations to be used in system testing processes using machine learning techniques are provided herein. An example computer-implemented method includes obtaining, from multiple data sources, configuration information associated with at least one system; filtering out a subset of the configuration information based at least in part on at least one user request related to testing of at least a portion of the at least one system; determining at least a portion of the subset of the configuration information to be used in the testing of the at least a portion of the at least one system by processing the subset of the configuration information using one or more machine learning techniques; and performing one or more automated actions based on the determined at least a portion of the subset of the configuration information to be used in the testing.