Machine Learning Test Environment Reconfiguration

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Solution Overview

Problem

Performance testing of software applications is a time-consuming and error-prone process that requires manual configuration of test environments and data, often leading to the use of unnecessary computational resources and inefficient identification of optimal configurations for production deployments.

Innovation Solution

A system that automatically reconfigures test environments and test data using machine-learning algorithms to optimize performance tests, iteratively adjusting settings until specified performance requirements are met, while also monitoring and optimizing infrastructure usage.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual configuration of test environments and data is used for performance testing, then the testing process can be completed, but the time consumption and error rate increase significantly

Engineering Contradiction:

Improveaccuracy of performance testingVSAvoidtime for configuring test environments

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system enables self-service automation where the performance testing system automatically configures test environments, generates test data, and adjusts configurations based on feedback from test results. The system serves itself by using machine learning algorithms to autonomously determine optimal configurations without requiring manual intervention for each testing iteration.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical configuration processes with automated computational systems. Machine learning algorithms substitute for human operators in configuring test environments, generating test data, and analyzing results, thereby eliminating the time-consuming and error-prone manual configuration process while maintaining or improving testing accuracy.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Adaptability or versatility

If multiple test environments with different infrastructures are created manually, then comprehensive performance testing can be performed, but computational resources are wasted on unnecessary configurations

Engineering Contradiction:

Improvecoverage of test configurationsVSAvoidcomputational resources used

Core Design Contradiction:

Adaptability or versatilityVSLoss of energy

Solution Approach 1:

The system implements feedback loops where test results are automatically analyzed by machine learning algorithms to determine which configurations are effective and which are unnecessary. Based on this feedback, the system dynamically adjusts the test environment configurations, eliminating redundant infrastructures while maintaining comprehensive coverage of relevant test scenarios.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent dynamically changes configuration parameters of test environments based on machine learning insights. Instead of maintaining multiple static infrastructures, the system adjusts parameters such as infrastructure type, test data characteristics, and configuration settings according to what the machine learning model determines are optimal for achieving comprehensive test coverage with minimal resource consumption.

Inventive Principle:

Principle #35Parameter changes

3Productivity

If manual work is used to spin up test environments and create test data, then the testing process can proceed, but the overall productivity decreases

Engineering Contradiction:

Improvespeed of performance testingVSAvoidmanual effort required

Core Design Contradiction:

ProductivityVSEase of operation

Solution Approach 1:

The system performs preliminary actions by pre-configuring test environments and generating test data automatically before testing begins. Machine learning algorithms predict required configurations and prepare test environments in advance, eliminating the need for manual spinning up of environments and creation of test data during the testing process, thereby significantly improving productivity.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS11036576B2Automatically reconfiguring a performance test environment

Publication Date: 2021.06.15 CAPITAL ONE SERVICES LLC

AI summary

Described herein is a system for automatically reconfiguring a test environment. As described above, performance testing can be a time-consuming and error-prone resulting in the use of unnecessary computational resources. The system may use machine-learning to determine whether the test environment, test data, and/or test script is to be reconfigured to optimize the performance test. The system may iteratively reconfigure the test environment, test data, and/or test script, and re-execute the performance test, until an optimal performance test of an application is executed based on a specified performance requirement.