AI Test Optimization for Product Release Sequencing

Solution Overview

Problem

Current product release processes require extensive testing to ensure product quality and reliability, which can be resource-intensive and time-consuming, posing challenges for developers to achieve frequent releases without significant resource consumption.

Innovation Solution

A method utilizing machine learning models to determine an optimal product deployment sequence by predicting probabilistic times for testing features, incorporating historical deployment data, impact scores, and buffer times, thereby optimizing testing efficiency and resource allocation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If extensive testing is performed to ensure product quality and reliability, then product reliability is improved, but resource consumption and time required for testing increase

Engineering Contradiction:

Improveproduct reliabilityVSAvoidresource consumption

Core Design Contradiction:

ReliabilityVSLoss of energy

Solution Approach 1:

The system performs preliminary actions by predicting probabilistic testing times and determining optimal deployment sequences before actual testing begins. ML models analyze historical data to pre-calculate required testing durations and resource allocations, enabling proactive resource planning rather than reactive testing adjustments

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent changes key parameters including probabilistic time predictions, impact scores, and buffer times to optimize the testing process. By dynamically adjusting these parameters based on ML model outputs and historical deployment patterns, the system achieves reliable testing with reduced resource consumption

Inventive Principle:

Principle #35Parameter changes

2Reliability

If extensive testing is performed to ensure product quality and reliability, then product reliability is improved, but testing time increases

Engineering Contradiction:

Improveproduct reliabilityVSAvoidtesting time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary actions by predicting probabilistic testing times and determining optimal deployment sequences before actual testing begins. ML models analyze historical data to pre-calculate required testing durations and resource allocations, enabling proactive resource planning rather than reactive testing adjustments

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent incorporates feedback mechanisms where ML models continuously learn from historical deployment data and testing outcomes. This feedback loop enables the system to refine its time predictions and optimize testing sequences, progressively reducing testing time while maintaining reliability standards

Inventive Principle:

Principle #23Feedback

3Productivity

If frequent product releases are made to ensure developer and customer success, then productivity is improved, but testing resource consumption increases

Engineering Contradiction:

Improverelease frequencyVSAvoidtesting resource consumption

Core Design Contradiction:

ProductivityVSLoss of energy

Solution Approach 1:

The system performs preliminary actions by predicting probabilistic testing times and determining optimal deployment sequences before actual testing begins. ML models analyze historical data to pre-calculate required testing durations and resource allocations, enabling proactive resource planning rather than reactive testing adjustments

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent applies partial action by determining optimal numbers of test cases to execute based on impact scores and probabilistic time predictions. Rather than always executing all possible tests, the system selectively performs the minimum necessary testing required to maintain quality standards, reducing overall resource consumption while enabling frequent releases

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS20240386337A1Artificial intelligence (AI)-powered test optimization system and methodology

Publication Date: 2024.11.21 DELL PROD LP

AI summary

An example methodology includes, by a computing device, determining a plurality of products associated with a product release, the products including a product that is being released and one or more interlocks linked to the product release. The method also includes, by the computing device, determining a number of features that are to be deployed for each product of the plurality of products and determining a testing that is to be performed for each product of the plurality of products. The method also includes, by the computing device, determining using one or more machine learning (ML) models, a probabilistic time to test the features that are to be deployed for each product of the plurality of products and generating the optimal product deployment sequence for the product release based on a release sequence determined from historical deployments and the probabilistic times to test the features that are to be deployed.