QA Test Adaptation via Customer Knowledge Base
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Solution Overview
Problem
The quality gap between QA testing of data storage applications and customer experiences in the field is significant due to differences in system configurations and parameters, leading to potential customer dissatisfaction.
Innovation Solution
A system that communicably couples customer storage systems with a customer knowledge base, allowing data on system configurations and application versions to be migrated to a QA database, enabling adaptation of test plans and execution of application upgrades to match common customer configurations, thereby closing the quality gap.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If QA personnel use static test cases with fixed system configurations and parameters, then testing can be standardized and reproduced, but the test results do not match customer experiences in the field
Solution Approach 1:
The patent transforms static test cases into dynamic test cases that automatically adapt to different customer system configurations. The system retrieves actual customer configuration data from the knowledge base and dynamically generates test cases that match real-world deployments, resolving the contradiction between standardization and adaptability.
Solution Approach 2:
The system changes test parameters by retrieving actual customer system configuration parameters (hardware types, IO protocols, system settings) from the knowledge base and using them to configure test cases. This allows test cases to adapt to different configurations while maintaining standardized testing processes.
2Quantity of substance
If QA testing uses generic system configurations, then testing coverage can be broad, but quality gaps arise when customer-specific configurations are not represented
Solution Approach 1:
The system establishes a feedback loop where customer configuration data from the knowledge base is continuously retrieved and used to update and adapt test cases. This feedback mechanism ensures that testing coverage remains broad while accurately representing actual customer configurations, eliminating quality gaps.
Solution Approach 2:
The system performs preliminary actions by pre-retrieving and storing customer configuration data in the knowledge base before testing. This allows test cases to be pre-configured with accurate customer-specific parameters, ensuring both broad coverage and high configuration accuracy.
3Ease of operation
If test cases are manually created by QA personnel, then testing can be customized, but the process is time-consuming and may miss common customer configurations
Solution Approach 1:
The system enables self-service by automatically retrieving customer configuration data from the knowledge base and generating test cases without manual intervention. This automation reduces both the time required for test development and the effort needed by QA personnel, while ensuring common customer configurations are accurately represented.
Solution Approach 2:
The system creates copies of customer configuration data from the knowledge base and uses these copies to generate test cases. This copying mechanism allows rapid reproduction of actual customer environments in testing, significantly reducing test development time while maintaining accuracy.
Data Source
AI summary
Techniques for performing test adaption and distribution for customer storage systems in accordance with a customer knowledge base. The techniques can include sending, by each customer storage system, data to the customer knowledge base. The data includes, for each customer storage system, a version of a data storage application and associated system configuration and parameters. In response to a query from a QA testing system, the data is migrated from the customer knowledge base to a QA database. The QA testing system analyzes the data to determine the most common system configuration, parameters, and data storage application version for most of the customer storage systems. The QA testing system adapts a test plan for testing the data storage application to conform with the most common system configuration and parameters and executes an application upgrade plan on QA storage appliances pre-prepared in accordance with the most common data storage application version.