Inconsistency Generation System for Test Data
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Solution Overview
Problem
Existing computing systems face challenges in handling inconsistent data due to errors in program code, incomplete transactions, replication issues, and mass data upload mechanisms that often bypass consistency checks, leading to invalid data associations and constraints violations.
Innovation Solution
A system and method for generating test data that includes actual inconsistencies observed in tenant data, by analyzing consistency constraints from metadata, identifying inconsistencies, and modifying test tenant data to exhibit a superset of these inconsistencies, ensuring that secondary systems and applications can handle and test against such data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If random inconsistencies are included within application data for testing, then testing coverage of inconsistency handling is improved, but the difficulty of coding an application to address all possible inconsistencies increases and testing efficiency decreases
Solution Approach 1:
The system automatically extracts inconsistency patterns from production tenant data and generates test data with inconsistencies without requiring manual coding. The application self-services by using its own operational data to create test cases, eliminating the need to manually code all possible inconsistency scenarios.
Solution Approach 2:
The system performs preliminary analysis of production data to identify inconsistency patterns before generating test data. By extracting patterns from actual tenant data in advance, the system prepares a focused set of realistic inconsistency types that will be used to generate test cases, avoiding the need to code all possible inconsistencies.
2Reliability
If comprehensive consistency checks are implemented in applications, then data integrity is improved, but processing speed and system performance deteriorate
Solution Approach 1:
The system extracts inconsistency patterns from production data and uses them to generate test cases separately from the main application flow. By taking out the consistency checking function into a dedicated test data generation process, the main application can maintain fast processing while the separate system ensures data integrity through comprehensive pattern-based testing.
3Productivity
If mass data upload mechanisms bypass consistency checks to improve data import speed, then productivity is improved, but data quality and consistency deteriorate
Solution Approach 1:
The system converts the harmful effect of inconsistent data imported through fast upload mechanisms into a beneficial resource by using these actual inconsistencies as patterns for generating realistic test data. The inconsistencies that would normally be considered errors become valuable test cases that help improve the system's ability to handle real-world data quality issues.
Data Source
AI summary
Systems and methods include determination of first constraints on columns of database tables of a test tenant system based on metadata associated with the columns of database tables, determination of second constraints on the columns of database tables based on data stored in the columns of database tables of the test tenant system, identification of inconsistent data stored in columns of database tables of a plurality of tenant systems based on the first constraints and the second constraints, adding of records to the database tables of the test tenant system based on the identified inconsistent data, and execution of a test based on the database tables of the test tenant system including the added records.