Test Database Data Generation Using Column Dependency Extraction
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Solution Overview
Problem
Existing methods for generating test database data in enterprise system replacements fail to accurately reflect dependencies between columns or tables, leading to undetected bugs and failures during the testing phase due to differences between testing and actual data.
Innovation Solution
A method that analyzes dependencies between columns or tables based on production DB data and schemas, extracting distribution and dependency information to generate test DB data that mirrors the actual data, including column dependency calculations and data verification to ensure accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If test DB data is generated based on column characteristics alone, then data generation is simple, but dependencies between columns or tables are not reflected
Solution Approach 1:
The patent extracts dependency information between columns and tables in advance from the production DB schema and data, storing it for later use during test data generation. This preliminary analysis enables the generation of test data that reflects actual data relationships without requiring complex real-time processing during testing.
Solution Approach 2:
The patent introduces an intermediary mechanism that uses extracted dependency information as a bridge between the simple column-based generation approach and the need for accurate multi-table dependencies. This intermediary layer guides the generation process to respect column and table relationships while maintaining relative simplicity.
2Reliability
If user correction or separate programs are used to reflect dependencies, then dependencies are captured, but workload increases and data characteristics may diverge from production DB
Solution Approach 1:
The system performs self-service by automatically extracting dependency information from the production DB schema and data, then using this extracted information to guide test data generation. This eliminates the need for manual user correction or separate complex programs, as the system autonomously captures and applies dependency relationships.
Solution Approach 2:
Dependency information is extracted in advance from the production environment and stored for use during test data generation. This preliminary extraction phase separates the complex dependency analysis from the data generation phase, reducing overall process complexity while ensuring accurate dependency reflection.
3Reliability
If manual correction is performed to reflect dependencies, then testing accuracy improves, but time consumption increases
Solution Approach 1:
The patent performs preliminary extraction of dependency information from the production DB before test data generation. This advance preparation automates what would otherwise require time-consuming manual correction, maintaining high testing accuracy while significantly reducing the time investment required.
Solution Approach 2:
The system uses feedback from the extracted dependency information to automatically adjust and guide the test data generation process. This feedback mechanism ensures that test data accurately reflects production data relationships without requiring manual intervention, thereby improving accuracy while minimizing time loss.
Data Source
AI summary
A test DB data generation method for generating a database for testing, which approximates an existing database, the test DB data generation apparatus comprising: extracting distribution information of values of each column of the existing database; extracting column dependency information of the existing database; and generating test DB data based on the distribution information and the column dependency information.