Synthesized Test Data Rebalancing for Complete Scenario Coverage
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Solution Overview
Problem
Existing software testing methods require manually created test cases that often fail to cover all possible real-life scenarios, leading to incomplete testing and the use of sensitive or non-realistic data, which compromises testing quality.
Innovation Solution
A method and system for creating synthesized test data with predefined test case coverage by processing queries to extract test cases, determining data storage locations, analyzing distribution, and rebalancing input test data to ensure all scenarios are covered, using production-like data with obfuscation or mock data to maintain quality and privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If test cases are manually created, then test case creation is possible, but test case coverage is incomplete
Solution Approach 1:
The system performs preliminary analysis of the software application to automatically generate comprehensive test cases before manual creation, ensuring all possible scenarios are covered. This preliminary automated generation provides a complete foundation that manual processes alone cannot achieve.
Solution Approach 2:
The system creates synthetic test data that copies the statistical properties and distribution patterns of real production data without containing actual sensitive information. This allows complete test case coverage to be achieved while maintaining data privacy and realism.
2Reliability
If real production data is used for testing, then testing quality is improved, but privacy risks increase
Solution Approach 1:
The system generates synthetic test data that replicates the statistical properties, distributions, and relationships of real production data without containing actual sensitive information. This copying approach maintains testing quality by preserving data characteristics while eliminating privacy risks associated with using real production data.
Solution Approach 2:
The synthetic data maintains local quality by preserving specific statistical properties and distribution patterns of the original production data at different levels. This ensures that testing quality is maintained for specific test scenarios while the overall data is synthetic and privacy-safe.
3Object-affected harmful factors
If synthetic test data is generated, then privacy is protected, but test case coverage may be insufficient
Solution Approach 1:
The system performs preliminary automated analysis to identify all possible test scenarios and generates comprehensive synthetic test data before execution. This preliminary action ensures that synthetic data achieves complete test case coverage by systematically covering all scenarios rather than relying on limited manual test data selection.
Solution Approach 2:
The synthetic test data generation system is designed to be universal, automatically adapting to different software applications and test scenarios. This multi-functionality allows the same synthetic data generation approach to achieve complete test case coverage across diverse testing contexts while maintaining privacy protection.
4Reliability
If more test cases are created to improve coverage, then testing completeness increases, but manual effort increases
Solution Approach 1:
The system replaces the mechanical manual process of creating and managing test cases with an automated computational system. This substitution uses algorithms to automatically generate comprehensive test cases and synthetic test data, eliminating the time-consuming manual effort while achieving complete testing coverage.
Solution Approach 2:
The test case generation system is self-service, automatically analyzing the software application, identifying test scenarios, generating test cases, and creating synthetic test data without requiring manual intervention. This self-service capability achieves complete testing coverage while eliminating manual effort entirely.
Data Source
AI summary
Disclosed is method for creating synthesized test data (302) having predefined test case coverage, method comprising processing query for extracting plurality of test cases (PTC) and for determining data storage location of input test data (ITD) corresponding to PTC; extracting ITD from data repository (204, 310) and executing PTC on ITD for determining individual test case coverage percentages of ITD; determining overall test case coverage (OTCC) (308) of ITD; when OTCC of ITD is less than predefined test case coverage, identifying one or more test cases (312) for which individual test case coverage percentage of ITD is less than predefined value; analyzing distribution of ITD; and rebalancing ITD based on distribution of ITD, for producing synthesized test data, wherein ITD is rebalanced for covering conditions in one or more test cases in manner that OTCC of synthesized test data would be equal to or greater than predefined test case coverage.