Test Data Generation via Iterative Seed Transformation
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Solution Overview
Problem
Existing software testing methods face challenges in generating effective test data that replicates the syntax, semantics, and statistics of production data without exposing sensitive information, and current synthetic data generation tools are costly, require manual preprocessing, and produce non-reusable data.
Innovation Solution
A method that transforms seed data, which can be production data or user-defined, through a series of iterations to generate test data sets with desired characteristics, including a primary data set and secondary data sets, allowing for efficient data storage and loading into a test database while preserving the characteristics of the original data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If production data is used for testing, then test data effectiveness is improved, but data privacy security deteriorates
Solution Approach 1:
The patent creates synthetic test data that copies the statistical characteristics, syntax, and semantics of production data without using actual production data. The system analyzes production data to learn its patterns and generates artificial data that replicates these characteristics, thereby maintaining test effectiveness while eliminating privacy risks associated with using real production data.
2Object-affected harmful factors
If synthetic data generation tools are used, then data privacy is improved, but cost and complexity increase
Solution Approach 1:
The system performs self-service by automatically analyzing production data characteristics and generating synthetic test data without requiring manual preprocessing or configuration. The patent implements automated statistical analysis, pattern recognition, and data generation capabilities that eliminate the need for manual intervention, reducing both cost and complexity compared to traditional synthetic data generation tools.
3Reliability
If extensive test data is generated, then test coverage is improved, but storage requirements increase
Solution Approach 1:
The patent employs parameter changes by transforming the statistical parameters and characteristics of production data into synthetic test data with controlled quantities. The system adjusts data generation parameters to produce sufficient test coverage while optimizing storage efficiency through compact data representation and selective generation of test data subsets based on testing requirements.
Data Source
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AI summary
Systems and methods for test data generation are described. In one implementation, the method includes receiving seed data (118-1) having one or more characteristics. Further, the method includes obtaining a selection criterion indicating a selected portion of the seed data (118-1) to be transformed. Based on the selection criterion, the seed data (118-1) is transformed for at least a plurality of iterations to generate test data (118-2). The test data (118-2) comprise a plurality of data sets including a primary data set (120-1) generated in a first iteration and a secondary data set (120-2) generated in each subsequent iteration. The primary data set (120-1) includes transformed data corresponding to the selected portion of the seed data (118-1) and non-transformed data corresponding to a remaining portion of the seed data (118-1) and each secondary data set (120-2) includes transformed data corresponding to the selected portion of the seed data (118-1).