SQL Mutation for Synthetic Regression Test Data
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Solution Overview
Problem
During software regression testing, it is challenging to create proper data for complex queries that return qualified records without exposing confidential information, and existing methods like data masking are often impractical due to security concerns and licensing issues.
Innovation Solution
A method that validates and mutates Structured Query Language (SQL) statements by determining their pattern type, reconstructing them to generate simulated data that excludes confidential elements, and validating the mutated queries to ensure they produce accurate results without revealing sensitive information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If confidential data is used for regression testing, then test accuracy is improved, but security risks increase
Solution Approach 1:
The patent creates a copy of the production database with synthetic data that mimics the structure, relationships, and query patterns of real data without containing actual confidential information. This copy is used for regression testing, maintaining test accuracy while eliminating security risks associated with using real confidential data.
Solution Approach 2:
The patent introduces an intermediary layer between the testing process and confidential data by using a data simulation system that generates synthetic datasets. This intermediary preserves the essential characteristics needed for testing while blocking direct access to sensitive information, thus resolving the contradiction between test accuracy and security.
2Object-affected harmful factors
If data masking is applied to protect confidential information, then security is improved, but data quality deteriorates
Solution Approach 1:
Instead of masking production data, the patent creates a complete synthetic copy of the database with artificially generated data that preserves all structural relationships, foreign key constraints, and query patterns. This approach maintains full data quality for testing purposes while completely eliminating the need to mask any confidential information.
Solution Approach 2:
The patent extracts only the essential structural and relational characteristics from the production database while leaving out all confidential data values. The synthetic data retains the schema, relationships, and query behavior needed for testing, but removes all sensitive information, thus achieving both security and data quality.
3Measurement precision
If complex queries are tested with real data, then test results are more accurate, but the complexity of data preparation increases
Solution Approach 1:
The patent implements a self-service data simulation system that automatically generates synthetic datasets tailored to specific query patterns and testing requirements. The system autonomously creates appropriate test data without manual intervention, reducing data preparation complexity while maintaining high test result accuracy through query-aware data generation.
Solution Approach 2:
The patent performs preliminary analysis of the production database structure, relationships, and query patterns before generating synthetic test data. This preliminary action enables the creation of highly accurate test datasets that are pre-configured to support complex query testing, eliminating the need for complex manual data preparation during actual testing.
Data Source
AI summary
A simulated dataset is queried for regression by validating a structured query language (SQL) statement, determining a pattern type of the SQL statement, reconstructing the SQL statement according to a predetermined process for the pattern type, creating a mutated SQL statement for querying a simulated dataset, and validating the mutated SQL statement. The simulated dataset is based on a confidential dataset having the confidential elements removed or replaced.