Schema-Based Synthesized User Data for Secure Software Testing
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Solution Overview
Problem
The challenge of generating realistic user data for software testing is complicated by security risks and the difficulty in capturing the range of potential user inputs, leading to inefficient and inaccurate testing.
Innovation Solution
A method and system for generating synthesized user data using a data specification schema that specifies fields and values, leveraging external algorithms and databases to create realistic test data objects, which are then stored in a tabular format for easy use in testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real user data is used for testing, then testing accuracy is improved, but security risks increase
Solution Approach 1:
The patent creates synthesized user data that copies the structure, format, and realistic characteristics of real user data without using actual sensitive information. The synthesis engine generates mock data objects that mirror the schema and relationships of production data, providing testing accuracy while eliminating security risks associated with real user data.
2Reliability
If diverse user inputs are tested, then application reliability is improved, but testing complexity increases
Solution Approach 1:
The synthesis engine serves multiple functions: it generates diverse test data across various schemas, creates realistic user inputs, and supports different testing scenarios all through a single unified system. This multi-functionality allows comprehensive reliability testing without proportionally increasing complexity, as the engine handles diverse requirements through a consistent data generation framework.
Solution Approach 2:
The system pre-generates diverse user input data and stores it in a database before actual testing occurs. By preparing test data in advance with proper schemas and relationships, the system eliminates the need for complex real-time data generation during testing, thereby improving reliability through comprehensive test coverage while managing complexity through upfront data preparation.
3Reliability
If comprehensive test data is generated, then testing coverage is improved, but data generation time increases
Solution Approach 1:
The system generates comprehensive test data sets in advance and stores them in a database for reuse. By pre-generating data with proper schemas, relationships, and diversity before testing begins, the system achieves high testing coverage without time loss during actual test execution, as the data is readily available from prior generation.
Solution Approach 2:
The synthesis engine creates realistic data copies that maintain schema integrity and relationships, enabling comprehensive testing coverage through synthesized data rather than requiring extensive manual data collection or generation during testing. This copying approach provides full coverage while reducing time investment compared to gathering real data.
Data Source
AI summary
Disclosed are examples of systems, apparatuses, methods, and computer program products for generating synthesized user data. A method may involve receiving a data specification schema. A method may involve determining a number of test data objects to be generated. A method may involve defining the test data objects, the defining of each test data object including: determining, from the data specification schema, a number of fields of the test data object to be populated, the fields representing categories of simulated user data; and determining values for the fields, the values simulating user data. The method may involve storing the test data objects in a database. The method may involve generating a tabular data file including or identifying the test data objects, the tabular data file configured to be processed by one or more processors of a computing system during a user data testing procedure of the computing system.