Test Data Generator Using Scenario Creator
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Solution Overview
Problem
Current methods for generating test data, such as manual entry, programmatic generation, and data pruning from production datasets, are inefficient, prone to errors, and limited in complexity, affecting the validity and usefulness of unit, integration, and functional testing.
Innovation Solution
A digital data processor-based test data generator system that uses a scenario creator to generate test data sets by enumerating values for entities and their attributes based on hierarchical and priority relationships, with the ability to accept user input and utilize generators and receivers for formatting test data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual test data generation is used, then test data can be created with some control, but it is inefficient, time-consuming and limited in complexity
Solution Approach 1:
The system allows test data to generate itself through automated enumeration of entities and attributes based on hierarchical relationships, eliminating the need for manual creation while maintaining control over the generation process through defined parameters and rules
Solution Approach 2:
The system performs preliminary setup by defining entities, attributes, and hierarchical relationships before test data generation begins, enabling automated and efficient generation without manual intervention during the actual testing process
2Quantity of substance
If programmatic test data generation is used, then larger sets of test data can be produced, but it is still limited in complexity and just as time consuming
Solution Approach 1:
The system segments test data generation into distinct entities with attributes and hierarchical relationships, allowing complex test data to be built from simpler components through systematic enumeration rather than monolithic programmatic generation
Solution Approach 2:
The system adds the dimension of hierarchical relationships between entities, transforming flat programmatic generation into multi-dimensional data structures that naturally increase complexity without requiring more time
3Reliability
If data pruning from production data is used, then real production data can be obtained, but confidential data must be encrypted or removed and negative testing is difficult
Solution Approach 1:
The system extracts only the essential structural elements (entities, attributes, relationships) from production data patterns without using actual production data, eliminating confidential information while preserving data validity for testing
Solution Approach 2:
The system creates synthetic copies of production data structures with realistic relationships and hierarchies, maintaining data validity for testing while ensuring no actual confidential information is present
4Adaptability or versatility
If production data is used for testing, then real-world scenarios can be tested, but code and business logic cannot be tested before production environment introduction
Solution Approach 1:
The system performs preliminary test data generation based on hierarchical relationships and entity definitions, enabling code and business logic testing to occur before production environment introduction while still using realistic data structures
Data Source
AI summary
The invention provides, in one aspect, a digital data processor-based test data generator, that includes a digital data processing system with one or more digital data processors that are coupled for communications. A scenario creator executes on the digital data processing system and accepts, for each of one or more entities (“domains”), a plurality of parameters, including a hierarchical relationship between that entity and one or more other entities, a priority-of-test-data-creation relationship between that entity and one or more entities, and one or more attributes of that entity. The scenario creator generates a parameter set that defines a test data set specifying the aforesaid entities, relationships and attributes. The test generator further includes an engine that enumerates values for the entities and their attributes in an order determined by the aforesaid relationships.