Context-Sensitive Synthetic Data Generation for System Testing
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Solution Overview
Problem
Current test data for data processing systems lacks realism and sophistication, particularly in capturing contextual relationships and errors, which is essential for evaluating complex data capturing and processing systems.
Innovation Solution
A system and method for generating domain-specific, context-sensitive test data that can be regenerated with increasing sophistication, using user-defined rules and templates to create realistic and internally consistent data sets for testing data processing systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional test data generation methods are used, then data processing systems can be tested, but the test data lacks realism and contextual relationships
Solution Approach 1:
The patent applies preliminary action by pre-defining templates with field definitions, data types, and contextual relationships before test data generation. These templates establish the structural framework and business rules in advance, allowing the generation system to produce realistic test data by systematically populating predefined patterns rather than creating complex logic during runtime.
Solution Approach 2:
The patent applies copying by replicating real-world data patterns, structures, and contextual relationships through templates. Instead of inventing new complex generation logic, the system copies the essential characteristics of actual domain data (such as form structures, field relationships, and validation rules) into template definitions that can be repeatedly instantiated to generate realistic test datasets.
2Measurement precision
If test data includes contextual relationships and modeled errors, then data capturing system performance can be properly evaluated, but the data generation process becomes more complex
Solution Approach 1:
The patent applies segmentation by dividing the data generation process into distinct modular components: templates define field structures, data types specify value patterns, field rules establish contextual relationships, and error models introduce controlled anomalies. Each component handles a specific aspect of test data generation, making the overall complex system manageable through clear separation of concerns and independent configuration of each segment.
Solution Approach 2:
The patent applies parameter changes by allowing flexible configuration of data generation parameters within templates and rules. Users can modify field definitions, data type parameters, rule conditions, and error characteristics to generate different test scenarios. This parametric approach enables the system to handle complexity through configurable parameters rather than hard-coded complex logic.
3Adaptability or versatility
If sophisticated test data with controlled distributions is generated, then complex processing procedures can be evaluated, but the generation system requires advanced rule-based processing
Solution Approach 1:
The patent applies universality by designing templates and data types as multi-functional components that can serve various test scenarios. A single template framework can generate different types of test data (forms, records, datasets) by configuring different field definitions and data types. The same rule-based engine handles multiple functions including data generation, validation, contextual relationship enforcement, and error injection, reducing the need for separate specialized systems.
Data Source
AI summary
A data generation system provides for generating domain-specific, context-sensitive data collections as synthetic data for testing the performance of data processing systems. Within the data generation system, a composition module defines a data generation template containing a plurality of fields each capable of holding one or more values according to specifications defined for predetermined data types. An evaluation module sorts the fields in an order of dependency so that fields whose values affect the values in other of the fields are ordered before the fields whose values are affected by values in other fields. A data generation module populates the fields with values and retrieves a subset of the values populating the plurality of fields for generating each of a plurality of data sets, which are written into memory and made accessible for use in testing data processing systems.