Big Data Test Data Set Generator for Agile Testing
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Solution Overview
Problem
Current big data testing methods face challenges in generating effective test data sets that cover various constraints and scenarios efficiently, particularly in agile development processes, where high volumes of data and dynamic changes hinder quick feedback and processing times.
Innovation Solution
A self-adaptive Big Data Test Data Set Generator automates the creation of partitioned test data sets by analyzing constraints and data sources, using combinatorial coverage and JSON formats to specify constraints, enabling rapid generation of effective test data sets that satisfy complex requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the entire historical database or large portions of original data are processed for testing, then test data coverage and completeness are improved, but processing time increases significantly (taking days or weeks)
Solution Approach 1:
The patent segments the large original data set into smaller, manageable partitions based on combinatorial coverage criteria. Instead of processing the entire historical database, the system divides data into meaningful segments that collectively cover all test scenarios and constraints, thereby reducing processing time while maintaining comprehensive test coverage.
Solution Approach 2:
The patent applies partial action by generating only the necessary portion of test data required to cover all test scenarios. Rather than processing 100% of the original data, the system calculates and processes only the minimal sufficient subset (e.g., 5-10% coverage) that satisfies all constraints and combinatorial requirements, significantly reducing processing time while maintaining effectiveness.
2Adaptability or versatility
If manual test data generation is performed by project managers, architects, developers, and testers, then test data can be customized to meet specific requirements, but the process is time-consuming and difficult to maintain when new data or constraints are introduced
Solution Approach 1:
The patent implements self-service by automating the test data generation process. The system automatically analyzes constraints, determines combinatorial coverage requirements, generates appropriate test data partitions, and validates results without requiring manual intervention from project managers, architects, developers, or testers. This maintains adaptability to changing requirements while dramatically improving productivity.
Solution Approach 2:
The patent introduces dynamics by creating a flexible, adaptive system that automatically adjusts to changing data and constraints. When new data sources or constraints are introduced, the system dynamically recalculates combinatorial coverage requirements and regenerates test data partitions accordingly, eliminating the maintenance burden associated with manual processes while preserving customization capabilities.
3Productivity
If random test data generation tools are used, then test data can be generated quickly, but the test data sets do not provide effective coverage or include quality test data
Solution Approach 1:
The patent applies preliminary action by pre-calculating combinatorial coverage requirements and constraint specifications before generating test data. The system analyzes all constraints and test scenarios in advance, determines the optimal partitioning strategy, and then generates test data that is guaranteed to meet coverage requirements. This eliminates the trial-and-error nature of random generation while maintaining quick generation speeds.
Solution Approach 2:
The patent changes key parameters from random generation to constraint-driven generation. Instead of using random selection with no regard for coverage, the system transforms the generation process to systematically select data partitions based on combinatorial coverage criteria, constraint satisfaction, and test scenario requirements. This maintains generation speed while dramatically improving test data quality and effectiveness.
4Reliability
If test data sets are generated to cover various constraints and test scenarios, then test effectiveness is improved, but the complexity of specifying and managing constraints increases
Solution Approach 1:
The patent applies universality by creating a unified constraint specification framework that handles multiple types of constraints (foreign key, unique key, check constraints, business rules) through a single systematic approach. The system provides universal methods for specifying, analyzing, and satisfying all constraint types simultaneously, reducing the complexity of managing diverse constraint requirements while achieving comprehensive test scenario coverage.
Data Source
Figure 1~2B
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Figure 3B
AI summary
A system for generating an effective test data set for testing big data applications includes a data collector, a data analyzer, an input domain modeler, a self-adaptive input domain modeler, and a test data set generator. The data collector collects a high volume of data from an original data set and initial constraints, the data analyzer analyzes the data and the initial constraints to generate analytical results, the input domain modeler automatically generates an input domain model based on the analytical results, the self-adaptive input domain modeler generates a self-adaptive input domain model by combining the input domain model and analytical results, and the test data set generator generates an initial test data set based on the self-adaptive input domain model. A method for generating an effective test data set for testing big data applications is also described.