Data-Driven Testing Framework for Software Reliability
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Solution Overview
Problem
The challenge in software testing lies in efficiently identifying defects or shortcomings in applications, particularly due to the difficulty in proving the absence of defects, managing test environments, and establishing the integrity of results, which is often resource-intensive and not cost-effective to cover every possible case.
Innovation Solution
A data-driven testing framework that includes a data engineering module for creating engineered test data, a computational environment manager for controlling the test environment, and a result analysis module for comparing outputs, which can extract subsets of production data, augment data, and ensure referential integrity, thereby facilitating efficient and reliable testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a large number of tests are run to cover every possible case, then the completeness of testing is improved, but the cost and time consumption increase significantly
Solution Approach 1:
The patent extracts only the essential features and data from production systems to create simplified test datasets. By taking out only the necessary elements (key data attributes, representative business scenarios) rather than copying entire production environments, the system achieves comprehensive testing coverage while dramatically reducing test data volume and testing time.
Solution Approach 2:
The patent segments the complex production environment into distinct test components: data extraction modules, transformation layers, and isolated test execution environments. This segmentation allows systematic coverage of critical areas while excluding unnecessary complexity, enabling thorough testing without requiring complete reproduction of production conditions.
2Reliability
If production data is used for testing, then the representativeness of test data is improved, but the complexity of managing data integrity and referential constraints increases
Solution Approach 1:
The patent extracts only the necessary data elements from production systems, separating test data requirements from production data complexity. By taking out only essential attributes and relationships needed for testing, the system maintains data representativeness while eliminating unnecessary referential constraints and data management overhead.
Solution Approach 2:
The patent creates simplified copies of production data structures that capture essential characteristics without replicating full production complexity. These test data copies include representative samples with necessary relationships but exclude complex referential constraints, enabling easier data management while maintaining test validity.
3Measurement precision
If test data is manually created and controlled, then the precision of test results is improved, but the productivity of the testing process decreases
Solution Approach 1:
The patent implements self-service data generation where the system automatically extracts, transforms, and prepares test data without manual intervention. The data extraction and transformation modules autonomously process production data to create test datasets with precise characteristics, eliminating manual data preparation while maintaining test result accuracy through automated validation processes.
Data Source
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AI summary
An apparatus for testing applications includes a data-processing machine including a memory and a processor operably coupled to the memory. The data-processing machine is configured to implement a data-driven testing framework that includes a data engineering module (16), a computational environment manager (44), and a result analysis module (72). The data engineering module is configured to create engineered test data based at least in part on the application to be tested. The computational environment manager is configured to control a computational environment in which the application is to operate on the engineered test data. The result analysis module is configured to compare engineered test data operated upon by the application with an expected output.