Test Configuration Generation System for Software Testing
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Solution Overview
Problem
Existing software testing methods face challenges in generating valid test configurations for large volumes of data and multiple parameters, leading to inefficient manual efforts and increased risk due to the difficulty in identifying and discarding invalid configurations, especially when new requirements arise.
Innovation Solution
A method and system for generating valid test configurations by retrieving test parameters from data sources, associating them with requirements and criticality, identifying and storing data values, and automatically detecting and replacing invalid configurations to produce optimized test configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual association of test data with test parameters is used to generate test configurations, then flexibility in test scenario design is maintained, but time consumption increases significantly (30-40% of planning effort)
Solution Approach 1:
The system performs preliminary actions by automatically generating test configurations before manual validation. The automated generation creates initial test configuration sets based on test scenarios and test data, which are then validated and refined manually, significantly reducing the time-consuming manual configuration work while preserving design flexibility
Solution Approach 2:
The system introduces an intermediary automated configuration generation layer between test scenario design and manual configuration. This intermediary automatically correlates test parameters with test data values, producing draft configurations that reduce manual effort by 70-80% while allowing manual adjustment to maintain flexibility
2Reliability
If all possible data combinations are generated for test parameters, then comprehensive test coverage is achieved, but difficulty in identifying valid configurations increases
Solution Approach 1:
The system performs preliminary validation by correlating test parameters with their valid data values before generating test configurations. This preliminary action filters out invalid combinations early in the process, ensuring that only valid configurations are produced, thus maintaining comprehensive coverage while eliminating the difficulty of identifying valid configurations
Solution Approach 2:
The system implements feedback mechanisms where test parameter constraints and valid value ranges are continuously referenced during configuration generation. This feedback loop ensures that generated configurations are validated against known valid combinations, maintaining reliability while reducing the difficulty of detection through automated validation rules
3Productivity
If test configurations are generated without optimization, then generation speed is maintained, but quality of test configurations decreases leading to requirement risk
Solution Approach 1:
The system performs preliminary optimization by automatically selecting and prioritizing test configurations based on requirement criticality and risk assessment. High-priority configurations are generated and validated first, ensuring quality coverage of critical requirements while maintaining efficient generation speed through automated prioritization algorithms
Solution Approach 2:
The system changes parameters by dynamically adjusting configuration generation based on requirement criticality levels and risk factors. Critical requirements receive more rigorous validation and higher priority in the generation sequence, while less critical requirements are processed efficiently, optimizing both quality and speed through parameter-driven prioritization
4Measurement precision
If large volumes of test data are handled manually, then data accuracy can be verified, but handling difficulty and time consumption increase
Solution Approach 1:
The system introduces an intermediary automated data correlation layer that manages the complexity of handling large volumes of test data. This intermediary automatically correlates test parameters with data values based on predefined relationships and constraints, reducing handling complexity while maintaining accuracy through automated validation against data dictionaries and business rules
Data Source
AI summary
The present disclosure is related in general to software testing and a method and a system for generating a valid set of test configurations for test scenarios. A test configuration generation system retrieves one or more test parameters from each of one or more test scenarios associated with a corresponding requirement and a requirement criticality. Further, the test configuration generation system identities data values corresponding to each of the one or more test parameters. Further, it generates a valid set of test configurations for each test scenario based on requirement risk and eliminates one or more invalid test configuration combinations detected and finally a valid set of test configurations for each test scenario is generated. The present disclosure ensures right coverage with right set of configurations that can be executed within the limited time available and reduces 80% of manual efforts in generating the test configurations.