Dynamic Combinatorial Test Design Model Adaptation
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Solution Overview
Problem
Existing Combinatorial Test Design (CTD) modeling techniques face challenges in efficiently testing systems under test (SUT) due to mismatches between the CTD model's value ranges and the actual system configuration, leading to the need for manual alterations or skipping of test cases.
Innovation Solution
The method involves dynamically querying the SUT to gather configuration information, modifying the CTD model to align with the system's actual value sets, and automatically adjusting attribute values and scaling the model based on the SUT's resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a static CTD model is used with fixed value ranges, then the test design is simple and easy to manage, but the model cannot adapt to actual system configuration changes leading to mismatches between expected and actual values
Solution Approach 1:
The patent implements dynamic CTD modeling by querying the system under test at runtime to obtain actual configuration values, then automatically modifying the CTD model's value ranges to match the actual system configuration. This transforms the static model into a dynamic one that adapts to real system states without manual intervention.
Solution Approach 2:
The system automatically queries itself to gather configuration information and self-modifies the CTD model based on the retrieved data. The model maintenance component autonomously adjusts value ranges without requiring manual input or external intervention, enabling the system to serve its own adaptation needs.
2Productivity
If manual alterations to test cases are performed to match system configuration, then test relevance to actual system is improved, but testing time and labor requirements increase
Solution Approach 1:
The system automatically queries its own configuration and self-adjusts the CTD model without requiring manual intervention. The model maintenance component autonomously retrieves system configuration data and modifies value ranges accordingly, eliminating the need for manual test case alterations and significantly reducing testing time.
Solution Approach 2:
The system performs preliminary configuration queries and model adjustments before executing test cases. By proactively gathering system configuration information and pre-modifying the CTD model to match actual system state, the system eliminates the need for time-consuming manual adjustments during or after testing.
3Reliability
If the CTD model includes all possible value combinations, then comprehensive test coverage is achieved, but the number of test cases becomes unmanageably large when system configuration varies
Solution Approach 1:
The patent dynamically adjusts the CTD model's value ranges based on actual system configuration queried at runtime. Instead of using fixed comprehensive value combinations, the model adapts to reflect only the relevant value ranges present in the actual system, reducing test case volume while maintaining appropriate coverage.
Solution Approach 2:
The system changes the parameter values (value ranges) in the CTD model based on actual system configuration. By modifying these parameters dynamically rather than using static comprehensive lists, the system maintains test coverage relevance while significantly reducing the number of test cases when system configuration varies.
Data Source
AI summary
Dynamic Combinatorial Test Design (CTD) modeling includes querying, by a processing device, a system under test (SUT) to be tested based on a CTD model. The processing device receives, based on the querying, system configuration information including one or more system value sets each corresponding to an attribute of the CTD model. The processing device modifies the CTD model based on the received configuration information.