Combinatorial Testing Multi-Level Data Structures
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Solution Overview
Problem
Existing software testing methods are inadequate for validating software products that utilize multi-level or hierarchical datatypes, as they face prohibitive complexity and infinite variations, leading to inefficient and exhaustive testing processes.
Innovation Solution
A method is introduced to generate a sparse set of metadata combinations providing full coverage of multi-level datatypes, allowing for the definition of multi-level test datatypes and execution of a test suite to validate software libraries, which includes determining metadata attribute tuples and generating sparse combinations for comprehensive testing without exhaustive enumeration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If exhaustive testing is performed on multi-level hierarchical data structures, then complete coverage of all variations is achieved, but the testing complexity and time become prohibitive or infinite
Solution Approach 1:
The patent segments the hierarchical data structure into multiple levels, where each level has its own metadata attributes. Testing is performed level-by-level rather than attempting to test all combinations at once. The test cases are generated by selecting representative values from each level's metadata attributes and combining them systematically, which divides the overwhelming testing task into manageable segments that can be executed efficiently.
Solution Approach 2:
The patent changes the approach from testing all possible parameter combinations to testing a selected subset of parameter combinations. By identifying key metadata attributes at each level and selecting representative values, the system transforms the testing problem from exhaustive enumeration to strategic sampling. This parameter change approach maintains reliability by ensuring coverage of critical combinations while dramatically reducing testing time.
2Reliability
If all variations of hierarchical data structures are tested, then complete validation is achieved, but the number of test cases becomes infinite
Solution Approach 1:
The patent applies partial action by testing a carefully selected subset of metadata attribute combinations rather than all possible variations. At each level of the hierarchical structure, representative values are chosen from the metadata attributes, and test cases are generated from these selections. This partial testing approach achieves sufficient validation coverage without requiring an infinite number of test cases, as it focuses on testing the structural integrity and metadata handling rather than every possible data value combination.
3Productivity
If known combinatorial testing approaches are used, then testing efficiency is improved, but they are not suited for handling hierarchical or multi-level datatypes
Solution Approach 1:
The patent extends combinatorial testing into a new dimension by adding hierarchical level structure to the testing approach. Rather than applying traditional flat combinatorial testing to hierarchical data, the system organizes test case generation around levels of the hierarchy, where each level contributes its own metadata attributes to the combination process. This dimensional extension allows combinatorial efficiency to be maintained while adapting to the complexities of hierarchical datatypes.
Solution Approach 2:
The patent creates a universal testing framework that can handle various types of hierarchical data structures across different applications. The system defines a general approach where each level of any hierarchical structure can have its own metadata attributes, and the test case generation process universally applies to any such structure. This multi-functional framework allows the same testing methodology to be applied to diverse hierarchical datatypes, making the approach both efficient and highly adaptable.
Data Source
AI summary
Methods and apparatus are disclosed for efficient combinatorial testing of multi-level datatypes and data objects. A multi-level datatype associated with a software library has a plurality of linked levels with corresponding metadata attributes. A sparse set of metadata combinations is generated, providing full coverage of identified tuples of the metadata. Multi-level test datatypes are defined, with metadata attributes following the generated metadata combinations, and used to execute a test suite and validate the software library. A user interface of the software library can be tested and validated directly using the defined test datatypes. Alternatively, functions of the software library can be tested with test objects that are instances of the test datatypes. In variations, the software library can be tested for combinations of data values, or a mix of data and metadata. The software library can be a rules framework providing configuration and implementation of if-then rules for client applications.