Test Case Generation Using Input Dependency Clusters
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Solution Overview
Problem
Existing test case generation methods, such as the Cartesian Product and Orthogonal Array methods, are time-intensive and computationally costly due to the need to input and compute all possible input fields and variables, and they do not efficiently cover all possible scenarios, leading to inconsistencies.
Innovation Solution
A method and system that utilize user-identified dependent relationships among input fields to create relational field clusters, allowing for efficient test case generation by computing all possible test conditions among these fields, reducing the number of test cases needed through techniques like the Cartesian Product, and displaying results that comply with user-defined logic rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the Cartesian Product method is used to test all possible input values, then maximum coverage of the tested item is achieved, but the number of test cases becomes overwhelming and the process becomes time intensive and costly
Solution Approach 1:
The patent segments the input fields into independent sets based on their dependency relationships. Fields are divided into groups where fields in the same group have dependent relationships, and fields in different groups are independent. This segmentation allows the system to generate test cases by combining results from independent field sets, significantly reducing the total number of test cases while maintaining comprehensive coverage.
Solution Approach 2:
The patent performs preliminary analysis to identify and establish dependency relationships among input fields before test case generation. By pre-determining which fields are dependent on each other and organizing them into independent sets, the system prepares the foundation for efficient test case generation, avoiding redundant testing of independent fields.
2Productivity
If the Orthogonal Array method is used to test common scenarios, then the number of test cases is reduced, but not all possible scenarios are considered and inconsistencies may arise
Solution Approach 1:
The patent segments fields into independent sets based on dependency relationships, allowing systematic exploration of all possible scenarios within each set while maintaining efficiency. This segmentation ensures complete coverage of dependent field combinations without the redundancy of testing all possible input values independently.
Solution Approach 2:
The patent dynamically adjusts the test case generation strategy based on the identified dependency relationships among fields. By analyzing which fields are dependent and organizing them into independent sets, the system adapts its testing approach to generate only the necessary test cases, avoiding both over-testing and under-testing.
3Reliability
If all possible input fields and variables are inputted and computed, then comprehensive testing is achieved, but the process becomes time intensive and requires significant computational resources
Solution Approach 1:
The patent segments input fields into independent sets based on dependency relationships, allowing the system to compute test cases by combining results from these segments. This segmentation reduces the computational complexity from testing all possible combinations independently to testing combinations within independent sets, significantly reducing testing time while maintaining completeness.
Solution Approach 2:
The patent performs preliminary identification and classification of dependency relationships among fields before computation. By pre-organizing fields into independent sets based on their relationships, the system prepares an efficient testing framework that avoids redundant computations and reduces overall testing time.
Data Source
AI summary
A system and method for providing an efficient test case generator is disclosed. A test case project is established upon request from a user, via a user interface, to test an item. The test case project includes a plurality of fields and corresponding designated values to be tested. A first set of identified fields of the plurality are received, wherein the first set includes two or more fields identified by the user as having a dependent relationship with at least one another. A first relational field cluster for the first set of related fields and their values is created. A test case generation technique is performed on the first relational field cluster to compute all relevant test conditions for the first relation field cluster. Results of the computed test conditions for the first relational field cluster are displayed to the user via the user interface.