Automatic Test Case Categorization for Model-Based Testing

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Manual categorization of test cases for model-based testing is time-consuming, costly, and prone to errors, hindering the adoption of this efficient testing technique due to the need for extensive human intervention and inefficiencies in grouping and sub-grouping similar test cases.

Innovation Solution

A system and method that automatically categorizes test cases using a test case importing module, primary and secondary grouping modules, a test step matrix generator, and algorithms like silhouette method and K-means, to efficiently group and sub-group test cases with minimal human intervention, facilitating optimal categorization and naming of test cases.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual categorization of test cases is performed, then test cases can be grouped based on similar functionalities, but it requires a lot of time and effort

Engineering Contradiction:

Improveaccuracy of test case categorizationVSAvoidtime required for categorization

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent replaces the manual mechanical process of categorizing test cases with an automated computational system. The system uses algorithms to automatically analyze test case descriptions, extract keywords, and group test cases by functionality without human intervention, thus eliminating time consumption while maintaining categorization accuracy

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables test cases to be self-categorized by automatically analyzing their own descriptions and characteristics. Each test case is processed independently through keyword extraction and similarity comparison, allowing the system to autonomously generate functional groupings without requiring manual review or adjustment

Inventive Principle:

Principle #25Self-service

2Ease of manufacture

If manual categorization of test cases is performed, then test cases can be organized into groups, but it is costly and prone to errors

Engineering Contradiction:

Improveease of test case organizationVSAvoidaccuracy of categorization

Core Design Contradiction:

Ease of manufactureVSReliability

Solution Approach 1:

The patent replaces error-prone manual categorization with an automated computational system that consistently applies the same algorithms and criteria to all test cases. This eliminates human errors such as inconsistent grouping criteria, missed test cases, or incorrect functional assignments, thereby improving reliability while maintaining ease of organization

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system incorporates feedback mechanisms where categorization results are continuously refined based on algorithmic analysis of test case patterns and relationships. The automated system learns from the data and adjusts grouping criteria to improve accuracy, ensuring consistent and reliable categorization without manual intervention

Inventive Principle:

Principle #23Feedback

3Productivity

If model-based testing is adopted, then testing efficiency is improved, but existing test cases need to be categorized which requires extensive human intervention

Engineering Contradiction:

Improvetesting efficiencyVSAvoidcomplexity of test case categorization process

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent extracts the categorization task from the overall testing process and handles it separately through automated algorithms. By isolating the categorization function and processing it independently using computational methods, the system eliminates the need for extensive human intervention in this specific sub-task, thereby reducing process complexity while maintaining high testing efficiency

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system segments the test case categorization process into distinct automated steps: keyword extraction, similarity comparison, group formation, and validation. This segmentation allows each step to be handled by specialized algorithms, reducing the overall complexity of the categorization process while enabling efficient processing that supports high productivity in model-based testing

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS10482006B2System and method for automatically categorizing test cases for model based testing

Publication Date: 2019.11.19 COGNIZANT TECH SOLUTIONS INDIA PVT LTD

AI summary

A system and computer-implemented method for automatically categorizing test cases is provided. The system comprises a test case importing module configured to receive one or more test cases and a primary grouping module configured to sort the received test cases based on description of the test cases into groups and form a test case array connecting each of the one or more received test cases with a corresponding group number. Furthermore, the system comprises a test step matrix generator configured to receive the one or more test cases and analyze test steps corresponding to each of the one or more received test cases to create a test step matrix. In addition, the system comprises a matrix concatenator configured to concatenate the test case array with the test step matrix and a secondary grouping module configured to generate one or more groups of test cases belonging to one or more categories.