Smart Test Case Generator Using Machine Learning
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Solution Overview
Problem
Current software testing methods are time-consuming and prone to errors due to their subjective nature, often requiring manual review and resulting in incomplete testing, with complex applications requiring thousands of hours to test and potentially missing critical test cases.
Innovation Solution
The system automatically generates test cases and scripts from graphical representations of applications using machine learning logic, analyzing attribute data to identify processing flows and simulate user interactions, thereby providing objective and thorough testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual testing methods are used to review application descriptions and create test cases, then testers can identify potential use cases, but the process becomes extremely time-consuming and may result in missed test cases
Solution Approach 1:
The patent replaces manual mechanical testing processes with an automated system that uses machine learning models to generate test cases. The system automatically analyzes application code, generates test scenarios, and executes tests without human intervention, thereby eliminating the time-consuming manual review process while maintaining or improving test case completeness through systematic code analysis.
Solution Approach 2:
The testing system performs self-service by automatically generating test cases from application code without requiring manual input from testers. The machine learning model autonomously analyzes the code structure, identifies testable scenarios, and creates comprehensive test suites, enabling the system to serve its own testing needs without external human assistance.
2Reliability
If comprehensive testing of complex applications with 60+ flows is performed manually, then more test cases can be identified, but the time required increases to thousands of hours annually
Solution Approach 1:
The patent replaces manual testing mechanics with automated machine learning-based test generation. The system processes complex applications with 60+ flows automatically, analyzing code structures and generating comprehensive test cases without human intervention, thereby maintaining high test coverage while dramatically improving testing efficiency and reducing time requirements.
Solution Approach 2:
The system changes the parameters of test generation by using machine learning models that can dynamically adjust test case complexity and depth based on application characteristics. This allows the system to efficiently handle varying levels of application complexity, from simple to highly complex applications with numerous flows, optimizing resource allocation and testing depth automatically.
3Adaptability or versatility
If subjective manual testing techniques are used, then testers can apply their expertise, but the process lacks objectivity and consistency
Solution Approach 1:
The patent replaces subjective human judgment with objective machine learning-based analysis. The system consistently applies standardized algorithms to analyze application code and generate test cases, eliminating variability introduced by different testers' expertise levels while maintaining high adaptability through configurable testing parameters and model training on domain-specific requirements.
Data Source
AI summary
Embodiments provide systems, methods, and computer-readable storage media for automated and objective testing of applications or processes. Graphical representations of the application may be analyzed to derive attribute data and identify flows (e.g., possible processing paths that may be accessed during utilization of the application by a user). Test cases may be automatically generated based on the attribute data and the identified flows. Additionally, testing scripts for testing the portions of the application corresponding to each identified flow may be generated using machine learning logic. Once generated, the testing scripts may be executed against the application to test different portions of the application functionality (or processes). Execution of the testing scripts may be monitored to generate feedback used to train the machine learning logic. Reports may be generated based on the monitoring and provided to users to enable the users to resolve any errors encountered during the testing.