NLP Test Case Generation from User Stories and Acceptance Criteria
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Solution Overview
Problem
Conventional software development techniques for generating test cases are time-consuming and error-prone, often leading to inadequate testing and reduced software quality due to missed defects and insufficient scenario coverage.
Innovation Solution
Utilizing machine learning techniques, particularly natural language processing (NLP), to automatically generate test cases by processing user input data, such as user stories and acceptance criteria, to identify relevant information and create comprehensive test scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual techniques are used to generate test cases, then test cases can be created with human judgment and understanding, but the process becomes time-consuming and error-prone
Solution Approach 1:
The system enables self-service test case generation by automatically processing user input data through machine learning models to produce test cases without requiring manual creation. The ML system serves itself by taking requirements as input and generating complete test cases independently, eliminating the time-consuming manual process while maintaining quality through algorithmic analysis.
Solution Approach 2:
The patent replaces the mechanical manual process of test case creation with an automated machine learning system. Instead of human testers manually writing test cases, the system uses ML models to automatically generate test cases from user input data, substituting the mechanical human effort with an automated intelligent system that reduces time while maintaining or improving accuracy.
2Adaptability or versatility
If manual techniques are used to generate test cases, then the process can be flexible and adaptable, but it leads to insufficient scenario coverage and missed defects
Solution Approach 1:
The machine learning system provides universal test case generation capability that can handle multiple types of software scenarios and requirements. The ML model is trained to recognize patterns across different domains and automatically generates comprehensive test cases covering various scenarios, ensuring broader coverage and more reliable software quality assessment compared to manual methods.
Solution Approach 2:
The system incorporates feedback mechanisms where the ML model learns from the generated test cases and their outcomes, continuously improving its ability to cover diverse scenarios. By analyzing patterns in requirements and test results, the system adapts and expands its scenario coverage over time, ensuring comprehensive testing and higher reliability through iterative learning.
3Productivity
If automated techniques are used to generate test cases, then efficiency and productivity are improved, but the complexity of the system increases
Solution Approach 1:
The patent uses machine learning models as intermediaries between user input data and generated test cases. The ML system acts as a mediator that automatically processes requirements and transforms them into comprehensive test cases, significantly improving productivity. While the ML component adds complexity, it enables automated generation that far exceeds manual efficiency, making the system complexity worthwhile for the substantial productivity gain.
Data Source
AI summary
Methods, apparatus, and processor-readable storage media for generating test cases for software testing using machine learning techniques are provided herein. An example computer-implemented method includes obtaining user input data associated with at least one software application; identifying one or more predetermined types of information, including one or more of topic-related information and entity-related information, from at least a portion of the user input data using at least a first set of one or more machine learning techniques; generating one or more test cases for testing at least a portion of the at least one software application by processing at least a portion of the identified information using at least a second set of one or more machine learning techniques; and performing one or more automated actions based at least in part on at least one of the one or more generated test cases.