LLM Test Case Generation from Natural Language Requirements
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Solution Overview
Problem
Manual test case generation in the Software Testing Life Cycle is time-consuming, labor-intensive, and prone to errors, with existing automation frameworks limited to test case execution and reporting, lacking automated test case generation capabilities.
Innovation Solution
A method and system utilizing a pre-configured Large Language Model (LLM) to generate test cases from natural language requirements, including test steps, data, and expected results, with validation and output in a predefined format, leveraging generative AI and machine learning for automated test case generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual test case generation is used, then test cases can be created with human judgment and understanding, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The patent introduces an intermediary system comprising natural language processing module, test scenario generation module, and test case generation module that mediates between requirements and test cases. This intermediary automatically transforms natural language requirements into structured test cases, resolving the contradiction by eliminating manual labor while maintaining quality through multi-stage processing and validation mechanisms.
Solution Approach 2:
The patent replaces the mechanical manual process of test case creation with an automated computational system. The system uses natural language processing, machine learning models, and automated generation algorithms to substitute human manual effort, thereby increasing productivity while maintaining test case quality through structured processing and validation.
2Ease of manufacture
If manual test case generation is used, then test cases can be customized and reviewed, but the process is prone to human errors and inconsistencies
Solution Approach 1:
The patent changes the parameters of test case generation from manual human operations to automated computational processes with controlled variables. The system uses configurable parameters, structured templates, and validation rules to ensure consistency, while maintaining flexibility through natural language input and customizable generation settings.
Solution Approach 2:
The patent implements feedback mechanisms including validation modules that check generated test cases against requirements, quality metrics assessment, and iterative refinement processes. This feedback loop ensures consistency and reliability by automatically detecting and correcting errors, while preserving flexibility through configurable validation rules.
3Extent of automation
If conventional test automation frameworks are used, then test case execution can be automated, but test case generation still requires manual effort
Solution Approach 1:
The patent extends the functionality of test automation frameworks to include not only test case execution but also test case generation. The unified system performs multiple functions - natural language processing, test scenario generation, test case creation, and execution - thereby eliminating the need for separate manual generation processes and reducing overall time loss.
Solution Approach 2:
The patent performs preliminary actions by automatically generating test cases from requirements before the execution phase. The system processes requirements, generates test scenarios, and creates test cases in advance, so that when execution is needed, the test cases are already prepared and ready, eliminating the time loss associated with manual generation.
4Reliability
If more comprehensive test scenarios are included, then test case coverage improves, but the complexity of generation and validation increases
Solution Approach 1:
The patent segments the test generation process into distinct modules: natural language processing module, test scenario generation module, test case generation module, and validation module. Each module handles specific aspects of comprehensive scenario coverage, managing complexity through functional decomposition while maintaining high coverage through coordinated operation of specialized components.
Data Source
AI summary
A method for automated test case generation is disclosed. The method includes receiving one or more requirements in natural language from a user device. Each of the one or more requirements includes an associated set of test scenarios. The method further includes generating one or more test cases for each requirement of the one or more requirements in a predefined template using a pre-configured Large Language Model (LLM). Each of the one or more test cases implements one of the associated set of test scenarios, and includes a plurality of test steps, test data corresponding to each of the plurality of test steps, and expected results of each of the plurality of test steps. The method further includes validating the one or more test cases based on a set of test validation parameters. Upon successful validation, the method further includes generating an output file in a predefined format.