AI Test Case Generation from Ambiguous Requirements
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Solution Overview
Problem
Existing test case generation processes are inefficient due to poorly written or unclear requirements, resource constraints, and the need for manual re-evaluation, leading to sub-optimal use of resources and delayed deployments.
Innovation Solution
An automated test case generation service using artificial intelligence and machine learning models to analyze, classify, and generate test cases and scripts, optimizing resource use and expediting deployments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual test case generation is used, then test case quality can be ensured by expert review, but resource consumption increases and deployment time is delayed
Solution Approach 1:
The system enables self-service test case generation where the automated generation service creates test cases from requirements without manual intervention. The service autonomously analyzes requirements, generates test cases, and integrates them into the testing workflow, eliminating the need for expert manual review while maintaining quality through automated validation mechanisms.
Solution Approach 2:
The patent replaces the mechanical manual process of expert review and test case creation with an automated generation service that uses software algorithms to analyze requirements and generate test cases. This substitution transforms the manual mechanical process into an automated computational process, improving both speed and consistency.
2Productivity
If automated test case generation is implemented, then resource use is optimized and deployment is expedited, but test case quality may deteriorate due to poor requirement specifications
Solution Approach 1:
The system performs preliminary analysis of requirements to identify poor specifications, missing information, or ambiguous statements before test case generation begins. By detecting these issues in advance, the system can either request clarification or generate appropriate test cases with noted assumptions, preventing quality deterioration while maintaining automated efficiency.
Solution Approach 2:
The automated generation service incorporates feedback mechanisms where generated test cases are evaluated against quality criteria, and the system iteratively refines its generation process. Feedback loops allow the service to learn from generated test cases and improve future generations, maintaining quality while preserving automation benefits.
3Measurement precision
If manual requirement analysis is performed, then test case accuracy is improved, but time consumption increases
Solution Approach 1:
The system replaces manual requirement analysis with automated text processing and pattern recognition algorithms. The automated generation service uses natural language processing and rule-based systems to analyze requirements and generate accurate test cases, substituting human mechanical analysis with computational analysis that is both faster and consistently accurate.
Solution Approach 2:
The system changes the parameters of analysis by using multiple analysis criteria, confidence thresholds, and generation rules that can be adjusted based on requirement quality. By dynamically adjusting analysis depth and generation parameters, the system achieves high accuracy for complex requirements while maintaining speed for simpler cases.
Data Source
AI summary
A method, a device, and a non-transitory storage medium are described in which a test case generation service is provided. The service may include receiving and validating requirement text associated with a prospective test case and/or test script. The service may invoke a remedial procedure when the requirement text is not validated. The service may include selecting multiple types of classification models based on a model profile. The service may include aggregating probability values associated with classifications for sentences, and approving such classification that satisfy a threshold value. The service may further include generating test cases and/or test scripts. The service may generate a schedule for test cases and/or test scripts based on one or multiple criteria values generated by one or multiple predictive models.