Neural Network Test Generation from Natural Language
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Solution Overview
Problem
Traditional test case automation requires technical familiarity with testing frameworks, leading to inconsistencies and inefficiencies, especially when new users create tests, due to the need for pseudo-programming skills and extensive functionality configuration.
Innovation Solution
A computational approach using a pre-trained neural network that identifies functional intent and language in natural language descriptions to automatically generate test scripts, leveraging convolutional neural networks and reinforcement learning to classify sentences and generate usable test data/configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional test case automation frameworks are used, then test automation can be achieved, but technical expertise and familiarity with pseudo-programming languages are required
Solution Approach 1:
The patent replaces the mechanical system of manual test case creation using pseudo-programming languages with an automated natural language processing system. The neural network model automatically translates natural language test descriptions into executable test cases, eliminating the need for manual configuration and technical expertise in test automation frameworks.
Solution Approach 2:
The system enables testers to create test cases independently using natural language without requiring expertise in test automation frameworks. The neural network model serves as an intelligent assistant that automatically generates test cases from simple text descriptions, making the test creation process self-service oriented and accessible to non-technical users.
2Adaptability or versatility
If test automation frameworks with extensive functionality are used, then comprehensive testing capabilities are achieved, but configuration complexity and time increase
Solution Approach 1:
The patent extracts the essential testing capabilities from complex automation frameworks and encapsulates them in a neural network model. Instead of requiring users to configure extensive framework functionality, the system extracts only the necessary elements and automatically generates test cases based on natural language input, significantly reducing configuration complexity.
Solution Approach 2:
The neural network model is pre-trained on extensive test data and framework knowledge before deployment. This preliminary action embeds comprehensive testing capabilities into the model, allowing it to automatically generate appropriate test cases without requiring users to manually configure framework settings or understand complex functionality during test creation.
3Reliability
If manual test case creation by testers is performed, then test coverage can be achieved, but consistency and effectiveness vary with user expertise
Solution Approach 1:
The neural network model serves as a universal test generation engine that processes all test cases through the same intelligent system. It maintains consistent effectiveness and quality across different test scenarios by applying learned patterns from training data, eliminating variations caused by individual tester expertise levels while maintaining high test coverage.
4Ease of operation
If natural language processing with neural networks is used, then ease of test creation is improved, but computational resources and training time are required
Solution Approach 1:
The system performs the computationally intensive training process in advance during system setup. The neural network model is pre-trained on extensive test data before deployment, so that during actual test case creation, only lightweight inference operations are required. This preliminary action shifts the time investment from operational use to initial setup, making the system efficient during production use.
Data Source
AI summary
Computer implemented methods and systems are provided for generating one or more test cases based on received one or more natural language strings. An example system comprises a natural language classification unit that utilizes a trained neural network in conjunction with a reinforcement learning model, the system receiving as inputs various natural language strings and providing as outputs mapped test actions, mapped by the neural network.