Natural Language Test Automation for Communication Network Nodes
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Solution Overview
Problem
Existing software testing methods for complex computerized systems, such as communication systems, are time-consuming and resource-intensive, particularly in generating test cases, and face challenges with non-formal natural language requirements and limited domain adaptability.
Innovation Solution
An AI-based Cognitive Test Generator (AICTG) system that uses natural language processing to analyze non-formal requirement specifications in multiple languages and generates executable test scripts in C# or JAVA, reducing manual effort and improving adaptability across domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual test case generation is used, then test accuracy and domain expertise can be applied, but time consumption and human effort increase significantly
Solution Approach 1:
The patent introduces an AI-based Cognitive Test Generator as an intermediary system between the requirement specification and the final test script. This mediator automatically analyzes natural language requirements, extracts test scenarios, and generates executable test scripts, thereby reducing manual effort while maintaining test quality through intelligent processing
Solution Approach 2:
The patent replaces the mechanical manual process of test case generation with an automated AI-based system. The cognitive test generator uses natural language processing and machine learning algorithms to substitute human testers' manual activities, significantly reducing time consumption while maintaining or improving test accuracy
2Productivity
If conventional automation approaches are used, then manual effort is reduced, but ability to handle natural language requirements and multi-language support is limited
Solution Approach 1:
The patent implements a universal cognitive test generator that can handle multiple languages and different types of requirement specifications. The system uses multi-lingual natural language processing capabilities and can adapt to various domains through configurable parameters, making it versatile rather than domain-specific
Solution Approach 2:
The patent employs parameter changes in the form of configurable language models and processing thresholds that allow the system to adapt to different languages and domains. By adjusting these parameters, the same automated system can effectively handle diverse requirement specifications without requiring domain-specific customization
3Reliability
If domain-specific testing knowledge is applied, then test quality improves, but adaptability to new domains decreases
Solution Approach 1:
The patent implements a dynamic system that can adapt its testing approach based on the input requirements and domain characteristics. The cognitive test generator dynamically adjusts its analysis depth, script generation strategies, and validation criteria according to the specific domain and requirements, rather than using fixed domain-specific rules
Data Source
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AI summary
A method of testing a network node in a communication system includes obtaining a natural language requirement specification that describes a testing requirement associated with the network node, and selecting one or more test case specifications from a library of test case specifications based on the natural language requirement specification. The method includes extracting textual features from the test case specifications, generating a feature vector comprising the extracted textual features from the test case specifications, and mapping the feature vector to a plurality of available test scripts. One or more of the plurality of available test scripts are selected for execution in response to the mapping of the feature vector, and for one or more of the selected test scripts, a confidence score is generated that measures a confidence of an association of the test case specification with the selected test script. Related systems are also disclosed.