Automated Testing Engine for Natural Language Mapping Specifications
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Solution Overview
Problem
Current dataset testing processes rely heavily on manual methods due to the natural language format of mapping specifications, which are not suitable for programmatically driven testing, leading to inefficiencies and incomplete test coverage.
Innovation Solution
A system utilizing a testing engine with machine learning and artificial intelligence techniques to transform input datasets into structured formats, automatically identifying test conditions and generating scripts, thereby enabling automated testing of datasets with natural language data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual processes are used to identify test data and create test scripts, then the testing process can handle natural language mapping specifications, but the testing time and effort increase significantly
Solution Approach 1:
The patent replaces manual mechanical processes with an automated system comprising a testing engine, natural language processing module, and machine learning model. The testing engine automatically parses natural language mapping specifications, extracts test conditions, identifies test data, and generates test scripts without human intervention, thereby reducing testing time while maintaining adaptability to natural language formats.
Solution Approach 2:
The system enables self-service automation where the testing engine independently performs the complete testing workflow. The natural language processing module automatically interprets mapping specifications, the machine learning model autonomously identifies test conditions and selects test data, and the system automatically generates and executes test scripts, eliminating the need for manual testing processes.
2Reliability
If manual inspection of dataset records is performed to identify test data, then test conditions can be understood in context, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The patent replaces manual inspection with an automated machine learning-based system. The natural language processing module parses mapping specifications to extract test conditions, and the machine learning model automatically analyzes dataset records to identify test data that satisfies these conditions. This automated approach maintains high accuracy in identifying relevant test data while dramatically improving testing efficiency by eliminating manual labor.
Solution Approach 2:
The patent introduces an intermediary machine learning model that acts as a bridge between the natural language mapping specifications and the dataset records. This intermediary automatically interprets the test conditions from the mapping specifications and matches them against the dataset to identify appropriate test data, thereby maintaining reliability while enhancing productivity.
3Ease of operation
If mapping specifications are written in natural language format, then they are easier to understand and write, but they cannot be processed by programmatically driven testing processes
Solution Approach 1:
The patent replaces the need for structured programming languages with a natural language processing system. The testing engine incorporates an NLP module that automatically parses and interprets natural language mapping specifications, converting them into executable test logic. This allows users to write mapping specifications in easy-to-understand natural language while the system automatically processes them programmatically, thereby achieving both ease of operation and automation.
Solution Approach 2:
The patent introduces a natural language processing intermediary that mediates between the human-readable mapping specifications and the automated testing engine. This intermediary translates natural language text into structured test conditions and logic that the programmatically driven testing process can execute, thereby enabling both natural language ease of use and automated processing capability.
4Productivity
If automated testing processes are implemented, then testing speed increases, but they require structured input formats that are difficult to write and maintain
Solution Approach 1:
The patent replaces complex structured input requirements with a natural language processing system. The testing engine automatically parses and interprets natural language mapping specifications, eliminating the need for users to learn and maintain complex structured formats. This maintains high testing speed through automation while simplifying the input format to easy-to-write natural language.
Data Source
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AI summary
Systems, methods, and computer-readable storage media facilitating automated testing of datasets including natural language data are disclosed. In the disclosed embodiments, rule sets may be used to condition and transform an input dataset into a format that is suitable for use with one or more artificial intelligence processes configured to extract parameters and classification information from the input dataset. The parameters and classes derived by the artificial intelligence processes may then be used to automatically generate various testing tools (e.g., scripts, test conditions, etc.) that may be executed against a test dataset, such as program code or other types of data.