AI Digital Assistant for Scriptless Software Testing Automation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional software testing approaches rely on complex script libraries that are difficult to maintain and scale, especially when dealing with multiple functional variations of an application.
Innovation Solution
An intelligent digital assistant is introduced that uses natural language processing with deep learning to enable script-less automation of software testing. The digital assistant receives natural language requests, identifies testing intents and user interface controls, and generates executable code to perform testing actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional script library approaches are used for software testing automation, then testing coverage can be achieved, but the complexity of creating and maintaining scripts increases significantly
Solution Approach 1:
The patent replaces the mechanical scripting system with an AI-based natural language processing system. Instead of requiring testers to write and maintain complex test scripts using traditional automation tools, the system uses NLP models to interpret natural language test descriptions and automatically generate and execute test cases. This substitution eliminates the need for manual script creation while maintaining comprehensive testing coverage.
Solution Approach 2:
The system enables self-service testing by allowing testers to describe test scenarios in natural language without needing to learn scripting languages or understand the complexities of test automation framework. The AI model automatically translates these descriptions into executable test cases, making the testing process accessible to non-technical users and eliminating the dependency on specialized testing expertise.
2Reliability
If skilled testers create and maintain test scripts, then testing quality can be ensured, but the demand for skilled testers and their time continues to grow
Solution Approach 1:
The patent replaces the human tester's manual script creation and maintenance work with an AI-based natural language processing system. The NLP model automatically generates, executes, and maintains test cases based on natural language descriptions, eliminating the need for skilled testers to spend time on routine scripting tasks while preserving testing quality through the AI's automated analysis and execution capabilities.
3Reliability
If complex script libraries are used to handle multiple functional variations, then testing comprehensiveness improves, but the effort to create and maintain scripts becomes a major project
Solution Approach 1:
The patent implements a universal natural language interface that can handle multiple functional variations and test scenarios through a single system. Instead of requiring separate scripts for each functional variation, the AI model interprets natural language descriptions and automatically adapts to different test scenarios, making the testing system versatile and efficient across multiple application functions.
Solution Approach 2:
The system replaces the mechanical process of creating and maintaining separate scripts for each functional variation with an AI-based natural language processing system. The NLP model automatically generates appropriate test cases for different functional scenarios based on natural language input, eliminating the need for manual script creation efforts while maintaining comprehensive testing coverage across all functional variations.
Data Source
AI summary
A digital assistant can provide support for automated testing of applications. A natural language interface can be provided by which a testing user can specify a request for one or more testing actions. A natural language processing model can recognize intents in the request, and the intents can be used to execute executable code to perform the requested testing actions. Multiple actions per request can be supported. An object repository can be leveraged to determine user interface control identifiers, and a test data container can store values for use during testing. Testing functionality can thus be provided to a wider base of testing users. A real time, scriptless approach can conserve computing resources.