Dynamic Test Case Extraction via NLP Peer-to-Peer Interaction Mining
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Solution Overview
Problem
Software testing often relies on ad-hoc test cases generated from individual interactions, which only cover limited scenarios, failing to account for the full scope of potential system interactions, leading to incomplete quality assurance.
Innovation Solution
A method using natural language processing to dynamically generate test cases from conversational contexts between users, storing these cases as repeatable and deployable robotic process automation bots, enabling automatic execution and expansion of test scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If test cases are structured on static processes, then test cases are easy to manage and execute, but test cases only handle limited scenarios and not the full scope of what may come up
Solution Approach 1:
The patent transforms static test cases into dynamic, adaptive test cases that automatically adjust based on actual user interactions. The system monitors peer-to-peer communications and dynamically generates test cases that reflect real usage scenarios, allowing the test suite to evolve with the system rather than remaining fixed.
Solution Approach 2:
The system enables self-service test case generation by automatically extracting test scenarios from actual user interactions without requiring manual intervention. The NLP system processes communications, identifies test cases, and generates automated test scripts independently, reducing the need for manual test case creation while expanding coverage.
2Adaptability or versatility
If test cases are generated ad-hoc from individual interactions, then test cases reflect actual usage scenarios, but test cases only cover limited scenarios and not the full scope
Solution Approach 1:
The system continuously monitors peer-to-peer communications and continuously generates test cases from actual user interactions. This continuous process ensures that test coverage expands over time as more interactions are captured, maintaining productive test case generation without requiring periodic manual intervention.
Solution Approach 2:
The patent replaces manual test case creation with automated NLP-based systems that process communications and generate test cases automatically. This substitution of mechanical manual processes with automated AI-based processing significantly improves productivity while expanding the scope of tested scenarios.
3Extent of automation
If manual test case creation is used, then test cases can be carefully designed, but manual intervention is required and scalability is limited
Solution Approach 1:
The system substitutes manual test case design and execution with automated NLP-based processing. The system automatically processes peer-to-peer communications, extracts test scenarios, generates test cases, and executes them without manual intervention, achieving high automation extent while managing complexity through modular AI components.
Data Source
AI summary
In an approach for generating a test case from a conversational context between two or more users and storing the test case as a repeatable and deployable robotic process automation bot, a processor monitors an online communication between a first user and a second user using a natural language processing method. Responsive to determining the second user has provided an affirmative response to a request to test a system, a processor outputs a payload to the second user, wherein the payload contains one or more test cases. Subsequent to a N number of iterations of the second user and one or more additional users testing the system, a processor builds a Convolutional Neural Network model to predict when the first test case is successful. Responsive to finding a flag of success, a processor stores the first test case as a repeatable and deployable robotic process automation bot.