LLM-Based Software QA for Automated Vulnerability Evaluation
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Solution Overview
Problem
Maintaining quality and security assurance of complex and feature-rich software systems has become cumbersome and time-consuming, requiring more sophisticated and automated testing methods.
Innovation Solution
An AI/ML system utilizing large language models (LLMs) as both an actor and evaluator to simulate interactions with software, automatically testing for vulnerabilities and evaluating quality and security without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional quality-assurance testing methods are used on feature-rich and user-interactive software, then testing coverage can be achieved, but the testing process becomes cumbersome and time-consuming
Solution Approach 1:
The system employs AI/ML models that autonomously generate test inputs, execute tests, and evaluate results without requiring human intervention for each testing operation. The models self-manage the entire testing workflow, from generating diverse test cases to analyzing outcomes and identifying vulnerabilities, thereby eliminating the time-consuming manual testing process while maintaining comprehensive quality assurance coverage
Solution Approach 2:
The patent replaces traditional mechanical/manual testing processes with intelligent AI/ML-based automated testing systems. Instead of human testers manually designing and executing test cases, the system uses large language models and machine learning models to automatically generate, execute, and analyze test inputs, substituting human effort with intelligent automation that operates faster and more efficiently
2Adaptability or versatility
If software becomes more feature-rich and user-interactive, then functionality and user experience improve, but maintaining quality and security becomes increasingly difficult and complicated
Solution Approach 1:
The AI/ML-based testing system is designed as a universal platform capable of testing diverse software types including chatbots, operating systems, security systems, web apps, and mobile applications. The system uses multi-functional AI models that can adapt to different software domains and testing requirements, providing comprehensive quality assurance across various feature-rich and user-interactive applications through a single unified testing framework
Solution Approach 2:
The system dynamically adjusts testing parameters and strategies based on the specific characteristics of the software being tested. The AI/ML models analyze the target software's features, user interactions, and security requirements to automatically modify test case generation, input selection, and evaluation criteria, thereby managing testing complexity adaptively while maintaining thorough quality assurance for sophisticated software systems
3Measurement precision
If manual software testing is performed to ensure quality and security, then vulnerabilities can be detected, but the process requires significant human interaction and expertise
Solution Approach 1:
The system employs AI/ML models that autonomously generate test inputs, execute tests, and evaluate results without requiring human intervention for each testing operation. The models self-manage the entire testing workflow, from generating diverse test cases to analyzing outcomes and identifying vulnerabilities, thereby eliminating the time-consuming manual testing process while maintaining comprehensive quality assurance coverage
Solution Approach 2:
The patent introduces AI/ML models as intermediary components between the tester and the software under test. These intelligent intermediaries automatically translate testing objectives into specific test inputs, execute the tests, and interpret the results to identify vulnerabilities. This intermediary layer handles the complexity of automated testing while providing precise vulnerability detection, making the process easier to operate while maintaining high detection accuracy
Data Source
AI summary
Systems and methods are provided for implementing quality assurance for digital technologies using language model (“LM”)-based artificial intelligence (“AI”) and/or machine learning (“ML”) systems. In various embodiments, a first prompt is provided to an LM actor or attacker to cause the LM actor or attacker to generate interaction content for interacting with test software. Responses from the test software are then evaluated by an LM evaluator to produce evaluation results. In some examples, a second prompt is generated that includes the responses from the test software along with the evaluation criteria for the test software. When the second prompt is provided to the LM evaluator, the LM evaluator generates the evaluation results.