GenAI Security Testing for Automated Test and Vulnerability Generation
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Solution Overview
Problem
Existing software testing processes require significant manual intervention by developers, leading to inefficiencies, errors, and increased testing time, especially in the design and execution of software tests, automation scripts, and vulnerability detection.
Innovation Solution
Employing a generative artificial intelligence (GenAI) model to generate software tests, automation scripts, and detect vulnerabilities, reducing human intervention by leveraging large language models and neural networks to understand software components and requirements, and automate the testing process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual software testing is used, then testing accuracy and requirement understanding are improved, but testing time and developer workload increase significantly
Solution Approach 1:
The patent introduces an AI model as an intermediary between the software program and the testing process. The AI model receives the software program and testing requirements, then automatically generates test cases, automation scripts, and identifies vulnerabilities. This intermediary handles the time-consuming manual tasks while maintaining high accuracy through advanced natural language processing and code analysis capabilities.
Solution Approach 2:
The patent replaces the mechanical manual testing process with an automated AI-based system. Instead of developers manually creating test cases and automation scripts, the AI model performs these tasks automatically by analyzing the software program and generating appropriate test artifacts. This substitution dramatically reduces testing time while maintaining reliability through sophisticated algorithms.
2Ease of operation
If manual testing processes are used, then complex requirement analysis is improved, but developer intervention and infrastructure management increase
Solution Approach 1:
The AI model performs self-service by automatically analyzing testing requirements, generating test cases, and creating automation scripts without requiring extensive manual intervention. The system independently processes the software program and produces comprehensive testing artifacts, reducing the need for developer involvement in routine testing tasks while simplifying infrastructure management through automated operations.
3Productivity
If automated testing is implemented, then testing speed is improved, but automation script generation and maintenance complexity increase
Solution Approach 1:
The AI model performs preliminary action by generating complete, ready-to-execute automation scripts as part of the initial test case creation process. Rather than requiring separate script generation and maintenance steps, the system proactively creates fully functional automation scripts that are immediately usable, thereby increasing testing speed while managing complexity through integrated generation.
Data Source
AI summary
An example operation may include one or more of executing tests on a software application via a test environment of a test platform and logging results of the tests in a log file, identifying a new feature to be added to the software application based on execution of a machine learning model on logged results of the tests stored in the log file, generating source code for the new feature to be added to the software application based on execution of a generative artificial intelligence (GenAI) model on the new feature and a repository of source code, and displaying the generated source code via a user interface of the software application. The example operation may further include an AI agent that performs an action based on the generated source code.