Generative AI Test Data Orchestration with Homomorphic Encryption
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Solution Overview
Problem
Conventional test data generation processes are inadequate in terms of productivity, precision, scalability, and agility, leading to increased manual effort, prolonged wait times, and delays in test execution, with a lack of self-serve capabilities and linkage between data in lower lanes and internal development environments.
Innovation Solution
A system utilizing generative artificial intelligence (AI) and homomorphic encryption to automate test data generation, orchestrate data requests, and secure data transmission through a distributed blockchain network, incorporating an orchestration rule engine and dynamic smart contract builder for efficient and secure test data provisioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual coordination with different teams is used for test data creation, then data security and control are maintained, but productivity decreases and wait time increases
Solution Approach 1:
The system enables self-service test data generation through an automated framework where test teams can independently request and receive test data without manual coordination. The generative AI system processes requests autonomously, creating test data on-demand while maintaining security protocols, thereby eliminating the need for manual team coordination and significantly improving productivity.
Solution Approach 2:
The patent replaces the mechanical manual coordination process with an automated digital system. The orchestration rule engine and generative AI automatically manage test data requests, validation, and generation, substituting human-to-human coordination with machine-to-machine automation. This substitution maintains security controls while dramatically reducing wait times and improving throughput.
2Stability of the object's composition
If conventional test data generation processes are used, then existing manual workflows are maintained, but scalability is limited and test execution time increases
Solution Approach 1:
The system introduces dynamic capabilities through the orchestration rule engine that can adapt to different test data requests in real-time. The generative AI dynamically generates test data based on specific requirements, and the system can scale resource allocation according to demand. This dynamic architecture maintains workflow consistency through standardized processes while enabling scalability to handle varying workloads and complex test scenarios.
Solution Approach 2:
The automated test data generation framework serves multiple functions: it generates test data, validates requests, orchestrates resource allocation, and integrates with existing DevOps pipelines. This universal system replaces multiple specialized manual processes with a single multi-functional platform that maintains consistency across different test scenarios while scaling to accommodate diverse and growing test requirements.
3Manufacturing precision
If manual test data creation and conditioning is performed, then control over data quality is maintained, but precision and test execution efficiency decrease
Solution Approach 1:
The system implements feedback mechanisms where the orchestration rule engine continuously monitors test data requests, validates requirements, and adjusts generation parameters in real-time. The generative AI receives feedback from validation results and iteratively improves test data quality. This automated feedback loop maintains precise quality control while eliminating the time delays associated with manual review and conditioning processes.
Solution Approach 2:
The system performs preliminary validation and preparation of test data requests before actual generation. The orchestration rule engine pre-validates requirements, checks resource availability, and configures generation parameters in advance. This preliminary action ensures quality standards are met before test data creation begins, reducing rework and accelerating the overall test execution timeline by eliminating sequential manual approval steps.
Data Source
AI summary
A method for generating and orchestrating software test data for use with distributed DevOps is provided. The method leverages generative AI and homomorphic encryption. The method includes receiving, at an AI engine, a test data request; receiving from an API interface information for responding to the test data request; receiving a plurality of requirement-based knowledge graphs; transferring shared knowledge information from the AI engine to the orchestration rule engine in order to perform a threshold check, to validate a type of the test data; and to pull a plurality of rules from a rule mapper for use in engaging a dynamic smart contract builder. The method includes creating a smart contract for transmitting the test data request to nodes selected from a distributed blockchain of blockchain nodes and for receiving one or more responses to the request for test data and then forwarding the responses to the API interface.