Automated Continuous Validation for Dynamic Computer Systems
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Solution Overview
Problem
Current technologies are inadequate for automated validation of dynamic systems, such as AI and quantum computing systems, for regulatory compliance, as they rely on manual methods that are time-consuming, prone to operational delays, and biased, especially when dealing with learning-driven changes and knowledge representation updates.
Innovation Solution
A method and system for automated continuous validation that detects learning in dynamic components by analyzing system logs, generates a replica, performs real-time User Acceptance Testing using synthetic test data, and checks compliance through Receiver Operator Characteristic (ROC) curves, ensuring repeatability and consistency before rolling out changes in production.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual testing and validation methods are used for dynamic systems, then validation can be performed, but it is time-consuming and prone to operational delays
Solution Approach 1:
The patent replaces manual mechanical testing processes with an automated validation system that uses machine learning models to generate test cases, execute tests, and analyze results automatically. This substitution eliminates human-operated delays while maintaining validation thoroughness through systematic automated testing workflows.
Solution Approach 2:
The validation system performs self-service by automatically generating test cases from regulatory requirements, executing tests against the dynamic system, and producing validation reports without human intervention. The system autonomously manages the entire validation lifecycle, reducing operational delays while ensuring comprehensive coverage.
2Measurement precision
If manual data selection and batch running are used, then testing can be performed, but it introduces bias and operational delays
Solution Approach 1:
The system replaces manual data selection with automated algorithms that generate test data based on regulatory requirements and system specifications. This eliminates human bias in data selection while improving operational efficiency through automated data generation and batch processing workflows.
Solution Approach 2:
The validation system creates copies of regulatory requirements and transforms them into automated test cases. This copying process eliminates manual interpretation bias while maintaining fidelity to regulatory intent, and enables parallel processing of multiple test scenarios simultaneously.
3Reliability
If validation testing is performed on dynamic systems with learning components, then compliance can be checked, but it is almost impossible to manage by human-driven testing
Solution Approach 1:
The patent replaces human-driven testing management with an automated validation system that handles the complexity of testing dynamic systems with learning components. The system automatically adapts test cases to account for system changes, manages version control, and tracks compliance status without human intervention.
Solution Approach 2:
The validation system implements continuous feedback loops that monitor system changes, automatically update test cases, and re-execute validations when the dynamic system learns or changes. This feedback mechanism manages the complexity of validating evolving systems by continuously adapting the validation process.
4Measurement precision
If manual selection of test data and determination of acceptance is used, then validation can be performed, but it creates operational delays and bias
Solution Approach 1:
The system replaces manual acceptance determination with automated algorithms that compare test results against predefined acceptance criteria derived from regulatory requirements. This eliminates human bias in acceptance decisions while dramatically increasing validation throughput through automated parallel processing of multiple test cases.
Solution Approach 2:
The validation system transforms acceptance criteria from subjective manual judgments into objective quantitative parameters that can be automatically evaluated. By changing the parameter representation of acceptance criteria, the system enables automated, bias-free determination while increasing validation productivity.
Data Source
AI summary
A method and system for automated continuous validation for regulatory compliance of CS with dynamic component. On identification of learning in the CS, a User Acceptance Testing (UAT) is performed using automated test cases of varying types in accordance with what-if scenarios and synthetic data generated using a unique approach. Thereafter, a base validation testing of the CS is performed with clean data (positive scenarios of outcome of the CS) and dirty data (negative scenarios) by conducting repeatability, stability (consistency) and reliability checks. The base validation testing is then followed by learning saturation testing on only if the dynamic component is validated, is rolled out in production environment else is rolled back to the earlier version.