Dynamic API Validation With Generative AI Test Generation
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Solution Overview
Problem
Large organizations face challenges in efficiently, effectively, and securely managing complex computer systems that exchange information with external systems, particularly due to cumbersome and error-prone API validation processes exacerbated by varying client metadata requirements and incomplete test coverage.
Innovation Solution
A system utilizing generative AI to dynamically generate test cases and data, combined with federated byzantine agreement, for comprehensive API validation, ensuring reliable and accurate testing across multiple nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual or partially automated rules test plans are used for API validation, then implementation simplicity is maintained, but test coverage is incomplete and error detection capability is reduced
Solution Approach 1:
The system performs preliminary actions by using Generative AI to predict API metadata, data patterns, and validation rules before actual API validation occurs. This pre-computation of test cases and validation criteria enables comprehensive test coverage without manual intervention during the validation process itself.
Solution Approach 2:
The patent introduces an intermediary layer consisting of the Generative AI model and FBA consensus mechanism that mediates between the API being validated and the validation process. This intermediary automatically generates test cases and validates results, resolving the contradiction between comprehensive testing and system complexity.
2Reliability
If comprehensive API validation with multiple test cases is implemented, then reliability and error detection are improved, but processing time and computational resources increase
Solution Approach 1:
The system implements periodic action through parallel processing of multiple test cases across distributed computing nodes. Instead of sequential validation, test cases are executed concurrently in periodic batches, reducing overall validation time while maintaining comprehensive test coverage through the FBA consensus mechanism.
3Reliability
If dynamic data and rules generation through FBA analysis is used, then test coverage and security detection are improved, but system complexity and computational overhead increase
Solution Approach 1:
The system applies self-service by enabling the Generative AI model to automatically generate test data and validation rules without external intervention. The FBA mechanism then autonomously executes these tests and reaches consensus on validation results, making the system self-sufficient in achieving comprehensive security detection.
Solution Approach 2:
The patent utilizes parameter changes by dynamically adjusting test case parameters, data patterns, and validation criteria based on API-specific characteristics predicted by the Generative AI model. This dynamic parameter adaptation enables comprehensive security testing tailored to each API without requiring a fixed complex test framework.
4Productivity
If parallel processing and distributed validation nodes are implemented, then processing efficiency and reliability are improved, but coordination complexity and consensus overhead increase
Solution Approach 1:
The FBA consensus mechanism serves multiple functions simultaneously: it coordinates distributed validation nodes, ensures data integrity, validates test results, and achieves agreement on pass/fail outcomes. This multi-functionality reduces the need for separate coordination systems, managing distributed complexity while maintaining high validation throughput.
Data Source
AI summary
Various aspects of the disclosure relate to automated testing for application programming interfaces (APIs). A dynamic API validation computing system leverages a generative AI model to dynamically generate a multitude of data sets and rules for use during API validation activities. A federated byzantine agreement mechanism performs the validation of each API using the generated test cases and test data. A generator engine incorporates a generative AI model that may be trained on a large corpus of API metadata and/or data characteristics to predict data patterns and/or validation rules for each of the APIs under test. The generator engine may also predict a structure and format of requests based on the training model inputs. Multiple Test cases for an API created through use of the generative AI model may be distributed and executed on different testing nodes that reach a consensus about whether each API has passed or failed.