API Validation via AI Feature Extraction and Tangle Consensus
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Solution Overview
Problem
The development, testing, and validation of application programming interfaces (APIs) require significant time and computing resources and are prone to errors.
Innovation Solution
A computing platform equipped with an AI engine trained on historical data to extract features, generate similarity scores, and solve a correlation matrix for new APIs, integrating a tangle consensus algorithm for validation, enabling efficient and error-reducing API development and validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional manual methods are used for API development, testing, and validation, then developers have full control over the process, but the process requires significant time and computing resources and is prone to errors
Solution Approach 1:
The system enables self-service automation where the API validation process automatically extracts features, generates test cases, executes tests, and produces validation reports without requiring manual intervention at each step, thereby reducing both time consumption and human error while maintaining validation quality
Solution Approach 2:
The patent replaces manual mechanical processes (developers manually writing test cases, manually executing tests, manually analyzing results) with an automated computing system that uses AI/ML models to extract features, generate test cases algorithmically, and automatically execute and analyze test results, significantly reducing time and error rates
2Reliability
If comprehensive testing and validation are performed manually, then validation thoroughness is improved, but computing resources and time requirements increase significantly
Solution Approach 1:
The system changes the parameters of the validation process by using AI/ML models to dynamically adjust test case generation parameters, feature extraction parameters, and validation thresholds based on the specific API being validated, enabling comprehensive validation with optimized resource usage rather than fixed resource-intensive manual processes
Solution Approach 2:
The system creates and executes multiple test case copies automatically generated from feature extractions, allowing comprehensive validation through parallel test execution rather than sequential manual testing, thereby improving validation thoroughness while maintaining resource efficiency through automated parallel processing
3Productivity
If automated validation systems are implemented, then time and resource efficiency are improved, but system complexity increases
Solution Approach 1:
The validation system is segmented into distinct modular components: feature extraction module, test case generation module, test execution module, and result analysis module. Each module performs a specific function and can be independently configured and maintained, reducing overall system complexity while maintaining high validation efficiency through modular automated processing
Data Source
AI summary
Arrangements for developing an application programming interface (API) are provided. A computing platform may train an artificial intelligence (AI) engine. The computing platform may receive a request to develop a new API. The computing may extract features of the new API. The computing platform may generate one or more similarity scores based on similarities between the extracted features of the new API and corresponding similar historical API features. The computing platform may output the new API based on solving a correlation matrix. The computing platform may test the API and output a validation score. The computing platform may deploy the API based on the validation score being equal to or exceeding a threshold.