API Model Inference via URL Path Tree Compaction
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Solution Overview
Problem
Existing methods for building and maintaining API models, particularly for RESTful APIs, are costly and challenging to keep in sync with reality, and there is a need for efficient methods to infer API models from observed API calls.
Innovation Solution
A computer-implemented method that observes a plurality of uniform resource locators (URLs), parses their paths into segments, creates a tree structure, determines a confidence factor using a decay function, and compacts sibling nodes into a compacted node when the number exceeds a variance threshold, to infer API models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional methods are used to build and maintain API models, then model accuracy can be maintained, but the cost and complexity of maintenance increase significantly
Solution Approach 1:
The system automatically infers API models by observing API calls and URLs without requiring manual intervention. The model building process is self-service, where the system serves itself by learning from observed traffic patterns, thereby reducing maintenance complexity while maintaining model accuracy
Solution Approach 2:
The system performs preliminary actions by continuously observing and analyzing API calls and URLs to proactively build and update models before they become outdated. This preliminary observation and analysis work prevents model drift and reduces the need for reactive maintenance
2Reliability
If manual methods are used to keep API models in sync with reality, then model relevance can be maintained, but time and resources are consumed
Solution Approach 1:
The system implements continuous observation of API calls and URLs, maintaining model relevance through uninterrupted monitoring. This continuous useful action ensures models stay synchronized with actual API usage patterns without requiring periodic manual updates, eliminating time loss while maintaining reliability
Solution Approach 2:
The system uses feedback from observed API calls and URLs to automatically update and refine models. This closed-loop feedback mechanism ensures models remain relevant by continuously adapting to changes in API usage patterns without consuming additional manual time or resources
3Manufacturing precision
If detailed tree structures are created from URL paths, then model precision improves, but structure complexity increases
Solution Approach 1:
The system merges sibling nodes in the tree structure that represent the same path segment, reducing structural complexity while preserving the precision needed for accurate API modeling. This consolidation maintains the necessary detail for model accuracy while simplifying the overall structure
Solution Approach 2:
The system segments URL paths into discrete path segments that form nodes in the tree structure. This segmentation approach maintains precision by capturing detailed path information while organizing it in a structured manner that can be efficiently managed and processed
Data Source
AI summary
A computer-implemented method includes observing, by a computer device, a plurality of uniform resource locators (URLs); parsing, by the computer device, a URL path of each of the URLs into path segments; creating, by the computer device, a tree structure, the tree structure having a plurality of nodes, each node representing a particular path segment of the path segments; determining, by the computer device, a confidence factor for a first path in the tree structure using a decay function, the first path having an end node; and compacting, by the computer device, sibling nodes of the nodes into a compacted node in response to a number of the sibling nodes exceeding a variance threshold.