Cloud Service API Testing Through Resource-Based Dependency Graphs
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Solution Overview
Problem
Conventional test methods for cloud services with microservice architectures suffer from low accuracy in identifying API dependency relationships due to inconsistent API parameter-naming styles, leading to inadequate vulnerability detection in distributed systems.
Innovation Solution
Constructing an API knowledge graph based on the reference relationship between API parameters and resource objects to accurately identify dependencies, using a cloud service test system that extracts knowledge from various data sources and applies AI models for iterative test case generation and optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If API dependency relationship is identified by matching parameter names, then the test can be performed, but the identification accuracy is low due to inconsistent naming styles
Solution Approach 1:
The patent introduces an API knowledge graph as an intermediary structure that stores standardized API definitions and parameter mappings. Instead of directly matching parameter names between APIs (which fails due to naming inconsistencies), the system uses the knowledge graph as a mediator to resolve dependencies through standardized identifiers and semantic relationships, thereby achieving both high accuracy and universality across different services with varying naming conventions
Solution Approach 2:
The patent transforms the dependency identification approach from direct parameter name matching to identifier-based matching through parameter standardization. By changing the matching parameter from human-readable names (which vary) to standardized unique identifiers (which are consistent), the system resolves the contradiction between accuracy and universality
2Reliability
If conventional parameter name matching method is used, then the test can be executed, but the vulnerability detection is inadequate
Solution Approach 1:
The patent performs preliminary construction of an API knowledge graph before vulnerability testing. This knowledge graph pre-stores standardized API definitions, parameter mappings, and dependency relationships extracted from service code and documentation. By preparing this structured knowledge base in advance, the system enables more reliable and accurate vulnerability detection during actual testing, rather than attempting to parse and match parameters on-the-fly
3Device complexity
If subjective test design by testers is used, then the test can be performed, but it is insufficient for complex distributed systems
Solution Approach 1:
The patent implements an automated system that extracts API definitions, parameter information, and dependency relationships directly from service code, documentation, and interface descriptions without requiring manual test design. The system automatically builds the API knowledge graph and generates test cases, enabling it to handle complex distributed systems with thousands of microservices that would be impractical to test manually
Data Source
AI summary
This application provides a cloud service test method, including: constructing an application programming interface API knowledge graph of a cloud service, where the API knowledge graph includes a reference relationship between an API parameter and a resource object; then identifying an API dependency relationship based on the reference relationship between an API parameter and a resource object; and then testing the cloud service based on the API dependency relationship, to obtain a test result. In the method, the API dependency relationship is identified based on the reference relationship between an API parameter and a resource object. Even if API parameter names do not match, the API dependency relationship can be accurately identified based on a same pointed resource object, thereby improving accuracy of a cloud service test. The method is universal and can meet a service requirement.