Service Integration Error Taxonomy for Microservice Testing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing integration error taxonomies are inadequate for modern systems with service components, particularly microservices and IoT systems, as they fail to account for distribution and integration into execution environments, leading to unforeseen integration errors and lack systematic approaches for classification and analysis.
Innovation Solution
A method using a system-specific integration error taxonomy is applied through a test device to identify and analyze integration errors by assigning relevant detection methods, generating a specialized taxonomy based on system and application information, and prioritizing error types for targeted testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing integration error taxonomies are used for monolithic or object-oriented systems, then classification of integration errors is provided, but they are insufficient for service-based systems as they do not account for distribution and integration into execution environments
Solution Approach 1:
The patent creates a specialized integration error taxonomy tailored specifically for service-based systems, giving different parts of the taxonomy different functions: one part handles general integration errors while another part specifically addresses distribution-related errors, communication errors, and execution environment integration errors. This local specialization resolves the contradiction by making the taxonomy adaptable to service-based systems while maintaining reliable error identification.
Solution Approach 2:
The taxonomy is designed to be dynamic and extensible, allowing new error types to be added as service-based systems evolve. The patent structures the taxonomy with hierarchical levels that can accommodate emerging error patterns in distributed systems, microservices, and cloud environments, thereby maintaining both adaptability and reliability over time.
2Reliability
If systematic approaches are used for integrating service components, then integration error identification is improved, but the complexity of creating and maintaining a specialized taxonomy increases
Solution Approach 1:
The integration error taxonomy is segmented into multiple hierarchical levels and categories. The patent divides errors into broad categories (such as communication errors, coordination errors, data errors) and further segments them into specific error types. This segmentation makes the taxonomy manageable and maintainable while providing systematic error identification capability.
Solution Approach 2:
The taxonomy is designed with universal structures and patterns that can be applied across different service-based systems regardless of their specific domain. The patent creates reusable error classification frameworks that can serve multiple purposes: error detection, error analysis, and improvement tracking, thereby reducing the complexity of maintaining the taxonomy while achieving reliable systematic error identification.
3Productivity
If experience-based or random test methods are used for integration, then testing can be performed quickly, but they are insufficient to take into account all potential sources of integration errors
Solution Approach 1:
The patent applies preliminary action by establishing a comprehensive integration error taxonomy before conducting integration tests. The taxonomy pre-defines all potential error sources in service-based systems, including distribution errors, communication errors, and execution environment errors. This preliminary classification framework guides the testing process to systematically cover all error sources while maintaining efficient test execution.
Solution Approach 2:
The taxonomy incorporates feedback mechanisms where test results are fed back into the taxonomy structure. The patent uses this feedback to refine and update the taxonomy, adding new error types discovered during testing and improving the classification of existing error types. This continuous feedback loop maintains both the speed and completeness of error detection over time.
Data Source
AI summary
Identifying integration errors in the integration of service components in a system based on a system-specific integration error taxonomy by a test device, wherein the test device stores a basic integration error taxonomy that defines integration error classes, wherein the respective integration error classes are assigned respective integration error types, including steps performed by the test device: performing an assignment method, wherein at least some of the integration error types in the basic integration error taxonomy are assigned respective integration error detection methods; receiving system information about the system; performing a specialization method, wherein a system-specific integration error taxonomy for examining the system depending on the system information is generated based on the basic integration error taxonomy; performing a selection method, wherein at least some of the integration error types in the system-specific integration error taxonomy are assigned respective ones of the integration error detection methods depending on system information.