API Gateway Knowledge Graph for Test Failure Prediction
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Solution Overview
Problem
API functional testing is hindered by the lack of linkage between assets in a test developer's ecosystem, making it difficult to identify faulty assets that can cause test failures, leading to inefficiencies in monitoring and testing processes.
Innovation Solution
A system that generates a knowledge graph by aggregating test-result graphs with metadata-linked graphs to identify functional relationships between assets, using machine learning to predict test failures and trigger alerts for potential issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional API functional testing is performed without asset linkage, then testing can be conducted with simple tools, but faulty assets cannot be identified in advance and test failures cannot be predicted
Solution Approach 1:
The system performs preliminary actions by generating a knowledge graph that links test assets to API specifications and implementations before testing begins. This advance linkage enables prediction of test failures and identification of faulty assets, resolving the contradiction by preparing the testing infrastructure in advance rather than reacting to failures during execution.
Solution Approach 2:
The patent introduces a knowledge graph as an intermediary structure that connects test assets, API specifications, and implementations. This mediator enables the system to trace relationships between assets and predict test outcomes, thereby improving reliability without requiring fundamental changes to the testing tools themselves.
2Loss of information
If assets are linked in a knowledge graph to enable impact analysis, then faulty assets can be identified in advance, but the system complexity increases significantly
Solution Approach 1:
The system segments the asset ecosystem into discrete, linkable entities (test assets, API specifications, implementations) that can be individually managed and connected in the knowledge graph. This segmentation reduces complexity by making each component manageable while enabling comprehensive impact analysis through their structured relationships.
Solution Approach 2:
The knowledge graph provides feedback mechanisms that automatically identify faulty assets and their impacts on test outcomes. This feedback loop reduces the perceived complexity by automating the analysis process, allowing the system to handle complex relationships without proportionally increasing operational burden.
3Loss of time
If comprehensive asset linkage is implemented, then impact analysis can be performed, but testing time and resource requirements increase
Solution Approach 1:
By performing preliminary linkage of assets in the knowledge graph before testing begins, the system enables rapid impact analysis during test execution. This preliminary preparation reduces testing time by eliminating the need for complex analysis during the testing phase itself.
Solution Approach 2:
The system implements selective asset linkage in the knowledge graph, focusing on critical relationships that have the greatest impact on test outcomes. This partial action approach maintains productivity by avoiding unnecessary linkage of all possible assets while still enabling effective impact analysis for the most relevant components.
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments for self-paced migration of an application programming language (API) gateway. An embodiment operates by receiving a functional test suite corresponding to an application programming interface (API). The embodiment generates a knowledge graph of information by combining a test result graph of information based on a result of execution of one or more tests in the functional test suite with a metadata linked graph of information based on metadata corresponding to the one or more APIs. The embodiment then generates an alert message corresponding to the function test suite based on the knowledge graph of information.