Dynamic API Test Generation for Multi-Tenant SaaS Instances
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Solution Overview
Problem
API testing for multi-tenant software-as-a-service applications faces challenges due to the inability to run successfully across multiple instances with different configurations and customizations, leading to data format issues and errors, as existing tests are typically designed for specific instances and fail to cover diverse customer instances.
Innovation Solution
A dynamic test case generation framework that utilizes API metadata and optional test seed data to generate test data for any instance, employing machine learning and large language models to ensure compatibility and correctness across various instances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If API tests are designed for specific instances with fixed configurations, then test reliability for that instance is improved, but adaptability across multiple tenant instances deteriorates
Solution Approach 1:
The patent applies dynamics by transforming static, instance-specific test cases into dynamic, adaptive test cases. The test case generation system dynamically creates test cases based on instance metadata and configuration, allowing the same test framework to adapt to different tenant instances with varying configurations, customizations, and data formats without requiring manual modification of test cases for each instance.
Solution Approach 2:
The patent utilizes parameter changes by modifying test case parameters based on instance-specific metadata. The system changes test parameters such as data formats, validation rules, and expected responses according to the specific instance configuration, enabling reliable testing across diverse instances while maintaining adaptability through parameterization rather than hardcoding.
2Reliability
If manual test case creation is used for each instance, then test coverage for specific instances is improved, but productivity and resource efficiency deteriorate
Solution Approach 1:
The patent implements self-service by enabling the test case generation system to automatically create test cases for instance B and other tenant instances without requiring manual intervention. The system autonomously retrieves instance metadata, generates appropriate test cases, and executes them, significantly improving productivity while maintaining comprehensive test coverage across all instances.
Solution Approach 2:
The patent applies copying by using template-based test case generation where test cases for different instances are copied and adapted from standardized templates. This allows rapid creation of instance-specific test cases by copying and modifying parameters rather than creating tests from scratch, thereby improving both coverage and efficiency.
3Measurement precision
If instance-specific test cases are maintained, then testing accuracy for each instance is improved, but device complexity and maintenance burden increase
Solution Approach 1:
The patent implements universality by creating a single, unified test case generation framework that serves all tenant instances. This universal system generates instance-specific test cases automatically based on shared metadata templates, eliminating the need to maintain separate test case repositories for each instance while preserving testing accuracy through parameterized adaptation.
Solution Approach 2:
The patent utilizes feedback mechanisms where the system continuously monitors test execution results and instance metadata changes. This feedback enables the system to automatically update and refine test cases, maintaining high accuracy while reducing manual maintenance complexity through automated adjustments based on real-time instance configurations.
Data Source
AI summary
In an example embodiment, a singular test is used to validate a user entity data model for any type of instance in an efficient manner. This results in an Entity-Agnostic test. This approach provides tremendous savings from test implementation, support, data storage, and test triaging perspective, as well as being able to always validate the functional correctness of the data model for any customer/user.