Cloud Functional Test Execution Plan Automation
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Solution Overview
Problem
Verifying functional requirements in cloud-computing environments with container orchestration tools is labor-intensive and scales poorly with the number of resources and capabilities, requiring extensive manual labor for testing each combination of resources and capabilities.
Innovation Solution
A method and system that generate an execution plan for functional tests in cloud-computing environments by combining code segments for resource and capability definitions, using a declarative infrastructure provisioner to automate the process and reduce redundant programming, allowing for efficient testing of resources and capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual testing methods are used to verify functional requirements, then testing can be performed with simple systems, but the labor required increases exponentially as the number of resources increases
Solution Approach 1:
The system automatically generates test cases and executes tests without requiring manual intervention for each test scenario. The testing framework self-services by combining resource definitions and capability definitions to autonomously create and run appropriate tests, eliminating the need for manual test creation and execution.
Solution Approach 2:
The system dynamically adjusts testing parameters based on the number and types of resources present in the system. As resources are added or removed, the testing framework automatically modifies the test suite to match the current system state, maintaining comprehensive coverage without manual reconfiguration.
2Reliability
If manual test generation is performed for each resource-capability combination, then comprehensive testing coverage can be achieved, but the complexity and time required increases significantly
Solution Approach 1:
The system merges resource definitions and capability definitions to automatically generate comprehensive test cases. By combining these two definition types, the system creates a unified testing approach that covers all resource-capability interactions without requiring separate manual test creation for each combination.
Solution Approach 2:
The system performs preliminary actions by pre-defining resources and capabilities with their respective attributes and relationships. These preliminary definitions are stored and automatically processed to generate the complete test suite, saving time by preparing test data and structures in advance rather than creating them during test execution.
3Measurement precision
If specialized personnel manually create and execute tests, then accurate functional verification can be performed, but the process does not scale to large and complex systems
Solution Approach 1:
The system replaces the mechanical process of manual test creation and execution with an automated computing system. The automated framework uses algorithms to generate, manage, and execute tests based on system definitions, eliminating the need for specialized personnel to manually handle each test while maintaining verification accuracy.
Solution Approach 2:
The testing framework is designed to be universal and applicable to systems of any size or complexity. It handles diverse resource types and capability definitions through a unified approach, allowing the same system to test everything from simple to highly complex configurations without requiring different methodologies or additional specialized personnel.
Data Source
AI summary
Techniques are disclosed for generating an execution plan for performing functional tests in a cloud-computing environment. Infrastructure resources and capabilities (e.g., system requirements) may be defined within an infrastructure object (e.g., a resource of a declarative infrastructure provisioner) that stores a code segment that implements the resource or capability. Metadata may be maintained that indicates what particular capabilities are applicable to each infrastructure resource. Using the metadata, the system can generate an execution plan by combining code segments for each resource with code segments defining each capability in accordance with the metadata. The execution plan may include programmatic instructions that, when executed, generate a set of test results. The system can execute instructions that cause the set of test results to be presented at a user device.