Automated Testing Scenario Generation for Cloud Application Performance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Performance and quality assurance testing of complex cloud-based applications is resource-intensive and time-consuming, requiring significant computing resources and hours to complete, especially when dealing with numerous and complex applications.
Innovation Solution
The implementation of an optimized testing scenario that monitors service calls across multiple applications, generates a service request tree, removes cyclic dependencies, and focuses on critical paths to reduce testing time and resource consumption, utilizing the Open Data Protocol (ODATA) and tools like OPT-AI for efficient data processing and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive performance testing is conducted across all applications, then testing coverage and quality assurance are improved, but testing time and resource consumption increase significantly
Solution Approach 1:
The patent segments the monolithic testing process into modular testing scenarios that can be independently configured and executed. Each testing scenario targets specific applications or service calls, allowing selective testing rather than comprehensive testing of all applications. This segmentation enables organizations to prioritize critical applications and reduce overall testing time while maintaining essential coverage.
Solution Approach 2:
The patent implements partial action by allowing users to execute only a subset of testing scenarios based on priorities, resource availability, and risk assessment. The system supports running critical path scenarios first, then progressively executing additional scenarios if resources permit. This approach ensures that essential testing coverage is achieved with reduced time and resource investment compared to exhaustive testing.
2Reliability
If comprehensive performance testing is conducted across all applications, then testing coverage and quality assurance are improved, but computing resource consumption increases significantly
Solution Approach 1:
The testing system is divided into independent, configurable scenarios that can be selectively executed. Each scenario targets specific applications, service calls, or functional areas, allowing resource allocation to be optimized based on business priorities rather than uniformly distributing resources across all applications. This enables reduced resource consumption while maintaining coverage of critical systems.
Solution Approach 2:
The patent allows dynamic adjustment of testing parameters such as concurrency levels, data volumes, and execution priorities. By changing these parameters based on resource availability and application criticality, the system can optimize resource consumption while maintaining adequate testing coverage. Less critical applications can be tested with lower resource allocation.
3Reliability
If service request trees include cyclic dependencies, then comprehensive testing paths are captured, but testing efficiency decreases due to redundant component execution
Solution Approach 1:
The patent extracts and identifies cyclic dependencies in the service request tree and removes them from the execution path. By detecting cycles where the same service or component would be called multiple times through different paths, the system eliminates redundant executions. This maintains testing completeness by ensuring all unique service calls are tested, while improving efficiency by avoiding repeated testing of the same components.
Solution Approach 2:
The system discards redundant test paths that create cyclic dependencies while recovering and preserving the essential testing coverage. By analyzing the service request tree to identify and remove cycles, the system maintains coverage of all necessary service calls and functional paths without the inefficiency of repeated executions. The unique testing requirements are retained while eliminating wasteful redundancy.
Data Source
AI summary
A testing scenario (forming part of a computing environment executing a plurality of applications) is initiated to characterize performance of the applications. During the execution of the testing scenario, various performance metrics associated with the applications are monitored. Thereafter, data characterizing the performance metrics is provided (e.g., displayed, loaded into memory, stored on disk, transmitted to a remote computing system, etc.). The testing scenario is generated by monitoring service calls being executed by each of a plurality of automates across the applications, generating a service request tree based on the monitored service calls for all of the applications, and removing cyclic dependencies in the service request tree such that reusable testing components are only used once. Related apparatus, systems, techniques and articles are also described.