Unified Test Automation System for CI/CD
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Solution Overview
Problem
Current IT system testing approaches are often manual, labor-intensive, and focused on specific devices or functions, making it difficult to effectively test overall system performance and scalability over time, especially in complex environments with frequent hardware and code revisions.
Innovation Solution
The implementation of a Unified Test Automation System (UTAS) that integrates automated build, test, and deployment tools to automatically select and execute test suites based on code attributes, store results, and provide analytics, using tools like Atlassian JIRA and Slack for defect tracking and communication, ensuring comprehensive testing across CI/CD environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual testing approaches are used with large suites of tests, then testing coverage can be comprehensive, but the amount of manpower required and time consumption increases significantly
Solution Approach 1:
The patent replaces manual mechanical testing operations with automated testing systems. Automated test execution engines run test suites without human intervention, automatically selecting tests based on code attributes and executing them against builds. This substitution eliminates the need for manual test execution while maintaining comprehensive testing coverage, directly resolving the contradiction between coverage and manpower efficiency.
Solution Approach 2:
The testing system performs self-service by automatically selecting appropriate test suites based on code attributes, executing tests, and generating reports without requiring manual intervention. The system autonomously manages the entire testing workflow from test selection to result analysis, enabling comprehensive testing while minimizing human resource requirements.
2Measurement precision
If focused testing on specific devices or functions is performed, then testing depth for particular components improves, but overall system performance and scalability testing becomes difficult
Solution Approach 1:
The patent implements a universal testing framework that can handle multiple testing objectives simultaneously. The system selects and executes different types of tests (functional, performance, scalability) based on code attributes, enabling both deep component testing and broad system-wide testing through a single automated platform. This multi-functionality resolves the contradiction by making the testing system adaptable to various testing scopes.
Solution Approach 2:
The testing system dynamically adapts its behavior based on code attributes. It automatically adjusts which test suites to execute, how many times to run them, and what resources to allocate, allowing the same system to provide both deep focused testing and broad system-wide testing depending on the specific code being tested.
3Productivity
If automated testing tools are introduced to improve efficiency, then testing speed and consistency improve, but integration complexity and system coordination requirements increase
Solution Approach 1:
The patent merges multiple testing tools and functions into a unified automated testing system. By integrating test selection, execution, reporting, and artifact management into a single coordinated system, it reduces the complexity that would otherwise arise from coordinating multiple separate tools, while maintaining high testing efficiency through automation.
4Reliability
If comprehensive automated testing is implemented to cover all test types, then system reliability and defect detection improve, but test execution time and resource consumption increase
Solution Approach 1:
The patent applies partial action by selectively executing only the necessary test suites based on code attributes. Rather than running all possible tests regardless of context, the system intelligently selects appropriate tests, running sufficient tests to ensure reliability while avoiding unnecessary execution time and resource consumption.
Solution Approach 2:
The system changes testing parameters dynamically based on code attributes. It adjusts test selection criteria, execution frequency, and resource allocation according to the specific characteristics of the code being tested, enabling comprehensive defect detection while optimizing execution time through parameter adaptation.
Data Source
AI summary
An automated testing framework to coordinate functions among code management, build management, automated test, resource reservation, artifact repositories and team communication subsystems. In one embodiment specific to software development, software developers check new code into a code management subsystem, a development project tracking system, or other tools that automatically generate a build. Test suites are then automatically selected and executed based on a scope of the project or code that was built. This scope can include such attributes as what portion of the software was built, the purpose (objective) of the build, the maturity of developing that build, and so forth. In one implementation, label directives may be used to associate build scope to test suites. During or after the automated tests, other actions may then be automatically triggered, to store test results, inform the development team, stored data integrated with test definition and results, or update an artifact repository.