Test Automation Platform for Data Ingestion Pipelines
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing test automation frameworks for software development projects, particularly for data ingestion pipelines, are error-prone, time-consuming, and resource-intensive due to static mapping issues and interruptions in continuous integration and continuous delivery (CI/CD) environments, leading to inefficiencies and potential data loss.
Innovation Solution
A test automation platform utilizing machine learning models for analyzing test cases, generating mapping files, and predicting applicable test scenarios, enabling automated execution of test scripts without interrupting data ingestion pipeline operations, and providing reporting and deployment functionalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a test framework is reconfigured from a first software development project to a second software development project, then flexibility in test automation is improved, but extensive re-writing of test cases and recalibration of success conditions is required
Solution Approach 1:
The patent uses machine learning models to automatically copy and adapt test cases from one project to another. The test intent identification model and test entity identification model analyze existing test cases and generate new test cases for different projects, eliminating manual re-writing and enabling rapid framework reconfiguration.
Solution Approach 2:
The test automation system performs self-service by automatically generating test cases and mapping files without human intervention. The machine learning models autonomously analyze project requirements, identify test intents, and create appropriate test cases, reducing the time and effort required for framework adaptation.
2Reliability
If testing is performed on data ingestion pipelines in CI/CD environments, then deployment reliability is improved, but interruptions in continuous integration and continuous delivery occur
Solution Approach 1:
The patent implements periodic action by scheduling tests to run at specific intervals or triggers rather than continuously blocking the CI/CD pipeline. Tests can be executed periodically on new data ingestion pipelines without preventing continuous integration and delivery of other components, maintaining both reliability and productivity.
Solution Approach 2:
The testing process is segmented into independent, modular test cases that can be executed selectively. The mapping file associates specific test cases with data ingestion pipelines, allowing only relevant tests to run when changes are detected, rather than blocking the entire CI/CD process.
3Measurement precision
If manual test case execution is performed on data ingestion pipelines, then testing accuracy is improved, but error-proneness and resource-intensiveness increase
Solution Approach 1:
The patent replaces manual mechanical testing operations with automated machine learning-based test generation and execution. The test intent identification model and test entity identification model automatically analyze and execute tests, eliminating human error while maintaining high accuracy through systematic, repeatable processes.
Solution Approach 2:
The patent introduces machine learning models as intermediaries between project requirements and test case generation. These models act as intelligent mediators that automatically translate project specifications into appropriate test cases, reducing both human resource requirements and error-proneness while maintaining testing accuracy.
Data Source
AI summary
A test automation platform may receive a test case for testing a data ingestion pipeline. The test automation platform may determine a test intent of the test case using a test intent identification model. The test automation platform may determine a type of entity associated with the data ingestion pipeline that the test case is to test using a test entity identification model. The test automation platform may generate a prediction of a data ingestion scenario for which the test case is to be applied based at least in part on the test intent of the test and the type of entity. The test automation platform may generate a mapping file representing the prediction of the data ingestion scenario. The test automation platform may execute, using test cases of the mapping file, a set of tests on a new data ingestion pipeline.