Testing Interface for Multi-Layer Pod Data Validation
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Solution Overview
Problem
Testing computer applications with multiple integration touch points and internal components that integrate with multiple infrastructure layers is complex and time-consuming, requiring technical resources to understand each layer's functionality to write effective test cases.
Innovation Solution
A system and method for testing components or scenarios with execution history, involving a testing interface that receives test identification, data layers, and testing parameters, executes tests, retrieves results, and outputs them, with features like generating synthetic data and training a machine learning engine to identify threats or peaks from test results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual testing is performed with detailed understanding of each layer, then test accuracy and reliability are improved, but testing time and complexity increase significantly
Solution Approach 1:
The system enables self-service testing where the testing interface automatically executes tests against the pod without requiring manual intervention or deep technical knowledge. The interface handles test case generation, parameter configuration, and result analysis automatically, allowing users to conduct reliable tests without time-consuming manual setup.
Solution Approach 2:
The testing interface acts as an intermediary layer between the user and the complex pod infrastructure. It abstracts the complexity of multiple data layers (collection, ingestion, messaging, enrichment, connect) by providing a simplified interaction model that automatically manages the testing process across all layers.
2Reliability
If comprehensive testing across multiple data layers is performed, then system reliability is improved, but device complexity and operational difficulty increase
Solution Approach 1:
The testing interface provides universal functionality to test all data layers (collection, ingestion, messaging, enrichment, connect) through a single unified interface. This multi-functional design eliminates the need for separate testing mechanisms for each layer, reducing overall system complexity while maintaining comprehensive coverage.
Solution Approach 2:
The pod is segmented into distinct data layers (collection, ingestion, messaging, enrichment, connect), each with its own testing capabilities exposed through the unified interface. This segmentation allows independent testing of each layer while presenting a simplified overall structure to the user.
3Productivity
If automated testing with synthetic data generation is implemented, then testing efficiency is improved, but system resource consumption increases
Solution Approach 1:
The system generates synthetic test data in advance before executing tests against the pod. This preliminary data generation allows the testing interface to quickly set up test scenarios without consuming resources during the actual test execution, improving overall testing efficiency while managing resource usage.
Data Source
AI summary
Systems and methods for testing components or scenarios with execution history are disclosed. A method may include: receiving, at a testing interface and from an application or program executed by a user electronic device, an identification of a test and one or more data layers of a plurality of data layers in pod to test, the plurality of data layers including a data collection layer, a data ingestion layer, a data messaging layer, a data enrichment layer, and a data connect layer; receiving, by the testing interface, a selection of testing parameters or values for the identified test; retrieving, by the testing interface, the identified test; executing, by the testing interface, the identified test on the identified one or more data layers using the selected testing parameters or values; retrieving, by testing interface, results of the execution of the test; and outputting, by the testing interface, the results.