ML-Based Desktop App Testing System
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Solution Overview
Problem
There is a need for an efficient and intelligent way to perform automated testing of complex desktop applications, as existing methods struggle with end-to-end testing of complex functions and often result in false failures due to real-time changes in input data.
Innovation Solution
A system that combines process automation technologies with artificial intelligence/machine learning capabilities to perform end-to-end testing of desktop applications. This system includes a test automation component that produces real runtime outputs and an artificial intelligence engine that generates predicted outputs based on historical data, analyzing the delta between real and predicted outputs to iteratively improve the machine learning model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional automated testing methods are used for complex desktop applications, then testing coverage can be achieved, but false failures occur due to real-time changes in input data
Solution Approach 1:
The system performs preliminary actions by generating predicted output values before actual testing using machine learning models trained on historical data. These predictions are prepared in advance to serve as reference points for comparing against real-time test outputs, enabling the system to anticipate expected behaviors and reduce false failures caused by real-time data variations.
Solution Approach 2:
The system implements feedback mechanisms by continuously comparing predicted output values with actual test output values. When discrepancies are detected, the system analyzes the delta between predicted and actual values, and this feedback is used to iteratively improve the machine learning models, thereby reducing false failures over time.
2Reliability
If end-to-end testing of complex functions is performed, then comprehensive testing coverage is achieved, but testing efficiency decreases due to the complexity of managing test processes
Solution Approach 1:
The system enables self-service by allowing machine learning models to automatically generate predicted output values without manual intervention. The models serve themselves by learning from historical test data and autonomously providing expected output references for future tests, eliminating the need for manual setup of test expectations and improving testing efficiency while maintaining comprehensive coverage.
Solution Approach 2:
The system applies parameter changes by dynamically adjusting the complexity of testing based on the specific function being tested. The machine learning models adapt their prediction parameters according to the historical data available for each function, allowing the system to efficiently handle both simple and complex functions with appropriate testing depth without manual configuration.
3Reliability
If machine learning models are used to predict test outputs, then false failures are reduced, but the system complexity increases due to model training and maintenance
Solution Approach 1:
The system performs preliminary model training using historical test data before deployment. This preliminary action prepares the machine learning models in advance with pre-learned patterns and relationships, reducing the complexity of real-time operations. The models are trained offline on historical data to establish baseline predictions, simplifying the runtime system architecture.
Solution Approach 2:
The system uses copying by creating simplified representations of complex test scenarios through machine learning models. Instead of managing the full complexity of end-to-end test processes, the system copies essential input-output relationships into trained models that can quickly generate predictions, reducing system architecture complexity while maintaining prediction accuracy.
Data Source
AI summary
A system is provided for automated intelligent testing of desktop applications through machine learning. In particular, the system may combine process automation technologies with artificial intelligence/machine learning capabilities to perform end-to-end testing of both simple and complex applications. The system may comprise a test automation component that may produce real runtime outputs of the testing process, and an artificial intelligence engine that may produce predicted outputs based on historical data. The system may analyze both the real runtime outputs with the predicted outputs and determine a delta between the real runtime outputs and the predicted outputs. The results of the analysis may be backpropagated to the artificial intelligence engine to iteratively improve the machine learning model over time. In this way, the system may provide an efficient way to test computer applications.