Automated user interface testing with machine learning
AssigneeFidelity Information Services, LLC
InventorsHarsh Sharma, Yogendra Singh Katheria, Seerajudeen Sheik Ahamed, Rajiv Ramanjani, Shefali Garg
Life of this patent
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Abstract
Systems and methods are provided for implementing automated user interface testing with integrated machine learning models. Systems and methods for detecting and preemptively correcting flow path errors are disclosed. Systems and methods for minimizing user input and optimizing testing efficiency are disclosed. A result dashboard is disclosed in which testing results and errors are displayed and a user may interact with interactive testing reports.
Claims21
-20. (canceled)
A system for implementing an automated user interface testing service, comprising:
- at least one processor;
- and
- at least one memory storing instructions that, when executed by the at least one processor, cause the system to perform operations comprising:
- training, by a computer, a neural network model based on input data to generate a trained neural network;
- receiving a uniform resource locator;
- extracting user interface data of a user interface element identified by the uniform resource locator;
- detecting an error in the extracted user interface data using the trained neural network;
- and
- based on detecting the error using the trained neural network, automatically correcting the error in the extracted user interface data, thereby updating the user interface element.
A method for implementing an automated user interface testing service, comprising:
- training, by a computer, a neural network model based on input data to generate a trained neural network;
- receiving a uniform resource locator;
- extracting user interface data of a user interface element identified by the uniform resource locator;
- detecting an error in the extracted user interface data using the trained neural network;
- and
- based on detecting the error using the trained neural network, automatically correcting the error in the extracted user interface data, thereby updating the user interface element.
A non-transitory computer-readable medium including instructions executable by one or more processors to carry out operations comprising:
- training, by a computer, a neural network model based on input data to generate a trained neural network;
- receiving a uniform resource locator;
- extracting user interface data of a user interface element identified by the uniform resource locator;
- detecting an error in the extracted user interface data using the trained neural network;
- and
- based on detecting the error using the trained neural network, automatically correcting the error in the extracted user interface data, thereby updating the user interface element.
Dependencies are inferred from the claim text.