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.

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.

Abstract: Methods and systems for autonomous testing of a computer application include receiving analytic data associated with at least one application programming interface (API) flow, wherein an API flow of the at least one API flow includes at least one API; determining response data of the at least one API by inputting the analytic data to a prediction model determined based on a first machine learning technique; determining a subset of the at least one API flow based on the response data and input data representing at least one of a priority level or a risk level of the at least one API flow; and outputting the subset of the at least one API flow for execution.

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.

Abstract: Methods and systems for autonomous testing of a computer application include receiving analytic data associated with at least one application programming interface (API) flow, wherein an API flow of the at least one API flow includes at least one API; determining response data of the at least one API by inputting the analytic data to a prediction model determined based on a first machine learning technique; determining a subset of the at least one API flow based on the response data and input data representing at least one of a priority level or a risk level of the at least one API flow; and outputting the subset of the at least one API flow for execution.

Abstract: A route tracing request packet is generated comprising a time-to-live value, a source address of a source of the route tracing request packet, and an address of a destination of the route tracing request packet. The source and destination are in the virtual network; the route tracing request packet is usable to identify the virtual appliance, and the virtual appliance is configured to examine the route tracing request packet for a time-to-live value indicating that the route tracing request packet has expired and sending a time-to-live exceeded message to the source address. The time-to-live exceeded message comprises an identifier for the virtual appliance. The route tracing request packet is forwarded to the destination. The time-to-live exceeded message is received. Data is extracted to determine network virtual appliances that were traversed by the route tracing request packet prior to expiration of the time-to-live. The network virtual appliances are reported.

Abstract: A route tracing request packet is generated comprising a time-to-live value, a source address of a source of the route tracing request packet, and an address of a destination of the route tracing request packet. The source and destination are in the virtual network; the route tracing request packet is usable to identify the virtual appliance, and the virtual appliance is configured to examine the route tracing request packet for a time-to-live value indicating that the route tracing request packet has expired and sending a time-to-live exceeded message to the source address. The time-to-live exceeded message comprises an identifier for the virtual appliance. The route tracing request packet is forwarded to the destination. The time-to-live exceeded message is received. Data is extracted to determine network virtual appliances that were traversed by the route tracing request packet prior to expiration of the time-to-live. The network virtual appliances are reported.