Micro Focus Lcc

Entit Software LLC

Avi Kabizon

Haim Shuvali

Ira Cohen

Amichai Nitsan

Ohad Assulin

Guy Offer

Michael Seldin

Meir Ron

Roy Wallerstein

Noam Mor

Shai Embon

Maria Dermer

Shalom Kramer

Rudy Medarov

Anat Levinger

Examples relate to selectively testing applications. In one example, a computing device may: receive, from a testing device, a current state of an application under test (AUT); select a user action flow from a plurality of user action flows, each user action flow including a plurality of user actions previously performed on the AUT when the AUT was in a prior state that corresponds to the current state; and provide the selected user action flow to the testing device.

Selective application testing

Examples include a user segmentation system for staged application rollout. In an example, an application usage engine receives data for users including a number of screens used in an application version and a number of user actions used in the application version. A staged rollout engine determines, for each user, an early adopter probability based on the number of screens used in the application version and the number of user actions used in the application version, and in some examples a preconfigured factor or weighting. For a subsequent application version, staged rollout groupings are determined. Users are assigned to staged rollout groupings based on the early adopter probabilities.

Staged application rollout

A plurality of completion times associated with an application request may be obtained. The plurality of completion times may include a first completion time and a second completion time. A plurality of response times associated with a first asynchronous operation triggered by the application request may be obtained. The plurality of completion times may include a first response time associated with the first completion time and a second response time associated with the second completion time. A first correlation score may be determined describing an effect of the first asynchronous operation on the application request based on the first completion time, the second completion time, the first response time, and the second response time. Visualization data may be generated representing the first correlation score.

Effect of operations on application requests

In one implementation, a system for user action duration includes a monitor engine to monitor a number of async operations to be loaded on a user interface, a detection engine to determine a repeating async operation from the number of async operations that has a pattern of repeating timeouts, a removal engine to remove the repeating async operation from the number of async operations, and a calculation engine to calculate a total time for the remaining number of async operations based on a total timeout of each of the remaining number of async operations.

User action duration

In one example of the disclosure, event notices are received, with each notice indicative of degradation of a configuration item. Configuration item past-preference pairings are accessed. Each pairing includes a count of operator-exhibited preferences for event notices associated with a first configuration item relative to event notices associated with a second configuration item. A prioritized ordering of the received event notices is created utilizing the past-preference pairings.

Prioritizing event notices utilizing past-preference pairings

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Yehud, Israel

Avi Kabizon