Micro Focus Lcc

Entit Software LLC

Hewlett Packard Enterprise Development LP

Hewlett-packard Development Company, L.p.

Amichai Nitsan

Inbar Shani

Haim Shuvali

Michael Gopshtein

Yaron Burg

Ilan Shufer

Hana Giat

Sigal Maon

Eyal Kenigsberg

Alexei Ledenev

Olga Shershevsky

Aviad Israeli

Adi Lipin

Lior Manor

Eli Mordechai

Moshe Eran Kraus

Yair Horovitz

Meidan Zemer

Boaz Shor

Avi Kabizon

Guy Offer

Yael Peisachov

Gil Pearl

Avi Saidian

Lior Reuven

Meir Ron

Yuval Koren

Roy Nuriel

Yirat Hendler

Adam Spektor

Dana Gilboa

Alon Berkoviz

Iris Sasson

Gal Tunik

Example implementations relate to comparable UI object identifications. Some implementations may include a data capture engine to capture data points during test executions of the application under test. The data points may include, for example, test action data and application action data. Additionally, some implementations may include a data correlation engine to correlate each of the data points with a particular test execution of the test executions, and each of the data points may be correlated based on a sequence of events that occurred during the particular test execution. Furthermore, some implementations may also automatically identify, based on the correlated data points, a set of comparable UI objects.

Comparable user interface object identifications

Examples disclosed herein relate, among other things, to an apparatus that includes a processor and a non-transitory machine-readable medium on which is stored instructions that, when executed by the processor, cause the processor to generate a report, the report including a value, access a memory, the memory storing indications of whether values are to be masked, determine that the value is to be masked based on the accessed memory, and substitute the value in the report with a masked value.

Report comprising a masked value

Example implementations relate to determining potential test actions. Some implementations may include a data capture engine to capture data points during test executions of the application under test. The data points may include, for example, test action data and application action data. Additionally, some implementations may include a data correlation engine to correlate each of the data points with a particular test execution of the test executions, and each of the data points may be correlated based on a sequence of events that occurred during the particular test execution. Furthermore, some implementations may also include a test verification engine to determine, based on the correlation of the data points, a potential test action to perform during a future test execution of the application under test.

Determining potential test actions

Example implementations relate to test execution comparisons. Some implementations may include a data capture engine to capture data points during test executions of the application under test. The data points may include, for example, test action data and application action data. Additionally, some implementations may include a data correlation engine to correlate each of the data points with a particular test execution of the test executions, and each of the data points may be correlated based on a sequence of events that occurred during the particular test execution. Furthermore, some implementations may also automatically compare the test executions, based on the correlated data points, to identify commonalities.

Test execution comparisons

In one example of the disclosure, a user-defined success criterion for an application change is received. The criterion is provided to a computing system associated with a developer-user of the application. Evaluation code, for evaluating implementation of the change according to the criterion, is received from the computing system. The evaluation code is caused to execute responsive to receipt of a notice of production deployment of the change. A success rating for the change is determined based upon application performance data attained via execution of the evaluation code.

Determining application change success ratings

A method for evaluating user interface efficiency includes discerning user actions with the user interface occurring between each of a plurality of adjacent pairs of transaction points. Those adjacent pairs of transaction points are evaluated to identify a given one of a plurality of user flows associated with the user interface. The identified user actions occurring between each adjacent pair of transaction points are assessed to calculate an efficiency score. The efficiency score is associated with the identified user flow and is indicative of user interface efficiency.

Evaluating user interface efficiency

Examples disclosed herein relate, among other things, to a monitoring system. The monitoring system may include a report analyzer configured to receive a report from one of a plurality of devices and determine whether the report includes at least one masked value. If the report includes at least one masked value, the report analyzer may be configured to determine, based on a plurality of reports associated with the masked value, whether an unmasking condition is satisfied, and if the unmasking condition is satisfied, to mark the masked value for unmasking.

Evaluating user experience for an application includes collecting, for each of a plurality of sessions of the application, a list of user event data items experienced during that session. The list of user event data items includes any of a device utilization quantifier, user action-response time pairs, and an application fault indicator. For each session, a value is assigned to each collected user event data item, and a session score is derived based on those assigned values. A user experience score is derived based upon a plurality of the derived session scores. The user experience score is reported.

Evaluating user experience

In one example of the disclosure, incident data indicative of a crash of an application is obtained, the crash occurring during an access of the application by a user at a computing device. A session depth at which the crash occurred is determined based upon the incident data. Time period data is obtained, the time period data indicative of a time period, following the crash, until the user next accesses the application at the computing device. A severity score for the crash is determined based upon the session depth and the time period.

Determining severity of application crashes

Examples disclosed herein relate to creating a first document skeleton associated with a first state of an application, creating a second document skeleton associated with a second state of the application, determining, according to a comparison of the first document skeleton and a second document skeleton, whether a transition occurred in the application, and in response to determining that the transition occurred in the application, logging a measured metric associated with the application.

Detecting transitions

An application process can be executed based on an initialization instruction, where the application process includes instructions associated with a hook framework. A virtual machine configured to load the hook framework on the virtual machine based on instructions included in the application process can be initiated and the instructions associated with the hook framework can be executed upon initiation of the virtual machine to insert a hook on the virtual machine. A nascent process configured to initiate an additional virtual machine can be initiated based on a request to load an application, where the additional virtual machine is hooked via the hook inserted on the virtual machine.

Hook framework

Example implementations relate to separating verifications from test executions. Some implementations may include a data capture engine that captures data points during test executions of the application under test. The data points may include, for example, application data, test data, and environment data. Additionally, some implementations may include a data correlation engine that correlates each of the data points with a particular test execution state of the application under test based on a sequence of events that occurred during the particular test execution state. Furthermore, some implementations may also include a test verification engine that, based on the correlation of the data points, verifies an actual behavior of the application under test separately from the particular test execution state.

Separating test verifications from test executions

In one example of the disclosure, a graphic user interface is sent to a client system for display via a web browser. The graphic user interface is to enable receipt of a user instruction to commence recording of user actions made via the web browser upon a subject web application. Responsive to receipt of the user instruction, a key is caused to be sent via the web browser to a host system hosting the subject web application. The key and recording data indicative of user actions performed upon the subject web application via the web browser are received from the host system via the web browser. The key and the recording data are included within a web page or web pages of the subject web application. The key is utilized to access the recording data. The recording data is sent to the client system for display via the web browser.

Application recording

Example implementations relate to determining idle testing periods. Some implementations may include a data capture engine to capture data points during test executions of the application under test. The data points may include, for example, test action data and application action data. Additionally, some implementations may include a data correlation engine to correlate each of the data points with a particular test execution of the test executions, and each of the data points may be correlated based on a sequence of events that occurred during the particular test execution. Furthermore, some implementations may also include an idle testing period determination engine to determine, based on the correlation of the data points, idle testing periods of the test executions. The idle testing periods may be periods of time where both the test executions and the application under test are idle.

Determining idle testing periods

A monitoring system includes a monitoring device for monitoring an event occurred on an original screen and a change from the original screen to a target screen as a result of the event. The monitoring device may include a report monitor to obtain a plurality of event reports, and a report analyzer to group the plurality of event reports into a plurality of report groups and to generate an event summary for at least one of the plurality of report groups.

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Yehud, Israel

Amichai Nitsan