Micro Focus Lcc

Entit Software LLC

Hewlett-packard Development Company, L.P.

Yaron Burg

Inbar Shani

Amichai Nitsan

Ilan Shufer

Alexei Ledenev

Ohad Assulin

Olga Kogan

Amit Levin

Ayal Cohen

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

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

Example implementations relate to automatically identifying regressions. 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 regression identification engine to automatically identify, based on the correlated data points, a regression between a first version of the application under test and a second version of the application under test.

Automatic regression identification

First object model data from a first application state generated in a first browser mode may be collected. The first object model data may comprise a code-based representation of a first layout element and a second layout element of the first application state. According to a layout detection rule, a first attribute of the first layout element may be compared with a second attribute of the first layout element or of a second layout element. A layout error corresponding to the first layout element may be identified based on the comparison of the first attribute with the second attribute.

Identifying a layout error

Example implementations relate to composing future tests. 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 composition engine to compose, based on an interaction with a visualization of results of a verification query of the correlated data points, a future test of the application under test.

Composing future application tests including test action data

Examples disclosed herein relate to determining event and input coverage metrics for a graphical user interface (GUI) control instance. Examples include accessing event and input occurrence data identifying interactions performed on a GUI of an application, determining an event coverage metric for the given GUI control instance based on the event occurrence data, and determining an input coverage metric for the given GUI control instance based on the input occurrence data.

Determining event and input coverage metrics for a graphical user interface control instance

Examples disclosed herein relate to acquiring identification of an application lifecycle management (ALM) entity associated with similar code. Examples include identifying a target code segment, and acquiring, from an ALM system, identification of an ALM entity associated with other code similar to the target code segment and identified by a code similarity system.

Acquiring identification of an application lifecycle management entity associated with similar code

In one example of the disclosure, code lines for a software program are received, the code lines including a unit of code lines. Code entities within the unit are identified. Each code entity includes a line or consecutive lines of code implementing a distinct program requirement or defect fix for the program. Context changes are identified within the unit, each context change including an occurrence of a first code line set implementing an entity, adjacent to a second code line set implementing another entity, within a same code scope. A code complexity score is determined based upon counts of entities identified and context changes identified within the unit, and upon counts of code lines and entities within the program.

Determining code complexity scores

A system and method for monitoring exploratory testing by a plurality of testers of software containing a graphical user interface is disclosed. The method includes recording interactions of each of the plurality of testers with a graphical user interface (GUI) under test. The recorded interactions of each tester can be stored in an interaction database. An interaction footprint map is created from the interaction database to show which portions of the GUI under test have had interaction with at least one of the testers. The interaction footprint map is displayed in relation to the GUI for at least one end user.

Growing community of inventors

Yehud, Israel

Yaron Burg