Entit Software LLC

Micro Focus Lcc

Amit Levin

Ilan Shufer

Olga Kogan

Ohad Assulin

Yaron Burg

Mor Gelberg

Examples relate to load testing. The examples disclosed herein enable obtaining lines of code that are recorded as an application is executed in a client computing device, the lines of code being recorded in chronological order of the execution; determining whether a dependency on at least one variable exists in individual lines of the lines of code; in response to determining that the dependency exists, storing the dependency in a data storage; identifying, from the lines of code, a line of code including a network call statement that calls a called variable; and eliminating a first subset of the lines of code based on the called variable and dependencies stored in the data storage, wherein a second subset of the lines of code that remain after the elimination comprises user-entered parameter data.

Load testing

Example implementations relate to determining visual testing coverages. Some implementations may include a principal application screen identification engine to identify, based on an image processing analysis of a set of screenshots of an application under test, principal application screens that represent a set of test executions of the application under test. Some implementations may also include a user interface element identification engine to identify user interface elements in the principal application screens. Some implementations may also include a visual testing coverage engine to automatically identify, based on an analysis of gaze data and the user interface elements, a visual testing coverage of the application under test.

Determining visual testing coverages

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

A first screenshot taken from a screen and having a first layout element may be determined to meet a first layout similarity threshold with a second screenshot having a second layout element. A first group comprising the first and second screenshot may be created in response to the first screenshot meeting the first layout similarity threshold with the second screenshot and it may be determined that a first feature of the first screenshot from the first group matches a second feature of a third screenshot from a second group. The second group may then be associated with a screen in response to the match determination.

Associating a screenshot group with a screen

In one example implementation, an example system includes a dependency engine, a filter engine, and a cluster engine. In the one example implementation, a file pair of a first commit log entry of a plurality of commit log entries in a commit history is identified by the dependency engine. In the one example implementation, a dependency rank is assigned to the file pair by the dependency engine based on a number of times the first file of the file pair and the second file of the file pair appear together in the plurality of commit log entries. In the one example implementation, a dependency rank is compared to a confidence rank by the filter engine. In the one example implementation, a second file is indicated as dependent on a first file when the dependency rank of the file pair achieves the confidence rank.

Dependency rank based on commit history

An example device in accordance with an aspect of the present disclosure includes an identification engine, a clustering engine, and a weighting engine. The identification engine is to identify User Interface (UI) elements of an application under test based on an image processing analysis of screens of the application under test. The clustering engine is to cluster the UI elements into at least one cluster based on at least one feature of the UI elements. The weighting engine is to assign score weights to the UI elements of a given screen according to which of the at least one cluster that a given one of the UI elements is clustered in.

Score weights for user interface (UI) elements

An example device in accordance with an aspect of the present disclosure includes a screen identification engine to identify a screen(s) of a test run, an element identification engine to selectively identify an element(s) among available elements of the screen(s), and a signature building engine to build a signature corresponding to the test run. The signature incorporates the screen(s) and element(s) to capture an application flow of the test run, while excluding available elements that do not correspond to the application flow.

Growing community of inventors

Yehud, Israel

Amit Levin