Abstract: Example implementations relate to simulating an environment. For example, a system for environment simulation may include a simulation engine to build an environment simulation to mimic portions of a real environment relevant to a detected anomaly trend, an acceleration engine to simulate, within the environment simulation, a scenario associated with the detected anomaly at a rate faster than the scenario occurs in the real environment, a abnormal behavior engine to detect a abnormal behavior associated with the scenario, and an adaptation engine to modify a device within the real environment to be adaptive to the scenario, based on the detected abnormal behavior.

Abstract: End user data partitioning can include receiving a number of data queries for a data source from a user, developing a dimension relation graph based on attributes of the number of data queries, and partitioning the data source based on the dimension relation graph.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Example implementations relate to simulating an environment. For example, a system for environment simulation may include a simulation engine to build an environment simulation to mimic portions of a real environment relevant to a detected anomaly trend, an acceleration engine to simulate, within the environment simulation, a scenario associated with the detected anomaly at a rate faster than the scenario occurs in the real environment, a abnormal behavior engine to detect a abnormal behavior associated with the scenario, and an adaptation engine to modify a device within the real environment to be adaptive to the scenario, based on the detected abnormal behavior.

Abstract: 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.

Abstract: 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.

Abstract: Dependency monitoring can include monitoring a first application and a second application for un-expected behavior. Dependency monitoring can also include receiving a description of a number of dependencies between a number of applications wherein the description of the number of dependencies is created before monitoring of the first application and the second application begins. Dependency monitoring can include sending a message to an information technology (IT) personnel, wherein the message identifies a dependency from the number of dependencies between the first application and the second application based on the description of the number of dependencies.

Abstract: Example implementations relate to generating application flow entities. Some implementations may include accessing a series of image frames from a test execution of an application and comparing, using an application test analysis device comprising a digital image processor, the image frames to each other to identify a subset of the image frames. The subset of image frames may be identified, for example, based on actions occurring during the test execution. Some implementations may also include automatically generating, using the application testing system, an application flow entity that represents the test. The application flow entity may be generated based on the subset of image frames.

Abstract: 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.

Abstract: Systems, methods, and machine-readable and executable instructions are provided for error developer association. Error developer association can include identifying a number of portions of the source code associated with a message, wherein the message is associated with an error. Error developer association can also include associating a developer with a portion of the source code of the number of portions of the source code. Error developer association can also include identifying a developer of the number of developers to resolve the error.

Abstract: 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.

Abstract: 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.

Abstract: Examples relate to testing applications using virtual reality. In one example, a computing device may: cause display of a viewable portion of a virtual environment on a VR display of the VR device: cause display of a virtual user device within the viewable portion of the virtual environment, the virtual user device corresponding to a hardware device that is running an application under test (AUT); cause display, on the virtual user device, of a virtual user interface of the AUT; receive feedback data indicating i) a change in the virtual environment, ii) a change in a state of the AUT, or iii) an interaction with the virtual user device; and in response to receiving feedback data, cause display of an updated viewable portion of the virtual environment on the VR display.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Examples relate to execution of interaction flows. The examples disclosed herein enable obtaining, via a user interface of a local client computing device, an interaction flow that defines an order of execution of a plurality of interaction points and values exchanged among the plurality of interaction points, the plurality of interaction points comprising a first interaction point that indicates an event executed by an application; triggering the execution of the interaction flow; determining whether any of remote client computing devices that are in communication with the local client computing device includes the application; and causing the first interaction point to be executed by the application in at least one of the remote client computing devices that are determined to include the application.