Abstract: 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.

Abstract: 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.

Abstract: Example embodiments relate to tracing source code for end user monitoring. In example embodiments, an application is monitored to obtain an interaction log, where the interaction log tracks application interactions by each of a plurality of synthetic monitors. Further, an execution of application code that is associated with the application is monitored to obtain an instrumentation log. At this stage, the interaction log and the instrumentation log are used to determine relationships between portions of the application code and the plurality of synthetic monitors. A notification of a modification to the application is received, and an affected subset of the synthetic monitors that are affected by the modification are identified based on the relationships.

Abstract: 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.

Abstract: 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.

Abstract: 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.

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.

Abstract: Error data may be collected. The error data may represent a first plurality of errors of a first type and a second plurality of errors of a second type to occur in a plurality of instances of an application transaction. Visualization data may be generated. The visualization data may represent an error flow diagram to display on an output device. The error flow diagram may comprise a first block having a first visual property based on a first number of the first plurality of errors, a second block having a second visual property based on a second number of the second plurality of errors, and a first linkage between the first block and the second block.

Abstract: Examples disclosed herein relate to identifying a configuration element value as a potential cause of a testing operation failure. Examples include causing a testing operation to be performed approximately in parallel on each of a plurality of instances of an application executed in respective testing environments, acquiring configuration element values from each of the testing environments, and identifying at least one of the configuration element values as a potential cause of a testing operation failure.

Abstract: 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.

Abstract: A method to manage a global feature library is provided herein. The method provides a global feature library with a plurality of features defined therein. The global feature library includes a feature switch for each of the plurality of features. The feature switch is useable with an application code. The feature switch is linked to an application code. The feature switch includes a feature value that turns a feature associated with the feature switch on and off based on a global value rule. The global rule is transmitted to a client device capable of storing the feature value in-memory.

Abstract: In some examples, a system provides a plurality of code changes of a program in a continuous deployment pipeline that performs tests on the plurality of code changes and deploys a code assembly including the plurality of code changes. The system ranks a plurality of tests based on a test attribute and a test context to provide a test rank for each of the plurality of tests, and selects, from among the plurality of tests using the test ranks, a subset of tests to include in a test set for the continuous deployment pipeline. The system executes the test set in the continuous deployment pipeline to test the plurality of code changes.

Abstract: A monitor that monitors an application is provided herein. The monitor provides a monitor function that monitors an application. The monitor embeds the monitor function in meta data of a code base of the application. The monitor function embedded in the code base is activated. The monitor function is used to monitor data associated therewith during deployment of the application.

Abstract: 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.

Abstract: A method to prioritize a plurality of tests in a continuous deployment pipeline. The method ranks the plurality of tests based on a test attribute and a test context to provide a test rank for each of the plurality of tests. The method sets a test set for the continuous deployment pipeline using the test ranks. The method executes the test set in the continuous deployment pipeline.

Abstract: In one implementation, a system for user interface components load time visualization includes a load engine to monitor a load time of a number of elements of a user interface, a color engine to assign a color to each of the number of elements of the user interface based on the load time, and a compile engine to display a component color map of the user interface utilizing the color assigned to each of the number of elements.

Abstract: Role based assessment for an IT management system, includes maintaining a plurality of roles, each role attributable to a user type within an IT management system. Mappings are defined between the plurality of user roles and assets of the IT management system. An assessment for the IT management system is then assembled from the perspective of a selected one of the plurality of roles based on mappings between the selected user role and the assets.

Abstract: 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.

Abstract: Examples disclosed herein relate to identifying a configuration element value as a potential cause of a testing operation failure. Examples include causing a testing operation to be performed approximately in parallel on each of a plurality of instances of an application executed in respective testing environments, acquiring configuration element values from each of the testing environments, and identifying at least one of the configuration element values as a potential cause of a testing operation failure.

Abstract: Example embodiments relate to determining code coverage based on production sampling. In example embodiments, a production execution data set that includes metrics for code units of a software application is obtained, where the metrics include input and output values for each of the code units and an average execution count for each of the code units. Further, application code execution is tracked during a testing procedure of the software application to determine executed lines of code. At this stage, production code coverage of the software application is determined based on the production execution data set and the executed lines of code.