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:
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 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:
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.
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:
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:
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.