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 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: 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 embodiments disclosed herein relate to enhancing test scripts with dynamic data. The disclosed embodiments include receiving production data that reflects real user interaction with an application process. Test scripts are generated based on the production data, where the test scripts simulate behavior relating to execution of the application process. The embodiments also include automatically enhancing the test scripts with dynamic data that includes at least one of correlation data and asynchronous data.

Abstract: Example embodiments disclosed herein relate to enhancing test scripts with dynamic data. The disclosed embodiments include receiving production data that reflects real user interaction with an application process. Test scripts are generated based on the production data, where the test scripts simulate behavior relating to execution of the application process. The embodiments also include automatically enhancing the test scripts with dynamic data that includes at least one of correlation data and asynchronous data.

Abstract: A method for generating a number of load scenarios can include collecting a number of real user metrics utilizing a monitor, calculating a load behavior of the number of real user metrics utilizing a baselining technique, and generating the number of load scenarios based on the load behavior.