After you’ve done all your homework and determined user scenarios and load profiles, you fire up your performance tool and begin to simulate those scenarios. If using LoadRunner for performance testing, when you record a session and then execute it, you have probably encountered errors such as “The same user can not login twice. “ or “ One user can only poll once”. When this happens, this basically blocks your performance test and stops you from moving forward. These errors occur because a server cannot accept the same user executing the same action twice because many applications are designed to have precise statistics from each individual user, so if one user logs in 10 times (as you may try to simulate in a load test), the results do not conform with what the application expects and error messages are returned. Everyone encounters these types of problems and there are numerous ways to troubleshoot them. One such method is using correlation.
Correlation is a process that converts the static data (or static session ID of the virtual user) to dynamic data and sends it to a server. This changes the session ID so that the server believes it is a new user. There are two common methods for correlation: auto correlation and manual correlation. For auto correlation, you need to click the Correlation Options link to set rules and then you can select which parameter or sessionID to correlate. Manual correlation is more difficult than auto correlation but in some situations we need to do this in order to troubleshoot and get better data for analysis. For more information see our whitepaper on Loadrunner Performance testing at:
As mentioned earlier, most applications are designed in order to have precise statistics from each user, so simulating users needs to be done in the same way as the application expects which means that each user needs an individual ID. In LoadRunner, correlation can be used to solve this problem. Other performance and load testing tools have similar methods, but the basic concepts of parameterization are similar.