Automatic, Load-Independent Detection of Performance Regressions by Transaction Profiles

Performance regression testing is an important step in the production process of enterprise applications. Yet, analysing this type of testing data is mainly conducted manually and depends on the load applied during the test. To ease such a manual task we present an automated, load-independent technique to detect performance regression anomalies based on the analysis of performance testing data using a con-cept known as Transaction Profile. The approach can be automated and it utilises data already available to the per-formance testing along with the queueing network model of the testing system. The presented “Transaction Profile Run Report ” was able to automatically catch performance regression anomalies ca-used by software changes and isolate them from those caused by load variations with a precision of 80 % in a case study conducted against an open source application. Hence, by deploying our system, the testing teams are able to detect performance regression anomalies by avoiding the manual approach and eliminating the need to do extra runs with varying load