Automatic Performance Space Exploration of Web Applications

Web applications have become crucial components of current service-oriented business applications. Therefore, it is very important for the company's reputation that the performance of a web application has been tested thoroughly before deployment. We present a tool-supported performance exploration approach to investigate how potential user behavioral patterns affect the performance of the system under test. This work builds on our previous work in which we generate load from workload models describing the expected behavior of the users. We mutate a given workload model (specified using Probabilistic Timed Automata) in order to generate different mutants. Each mutant is used for load generation using the MBPeT tool and the resource utilization of the system under test is monitored. At the end of an experiment, we analyze the mutants in two ways: cluster the mutants based on the resource utilization of the system under test and identify those mutants that satisfy the criteria of given objective functions