Generating Performance Test Model from Conformance Test Logs

In this paper, we present a method that learns a deterministic finite state machine from the conformance test logs of a telecommunication protocol; then that machine is used as test model for performance testing. The learning process is in contrast to most theoretical methods automatic; it applies a sequential pattern mining algorithm on the test logs, and uses a recently proposed metric for finding frequent and significant transition sequences. The method aims to help and speed up test model design, and at the same time it may not provide an exact solution, the equivalence of some states may not be proven. In the paper, we show the results of experiments on random machines, and issues and considerations that arise when the method was applied to 3GGP Telephony Application Server test logs.