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Autonomic computing programming models explicitly address self management properties by introducing the notion of ldquoAutonomic Element. However, most of currently developed systems do not employ autonomic self-managing programming paradigms. Thus, a current challenge is to find mechanisms to identify the self-tuning behavior and self-tuning parameters which have implicitly been declared using non-autonomic elements, and to expose them for monitoring or to an analysis framework. Static analysis, although it shows a good potential, it results in many false positives. In this paper, we provide a mechanism to identify the tuning parameters more accurately through dynamic analysis.