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Developers often lack the time or knowledge to profoundly understand the performance issues in largescale component-oriented enterprise applications. This situation is further complicated by the fact that such applications are often built using a mix of in-house and commercial-off-the-shelf (COTS) components. This paper presents a methodology for understanding and predicting the performance of component-oriented distributed systems both during development and after they have been built. The methodology is based on three conceptually separate parts: monitoring, modelling and performance prediction. Performance predictions are based on UML models created dynamically by monitoring-and-analysing a live or under-development system. The system is monitored using non-intrusive methods and run-time data is collected. In addition, static data is obtained by analysing the deployment configuration of the target application. UML models enhanced with performance indicators are created based on both static and dynamic data, showing performance hot spots. To facilitate the understanding of the system, the generated models are traversable both horizontally at the same abstraction level between transactions, and vertically between different layers of abstraction using the concepts defined by the Model Driven Architecture. The system performance is predicted and performance-related issues are identified in different scenarios by generating workloads and simulating the performance models. Work is under way to implement a framework for the presented methodology with the current focus on the Enterprise Java Beans technology.