Current complex service systems are usually comprised of many other components which are often external services performing particular tasks. The quality of service (QoS) attributes such as availability, cost, response time are essential to determine usability and eciency of such system. Obviously, the QoS of such compound system is dependent on the QoS of its components. However, the QoS of each component is naturally unstable and di erent each time it is called due to many factors like network bandwidth, workload, hardware resource, etc. This will consequently make the QoS of the whole system be unstable. This uncertainty can be described and represented with probability distributions. This thesis presents an approach to calculate the QoS of the system when the probability distributions of QoS of each component are provided by service provider or derived from historical data, along with the structure of their compositions. In addition, an analyzer tool is implemented in order to predict the QoS of the given compositions and probability distributions following the proposed approach. The output of the analyzer can be used to predict the behavior of the system to be implemented and to make decisions based on the expected performance. The experimental evaluation shows that the estimation is reliable with a minimal and acceptable error measurement.