Parameterized Reliability Prediction for Component-Based Software Architectures

Critical properties of software systems, such as reliability, should be considered early in the development, when they can govern crucial architectural design decisions. A number of design-time reliabilityanalysis methods has been developed to support this task. However, the methods are often based on very low-level formalisms, and the connection to different architectural aspects (e.g., the system usage profile) is either hidden in the constructs of a formal model (e.g., transition probabilities of a Markov chain), or even neglected (e.g., resource availability). This strongly limits the applicability of the methods to effectively support architectural design. Our approach, based on the Palladio Component Model (PCM), integrates the reliability-relevant architectural aspects in a highly parameterized UML-like model, which allows for transparent evaluation of architectural design options. It covers the propagation of the system usage profile throughout the architecture, and the impact of the execution environment, which are neglected in most of the existing approaches. Before analysis, the model is automatically transformed into a formal Markov model in order to support effective analytical techniques to be employed. The approach has been validated against a reliability simulation of a distributed Business Reporting System.