ESPRIT Project 27493 HIDE High-Level Integrated Design Environment for Dependability Deliverable 2: Transformations Report on the Speci cation of Analysis and Transformation Techniques

The quantitative analysis of the dependability attributes of computer systems using stochastic modelling is a process that requires ability and experience. Building the model of a system needs the introduction of assumptions, simplifications and abstractions, whose impact on the final results can not be estimated a priori. Also, slight variations in the value of a crucial parameter might cause dramatic changes in the final measures. Moreover, real systems show such a complexity that the definition of the model itself easily becomes an error prone task. Various methods and tools for dependability modelling and analysis have been developed which provide support to the analyst, during the phases of definition and evaluation of the models. In general, model types used for dependability analysis are in two categories; combinatorial and state-space [13]. In the list below, Markov models and high level approaches which have an underlying Markov model are belonging to state-space models. 1. Combinatorial dependability models Combinatorial model types include reliability block diagrams, fault trees and reliability graphs. It was shown in [12] that Fault Tree with Repeated Events (FTRE) is the most powerful type. However, the major insufficiency of combinatorial models is that they cannot model several dependencies among model elements, e.g. repair dependency caused by a shared repair facility. These dependencies can be modelled by state-space models. Moreover, algorithms were defined (e.g. in [13]) to transform combinatorial models into state-space models.