The main objective of the research proposal is to explore the usefulness and efficency of intelligent data processing methods in the field of fault modelling with a special emphasis on the comparison of heuristic and automatically generated models. The practical usefulness of the models will be proven by applying and validating the models in design for dependability pilot applications. In order to assess the feasibility of the approach, a complete design for dependability roundtrip will be carried out on pilot examples. In the first phase the existing records from the DBENCH database will be analysed by means of data mining. Subsequently, the efficacy of automated model extraction will be evaluated by comparing the results with those from the OLAP based analysis. Selected FT measures will be implemented in a pilot application based on the fault model generated in the previous phase. The measures should represent typical paradigms like architecture-level solutions (like control-flow checking by means of a watchdog processor, use of redundant data structures) and implementation near techniques (like self-checking code). The pilot system will undergo a fault injection campaign. The efficiency of the individual measures will be estimated by the combined methodology developed in the first phase. Finally, the results will be generalized and published in a directly reusable form for the academia and industry. The main candidate is to form the methodology to an analysis pattern over the standard XML based workflow description language, and in the form of standard UML and CWM (Common Warehouse Metamodel) patterns. Similarly, architectural level FT measures will be published as parametrizable UML 2.0 templates.