Fault injection (FI) is an experimental methodology used in a wide range of scenarios for validating the fault resilience of applications, especially safety-critical ones. A sufficiently thoroughgoing evaluation produces a significant amount of data regarding the behavior of software components or entire systems in the presence of faults. The core questions that practitioners using fault injection face are 1) how to extract and represent information, 2) how to effectively analyze that data and how to utilize the gained knowledge to improve the FI process. Previous works addressing these questions relied mainly on ad hoc approaches. The current paper presents a modern view of these problems, preparing and executing the knowledge extraction by exploratory (big) data analysis, methods, and tools. A real use-case based on FI campaigns composed of thousands of fault injections into a virtualized system indicates the huge potential of the approach. The outcome is the discovery of an opportunity for a drastic speed-up of the FI process unrevealed by the traditional methodology.

It is my distinguished pleasure to welcome all the participants to Budapest and the 43nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks. The Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) is the most prestigious international forum for presenting research results in the field of dependability and security. It is especially important for us that this conference is organized in our region for the first time in its history.

Resource allocation and scheduling optimization problems are core problems in the field of IT systems. However, such problems frequently underlie several additional constraints. The formalization of a real life problem requires a well-defined mathematical and modeling approach providing an integrated verification and optimization. The current paper proposes such methods adapting Process Network Synthesis algorithms to Petri net reachability problem: combining the efficiency of PNS optimization algorithms with the modeling power of Petri nets. They provide powerful techniques to compute optimal trajectories for the reachability analysis of the modeled system.

The VIATRA (visual automated model transformations) framework is the core of a transformation-based verification and validation environment for improving the quality of systems designed using the Unified Modeling Language by automatically checking consistency, completeness, and dependability requirements. In the current paper, we present an overview of (i) the major design goals and decisions, (ii) the underlying formal methodology based on metamodeling and graph transformation, (iii) the software architecture based upon the XMI standard, and (iv) several benchmark applications of the VIATRA framework.

The paper introduces a method which allows quantitative dependability analysis of systems modeled by using the Unified Modeling Language (UML) statechart diagrams. The analysis is performed by transforming the UML model to stochastic reward nets (SRNs). A large subset of statechart model elements is supported including event processing, state hierarchy and transition priorities. The transformation is presented by a set of SRN patterns. Performance-related measures can be directly derived using SRN tools, while dependability analysis requires explicit modeling of erroneous states and faulty behavior.