Deep neural networks (DNNs) are increasingly used in safety-critical autonomous systems
as perception components processing high-dimensional image data. Formal analysis of …

Markov chain analysis is a key technique in formal verification. A practical obstacle is that all
probabilities in Markov models need to be known. However, system quantities such as …

Parametric model checking (PMC) computes algebraic formulae that express key non-
functional properties of a system (reliability, performance, etc.) as rational functions of the …

Probabilistic model-checking aims to prove whether a Markov decision process (MDP)
satisfies a temporal logic specification. The underlying methods rely on an often unrealistic …

Providing assurance that self-adaptive software meets its dependability, performance and
other quality-of-service (QoS) requirements is a great challenge. Recent approaches to …

Almost without exception, cyber-physical systems operate alongside, for the benefit of, and
supported by humans. Unsurprisingly, disregarding their social aspects during development …

Autonomous systems are often used in applications where environmental and internal
changes may lead to requirement violations. Adapting to these changes proactively, ie …

Markov chain analysis is a key technique in formal verification. A practical obstacle is that all
probabilities in Markov models need to be known. However, system quantities such as …

Software-intensive systems are increasingly used to support tasks that are typically
characterized by high degrees of uncertainty. The modeling notations employed to design …

Stochastic models are widely used to verify whether systems satisfy their reliability,
performance and other nonfunctional requirements. However, the validity of the verification …