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

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

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

To accurately make adaptation decisions, a self-adaptive system needs precise means to
analyze itself at runtime. To this end, runtime verification can be used in the feedback loop to …

We present a new approach for synthesising Paretooptimal Markov decision process (MDP)
policies that satisfy complex combinations of quality-of-service (QoS) software requirements …

Performance models have been used in the past to understand the performance
characteristics of software systems. However, the identification of performance criticalities is …

While Deep Reinforcement Learning (DRL) provides transformational capabilities to the
control of Robotics and Autonomous Systems (RAS), the black-box nature of DRL and …

We present an efficient parametric model checking technique for the analysis of software
performability, ie, of the performance and dependability properties of software systems. The …

Self-adaptive systems are expected to mitigate disruptions by continually adjusting their
configuration and behaviour. This mitigation is often reactive. Typically, environmental or …

Probabilistic model checking is a widely used formal verification technique to automatically
verify qualitative and quantitative properties for probabilistic models. However, capturing …