Anna Ingólfsdóttir

Giovanni Bacci, Anna Ingólfsdóttir, Kim G. Larsen et al.

Continuous-time Markov chains (CTMCs) are popular modeling formalism that constitutes the underlying semantics for real-time probabilistic systems such as queuing networks, stochastic process algebras, and calculi for systems biology. Prism and Storm are popular model checking tools that provide a number of powerful analysis techniques for CTMCs. These tools accept models expressed as the parallel composition of a number of modules interacting with each other. The outcome of the analysis is strongly dependent on the parameter values used in the model which govern the timing and probability of events of the resulting CTMC. However, for some applications, parameter values have to be empirically estimated from partially-observable executions. In this work, we address the problem of estimating parameter values of CTMCs expressed as Prism models from a number of partially-observable executions. We introduce the class parametric CTMCs -- CTMCs where transition rates are polynomial functions over a set of parameters -- as an abstraction of CTMCs covering a large class of Prism models. Then, building on a theory of algorithms known by the initials MM, for minorization-maximization, we present iterative maximum likelihood estimation algorithms for parametric CTMCs covering two learning scenarios: when both state-labels and dwell times are observable, or just state-labels are. We conclude by illustrating the use of our technique in a simple but non-trivial case study: the analysis of the spread of COVID-19 in presence of lockdown countermeasures.

Giovanni Bacci, Anna Ingólfsdóttir, Kim Larsen et al.

Cyber-physical systems (CPSs) are naturally modelled as reactive systems with nondeterministic and probabilistic dynamics. Model-based verification techniques have proved effective in the deployment of safety-critical CPSs. Central for a successful application of such techniques is the construction of an accurate formal model for the system. Manual construction can be a resource-demanding and error-prone process, thus motivating the design of automata learning algorithms to synthesise a system model from observed system behaviours. This paper revisits and adapts the classic Baum-Welch algorithm for learning Markov decision processes and Markov chains. For the case of MDPs, which typically demand more observations, we present a model-based active learning sampling strategy that choses examples which are most informative w.r.t.\ the current model hypothesis. We empirically compare our approach with state-of-the-art tools and demonstrate that the proposed active learning procedure can significantly reduce the number of observations required to obtain accurate models.

Luca Aceto, Duncan Paul Attard, Adrian Francalanza et al.

The runtime analysis of decentralised software requires instrumentation methods that are scalable, but also minimally invasive. This paper presents a new algorithm that instruments choreographed outline monitors. Our instrumentation algorithm scales and reorganises monitors dynamically as the system executes. We demonstrate the implementability of choreographed outline instrumentation and compare it to inline instrumentation, subject to rigorous and comprehensive benchmarking. Our results debunk the general notion that outline monitoring is necessarily infeasible, and show that our implementation induces runtime overhead comparable to that of its inline counterpart for many practical cases.

Ian Cassar, Adrian Francalanza, Luca Aceto et al.

Runtime Monitoring is a lightweight and dynamic verification technique that involves observing the internal operations of a software system and/or its interactions with other external entities, with the aim of determining whether the system satisfies or violates a correctness specification. Compilation techniques employed in Runtime Monitoring tools allow monitors to be automatically derived from high-level correctness specifications (aka. properties). This allows the same property to be converted into different types of monitors, which may apply different instrumentation techniques for checking whether the property was satisfied or not. In this paper we compare and contrast the various types of monitoring methodologies found in the current literature, and classify them into a spectrum of monitoring instrumentation techniques, ranging from completely asynchronous monitoring on the one end and completely synchronous monitoring on the other.