Interval Change-Point Detection for Runtime Probabilistic Model Checking

Recent probabilistic model checking techniques can verify reliability and performance properties of software systems affected by parametric uncertainty. This involves modelling the system behaviour using interval Markov chains, i.e., Markov models with transition probabilities or rates specified as intervals. These intervals can be updated continually using Bayesian estimators with imprecise priors, enabling the verification of the system properties of interest at runtime. However, Bayesian estimators are slow to react to sudden changes in the actual value of the estimated parameters, yielding inaccurate intervals and leading to poor verification results after such changes. To address this limitation, we introduce an efficient interval change-point detection method, and we integrate it with a state-of-the-art Bayesian estimator with imprecise priors. Our experimental results show that the resulting end-to-end Bayesian approach to change-point detection and estimation of interval Markov chain parameters handles effectively a wide range of sudden changes in parameter values, and supports runtime probabilistic model checking under parametric uncertainty

New results on probabilistic verification : automata, logic and satisfiability

Probabilistic (or quantitative) verification is a branch of formal methods dealing with stochastic models and logic. Probabilistic models capture the behavior of randomized algorithms and other physical systems with certain uncertainty, whereas probabilistic logic expresses the quantitative measure on the probabilistic space defined by the models. Most often, the formal techniques used in studying the behavior of these models and logic are not just mundane extension of its non-probabilistic counterparts. The complexity of these mathematical structures is surprisingly different. The thesis is an effort at improving our continued under- standing of these models and logic. We will begin by looking at few interesting formal representations of discrete stochastic models. Namely, we will address the parameter synthesis problem for parametric linear time temporal logic and model checking of convex Markov decision processes with open intervals. The primary focus of the thesis is however on the satisfiability (or validity) problem of different probabilistic logics. This includes a bounded fragment of probabilistic logic and a simple quantitative (probabilistic) extension of mu-calculus. Decision procedures for the satisfiability problems are developed and a detailed complexity analysis of these problems is provided. The study of automata has been very effective in understanding logic. We will look at the newly conceived notion of p-automata, which are a probabilistic extension of alternating tree automata. As we will see, probabilistic logic exhibits both non-deterministic and stochastic behavior. The semantics of p-automata are extended to capture non-determinism and hence model Markov decision processes