We develop a model-based framework which supports approximate quantitative verification of implantable cardiac pacemaker models over hybrid heart models. The framework is based on hybrid input-output automata and can be instantiated with user-specified pacemaker and heart models. For the specifications, we identify two property patterns which are tailored to the verification of pacemakers: "can the pacemaker maintain a normal heart behaviour?" and "what is the energy level of the battery after t time units?". We implement the framework in Simulink based on the discrete-time simulation semantics and endow it with a range of basic and advanced quantitative property checks. The advanced property checks include the correction of pacemaker mediated Tachycardia and how the noise on sensor leads influences the pacing level. We demonstrate the usefulness of the framework for safety assurance of pacemaker software by instantiating it with two hybrid heart models and verifying a number of correctness properties with encouraging experimental results.

Abstract. We study the verification of a finite continuous-time Markov chain (CTMC) C against a linear real-time specification given as a deterministic timed automaton (DTA) A with finite or Muller acceptance conditions. The central question that we address is: what is the probability of the set of paths of C that are accepted by A, i.e., the likelihood that C satisfies A? It is shown that under finite acceptance criteria this equals the reachability probability in a finite piecewise deterministic Markov process (PDP), whereas for Muller acceptance criteria it coincides with the reachability probability of terminal strongly connected components in such a PDP. Qualitative verification is shown to amount to a graph analysis of the PDP. Reachability probabilities in our PDPs are then characterized as the least solution of a system of Volterra integral equations of the second type and are shown to be approximated by the solution of a system of partial differential equations. For single-clock DTA, this integral equation system can be transformed into a system of linear equations where the coefficients are solutions of ordinary differential equations. As the coefficients are in fact transient probabilities in CTMCs, this result implies that standard algorithms for CTMC analysis suffice to verify single-clock DTA specifications.1998 ACM Subject Classification: D.2.4.

This paper proposes a technique to synthesize parametric rate values in continuous-time Markov chains that ensure the validity of bounded reachability properties. Rate expressions over variables indicate the average speed of state changes and are expressed using the polynomials over reals. The key contribution is an algorithm that approximates the set of parameter values for which the stochastic real-time system guarantees the validity of bounded reachability properties. This algorithm is based on discretizing parameter ranges together with a refinement technique. This paper describes the algorithm, analyzes its time complexity, and shows its applicability by deriving parameter constraints for a real-time storage system with probabilistic error checking facilities.

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This work deals with Markov processes that are defined over an uncountable state space (possibly hybrid) and embedding nondeterminism in the shape of a control structure. The contribution looks at the problem of optimization, over the set of allowed controls, of probabilistic specifications defined by automatain particular, the focus is on deterministic finite-state automata. This problem can be reformulated as an optimization of a probabilistic reachability property over a product process obtained from the model for the specification and the model of the system. Optimizing over automata-based specifications thus leads to maximal or minimal probabilistic reachability properties. For both setups, the contribution shows that these problems can be sufficiently tackled with history-independent Markov policies. This outcome has relevant computational repercussions: in particular, the work develops a discretization procedure leading into standard optimization problems over Markov decision processes. Such procedure is associated with exact error bounds and is experimentally tested on a case study.