No Arabic abstract
The high availability and scalability of weakly-consistent systems attracts system designers. Yet, writing correct application code for this type of systems is difficult; even how to specify the intended behavior of such systems is still an open question. There has not been established any standard method to specify the intended dynamic behavior of a weakly consistent system. There exist specifications of various consistency models for distributed and concurrent systems; and the semantics of replicated datatypes like CRDTs have been specified in axiomatic and operational models based on visibility relations. In this paper, we present a temporal logic, EPTL, that is tailored to specify properties of weakly consistent systems. In contrast to LTL and CTL, EPTL takes into account that operations of weakly consistent systems are in many cases not serializable and have to be treated respectively to capture the behavior. We embed our temporal logic in Isabelle/HOL and can thereby leverage strong semi-automatic proving capabilities.
We propose a measure and a metric on the sets of infinite traces generated by a set of atomic propositions. To compute these quantities, we first map properties to subsets of the real numbers and then take the Lebesgue measure of the resulting sets. We analyze how this measure is computed for Linear Temporal Logic (LTL) formulas. An implementation for computing the measure of bounded LTL properties is provided and explained. This implementation leverages SAT model counting and effects independence checks on subexpressions to compute the measure and metric compositionally.
In this paper, we propose a new logic for expressing and reasoning about probabilistic hyperproperties. Hyperproperties characterize the relation between different independent executions of a system. Probabilistic hyperproperties express quantitative dependencies between such executions. The standard temporal logics for probabilistic systems, i.e., PCTL and PCTL* can refer only to a single path at a time and, hence, cannot express many probabilistic hyperproperties of interest. The logic proposed in this paper, HyperPCTL, adds explicit and simultaneous quantification over multiple traces to PCTL. Such quantification allows expressing probabilistic hyperproperties. A model checking algorithm for the proposed logic is also given for discrete-time Markov chains.
Whereas standard treatments of temporal logic are adequate for closed systems, having no run-time interactions with their environment, they fall short for reactive systems, interacting with their environments through synchronisation of actions. This paper introduces reactive temporal logic, a form of temporal logic adapted for the study of reactive systems. I illustrate its use by applying it to formulate definitions of a fair scheduler, and of a correct mutual exclusion protocol. Previous definitions of these concepts were conceptually much more involved or less precise, leading to debates on whether or not a given protocol satisfies the implicit requirements.
For many applications, we are unable to take full advantage of the potential massive parallelisation offered by supercomputers or cloud computing because it is too hard to work out how to divide up the computation task between processors in such a way to minimise the need for communication. However, a recently developed branch-independent tableaux for the common LTL temporal logic should intuitively be easy to parallelise as each branch can be developed independently. Here we describe a simple technique for partitioning such a tableau such that each partition can be processed independently without need for interprocess communication. We investigate the extent to which this technique improves the performance of the LTL tableau on standard benchmarks and random formulas.
One of the advantages of adopting a Model Based Development (MBD) process is that it enables testing and verification at early stages of development. However, it is often desirable to not only verify/falsify certain formal system specifications, but also to automatically explore the properties that the system satisfies. In this work, we present a framework that enables property exploration for Cyber-Physical Systems. Namely, given a parametric specification with multiple parameters, our solution can automatically infer the ranges of parameters for which the property does not hold on the system. In this paper, we consider parametric specifications in Metric or Signal Temporal Logic (MTL or STL). Using robust semantics for MTL, the parameter mining problem can be converted into a Pareto optimization problem for which we can provide an approximate solution by utilizing stochastic optimization methods. We include algorithms for the exploration and visualization of multi-parametric specifications. The framework is demonstrated on an industrial size, high-fidelity engine model as well as examples from related literature.