Quantitative Safety: Linking Proof-Based Verification with Model Checking for Probabilistic Systems

This paper presents a novel approach for augmenting proof-based verification with performance-style analysis of the kind employed in state-of-the-art model checking tools for probabilistic systems. Quantitative safety properties usually specified as probabilistic system invariants and modeled in proof-based environments are evaluated using bounded model checking techniques. Our specific contributions include the statement of a theorem that is central to model checking safety properties of proof-based systems, the establishment of a procedure; and its full implementation in a prototype system (YAGA) which readily transforms a probabilistic model specified in a proof-based environment to its equivalent verifiable PRISM model equipped with reward structures. The reward structures capture the exact interpretation of the probabilistic invariants and can reveal succinct information about the model during experimental investigations. Finally, we demonstrate the novelty of the technique on a probabilistic library case study

Stepwise Development Of Distributed Vertex Coloring Algorithms (Full Report)

Software-based systems have a strong impact in the daily life. For instance, systems like televisions, cell phones, credit cards are used for persons, while others systems, like networks, telecommunications, distributed and embedded devices, supercomputers, are used by organisations such as companies, governments, nations... Several countries, especially the advanced ones, rely on systems for the efficiency of domains like economy, health... Since they are needed in daily life, those systems should be reliable, and their specifications and design must be clear, understandable and should follow specific rules and they must avoid faults, failures and if they can not, they should at least be fault-tolerant and fail-safe. Therefore, because of those requirements, "Formal Verification" can be usefull to obtain an assurance and guarantee of their correctness with respect to safety and security issues

The Generalised Substitution Language extended to probabilistic programs

. Let predicate P be converted from Boolean to numeric type by writing hP i, with hfalsei being 0 and htruei being 1, so that in a degenerate sense hP i can be regarded as `the probability that P holds in the current state'. Then add explicit numbers and arithmetic operators, to give a richer language of arithmetic formulae into which predicates are embedded by h\Deltai. Abrial's generalised substitution language GSL can be applied to arithmetic rather than Boolean formulae with little extra effort. If we add a new operator p \Phi for probabilistic choice, it then becomes `pGSL': a smooth extension of GSL that includes random algorithms within its scope. Keywords: Probability, program correctness, generalised substitutions, weakest preconditions, B, GSL. 1 Introduction Abrial's Generalised Substitution Language GSL [1] is a weakestprecondition based method of describing computations and their meaning; it is complemented by the structures of Abstract Machines, together with which it ..