Incremental, Inductive Coverability

We give an incremental, inductive (IC3) procedure to check coverability of well-structured transition systems. Our procedure generalizes the IC3 procedure for safety verification that has been successfully applied in finite-state hardware verification to infinite-state well-structured transition systems. We show that our procedure is sound, complete, and terminating for downward-finite well-structured transition systems---where each state has a finite number of states below it---a class that contains extensions of Petri nets, broadcast protocols, and lossy channel systems. We have implemented our algorithm for checking coverability of Petri nets. We describe how the algorithm can be efficiently implemented without the use of SMT solvers. Our experiments on standard Petri net benchmarks show that IC3 is competitive with state-of-the-art implementations for coverability based on symbolic backward analysis or expand-enlarge-and-check algorithms both in time taken and space usage.Comment: Non-reviewed version, original version submitted to CAV 2013; this is a revised version, containing more experimental results and some correction

Hierarchical Set Decision Diagrams and Regular Models

This paper presents algorithms and data structures that exploit a compositional and hierarchical specification to enable more efficient symbolic model-checking. We encode the state space and transition relation using hierarchical Set Decision Diagrams (SDD) [9]. In SDD, arcs of the structure are labeled with sets, themselves stored as SDD. To exploit the hierarchy of SDD, a structured model representation is needed. We thus introduce a formalism integrating a simple notion of type and instance. Complex composite behaviors are obtained using a synchronization mechanism borrowed from process calculi. Using this relatively general framework, we investigate how to capture similarities in regular and concurrent models. Experimental results are presented, showing that this approach can outperform in time and memory previous work in this area

Parallel symbolic state-space exploration is difficult, but what is the alternative?

State-space exploration is an essential step in many modeling and analysis problems. Its goal is to find the states reachable from the initial state of a discrete-state model described. The state space can used to answer important questions, e.g., "Is there a dead state?" and "Can N become negative?", or as a starting point for sophisticated investigations expressed in temporal logic. Unfortunately, the state space is often so large that ordinary explicit data structures and sequential algorithms cannot cope, prompting the exploration of (1) parallel approaches using multiple processors, from simple workstation networks to shared-memory supercomputers, to satisfy large memory and runtime requirements and (2) symbolic approaches using decision diagrams to encode the large structured sets and relations manipulated during state-space generation. Both approaches have merits and limitations. Parallel explicit state-space generation is challenging, but almost linear speedup can be achieved; however, the analysis is ultimately limited by the memory and processors available. Symbolic methods are a heuristic that can efficiently encode many, but not all, functions over a structured and exponentially large domain; here the pitfalls are subtler: their performance varies widely depending on the class of decision diagram chosen, the state variable order, and obscure algorithmic parameters. As symbolic approaches are often much more efficient than explicit ones for many practical models, we argue for the need to parallelize symbolic state-space generation algorithms, so that we can realize the advantage of both approaches. This is a challenging endeavor, as the most efficient symbolic algorithm, Saturation, is inherently sequential. We conclude by discussing challenges, efforts, and promising directions toward this goal

Exposure to Endocrine Disruptors and Nuclear Receptors Gene Expression in Infertile and Fertile Men from Italian Areas with Different Environmental Features

Internal levels of selected endocrine disruptors (EDs) (i.e., perfluorooctane sulfonate (PFOS), perfluorooctanoic acid (PFOA), di-2-ethylhexyl-phthalate (DEHP), mono-(2-ethylhexyl)-phthalate (MEHP), and bisphenol A (BPA)) were analyzed in blood/serum of infertile and fertile men from metropolitan, urban and rural Italian areas. PFOS and PFOA levels were also evaluated in seminal plasma. In peripheral blood mononuclear cells (PBMCs) of same subjects, gene expression levels of a panel of nuclear receptors (NRs), namely estrogen receptor α (ERα) estrogen receptor β (ERβ), androgen receptor (AR), aryl hydrocarbon receptor (AhR), peroxisome proliferator-activated receptor γ (PPARγ) and pregnane X receptor (PXR) were also assessed. Infertile men from the metropolitan area had significantly higher levels of BPA and gene expression of all NRs, except PPARγ, compared to subjects from other areas. Subjects from urban areas had significantly higher levels of MEHP, whereas subjects from rural area had higher levels of PFOA in both blood and seminal plasma. Interestingly, ERα, ERβ, AR, PXR and AhR expression is directly correlated with BPA and inversely correlated with PFOA serum levels. Our study indicates the relevance of the living environment when investigating the exposure to specific EDs. Moreover, the NRs panel in PBMCs demonstrated to be a potential biomarker of effect to assess the EDs impact on reproductive health

Component-wise incremental LTL model checking

Efficient symbolic and explicit-state model checking approaches have been developed for the verification of linear time temporal logic (LTL) properties. Several attempts have been made to combine the advantages of the various algorithms. Model checking LTL properties usually poses two challenges: one must compute the synchronous product of the state space and the automaton model of the desired property, then look for counterexamples that is reduced to finding strongly connected components (SCCs) in the state space of the product. In case of concurrent systems, where the phenomenon of state space explosion often prevents the successful verification, the so-called saturation algorithm has proved its efficiency in state space exploration. This paper proposes a new approach that leverages the saturation algorithm both as an iteration strategy constructing the product directly, as well as in a new fixed-point computation algorithm to find strongly connected components on-the-fly by incrementally processing the components of the model. Complementing the search for SCCs, explicit techniques and component-wise abstractions are used to prove the absence of counterexamples. The resulting on-the-fly, incremental LTL model checking algorithm proved to scale well with the size of models, as the evaluation on models of the Model Checking Contest suggests

On-the-fly Uniformization of Time-Inhomogeneous Infinite Markov Population Models

This paper presents an on-the-fly uniformization technique for the analysis of time-inhomogeneous Markov population models. This technique is applicable to models with infinite state spaces and unbounded rates, which are, for instance, encountered in the realm of biochemical reaction networks. To deal with the infinite state space, we dynamically maintain a finite subset of the states where most of the probability mass is located. This approach yields an underapproximation of the original, infinite system. We present experimental results to show the applicability of our technique

Platform Dependent Verification: On Engineering Verification Tools for 21st Century

The paper overviews recent developments in platform-dependent explicit-state LTL model checking.Comment: In Proceedings PDMC 2011, arXiv:1111.006

Enteroviral Infections in the First Three Months of Life

Enteroviruses (EVs) are an important source of infection in the paediatric age, with most cases concerning the neonatal age and early infancy. Molecular epidemiology is crucial to understand the circulation of main serotypes in a specific area and period due to their extreme epidemiological variability. The diagnosis of EVs infection currently relies on the detection of EVs RNA in biological samples (usually cerebrospinal fluid and plasma, but also throat swabs and feces) through a poly-merase chain reaction assay. Although EVs infections usually have a benign course, they sometimes become life threatening, especially when symptoms develop in the first few days of life. Mortality is primarily associated with myocarditis, acute hepatitis, and multi-organ failure. Neurodevelopmental sequelae have been reported following severe infections with central nervous system involvement. Unfortunately, at present, the treatment of EVs infections is mainly supportive. The use of specific antiviral agents in severe neonatal infections has been reported in single cases or studies includ-ing few neonates. Therefore, further studies are needed to confirm the efficacy of these drugs in clinical practice