Model Checking Methods Research Articles

온라인 게임 서버의 파티 시스템 검증을 위한 스핀 모델 체커 적용에 관한 연구

Model checking method is able to check all possible cases automatically and is applicable to specifications or design before actual implementation so that some critical systems have adopted this method actively. However, the current practice of software verification is largely dependant on basic methods such as manual testing because of lack of understanding about this rigorous method and high verification cost. In this paper we conducted an experimental research for the automated verification using the SPIN model checker on an online-game server to study the applicability of the technique in this domain. The results show that we could verify major features of the online-game server party system with 5~7 GB memory and within 10 minutes execution time, and also found a hidden system error that passed existing testing process. This result shows the possibility of rigorous and effective verification with reasonable cost in comparison to manual testing.

Checking EMTLK Properties of Timed Interpreted Systems Via Bounded Model Checking

We investigate a SAT-based bounded model checking (BMC) method for EMTLK (the existential fragment of the metric temporal logic with knowledge) that is interpreted over timed models generated by timed interpreted systems. In particular, we translate the existential model checking problem for EMTLK to the existential model checking problem for a variant of linear temporal logic (called HLTLK), and we provide a SAT-based BMC technique for HLTLK. We evaluated the performance of our BMC by means of a variant of a timed generic pipeline paradigm scenario and a timed train controller system.

Bayesian logistic mixed-effects modelling of transect data: relating red tree coral presence to habitat characteristics

Abstract The collection of continuous data on transects is a common practice in habitat and fishery stock assessments; however, the application of standard regression models that assume independence to serially correlated data is problematic. We show that generalized linear mixed models (GLMMs), i.e. generalized linear models for longitudinal data, that are normally used for studies performed over time can also be applied to other types of clustered or serially correlated data. We apply a specific GLMM for longitudinal data, a hierarchical Bayesian logistic mixed-effects model (BLMM), to a marine ecology dataset obtained from submersible video recordings of the seabed on transects at two sites in the Gulf of Alaska. The BLMM was effective in relating the presence of red tree corals (Primnoa pacifica; i.e. binary data) to habitat characteristics: the presence of red tree corals is highly associated with bedrock as the primary substrate (estimated odds ratio 9–19), high to very high seabed roughness (estimated odds ratio 3–5), and medium to high slope (estimated odds ratio 2–3). The covariate depth was less important at the sites. We also demonstrate and compare two methods of model checking: full and mixed posterior predictive assessments, the latter of which provided a more realistic assessment, and we calculate the variance partition coefficient for reporting the variation explained by multiple levels of the hierarchical model.

Inference after checking multiple Bayesian models for data conflict and applications to mitigating the influence of rejected priors

Two major approaches have developed within Bayesian statistics to address uncertainty in the prior distribution and in the rest of the model. First, methods of model checking, including those assessing prior-data conflict, determine whether the posterior resulting from the model is adequate for purposes of inference and estimation or other decision-making. A potential drawback of this approach is that it provides little guidance for inference in the event that the model is found to be inadequate, that is, in conflict with the data. Second, the robust Bayes approach determines the sensitivity of inferences and decisions to the prior distribution and other model assumptions. This approach includes rules for making decisions on the basis of a set of posterior distributions corresponding to the set of reasonable model assumptions. Drawbacks of the second approach include the inability to criticize the set of models and the lack of guidance for specifying such a set. Those two approaches to model uncertainty are combined into a two-stage procedure in order to overcome each of their limitations. The first stage checks each model within a large class of models to assess which models are in conflict with the data and which are adequate for purposes of data analysis. The resulting set of adequate models is then used in the second stage either for summarizing a combined posterior such as a maximum-entropy posterior or for inference according to decision rules of the robust Bayes approach and of imprecise probability more generally. This proposed framework is illustrated by the application of a class of hierarchical models to a simple data set.

Probabilistic Analysis of Localized DNA Hybridization Circuits.

Molecular devices made of nucleic acids can perform complex information processing tasks at the nanoscale, with potential applications in biofabrication and smart therapeutics. However, limitations in the speed and scalability of such devices in a well-mixed setting can significantly affect their performance. In this article, we propose designs for localized circuits involving DNA molecules that are arranged on addressable substrates and interact via hybridization reactions. We propose designs for localized elementary logic circuits, which we compose to produce more complex devices, including a circuit for computing the square root of a four bit number. We develop an efficient method for probabilistic model checking of localized circuits, which we implement within the Visual DSD design tool. We use this method to prove the correctness of our circuits with respect to their functional specifications and to analyze their performance over a broad range of local rate parameters. Specifically, we analyze the extent to which our localized designs can overcome the limitations of well-mixed circuits, with respect to speed and scalability. To provide an estimate of local rate parameters, we propose a biophysical model of localized hybridization. Finally, we use our analysis to identify constraints in the rate parameters that enable localized circuits to retain their advantages in the presence of unintended interferences between strands.

Algorithms and models for complex natural systems

Analysing genomic data and complex natural phenomena in computational terms enhances our comprehension of both nature and computation. Thus, a cross fertilization of algorithms and models for natural complex systems at molecular, cellular, or higher levels, became an active research area, and a more in depth investigation of mutual relationships, synergies, similarities, and differences should be encouraged. This special issue is meant to foster novel hybrid approaches, including general methods of (bio) informatics and synthetic biology, as well as to present the new emerging research concerned with the study and analysis of genome organization, and with the design, modelling, and implementation of bio-inspired evolvable systems. Of particular interest are (unconventional) computational techniques designed to increase our understanding of the evolution of biological life, such as algorithms to infer gene structure and functioning, and parallel distributed computational models involving mechanisms of recognition, affinity based discrimination, reactivity and adaptation to the environment, and spatial search and moving. After a peer review process, 6 manuscripts were accepted for inclusion in this special issue. In ‘‘Combining flux balance analysis and model checking for metabolic network validation and analysis’’, by Roberto Pagliarini, Mara Sangiovanni, Adriano Peron, and Diego Di Bernardo, the authors present a novel useful approach for extracting relevant qualitative information from a metabolic network model, integrating constraintbased techniques with model checking methods. This new computational approach may be helpful in understanding the mechanisms governing the onset and progression of human metabolic-related disorders. It was applied to a simulation and analysis of a well-known inherited disease (primary hyperoxaluria type I) where the lack of a particular liver enzyme causes the body to accumulate excessive amounts of oxalate, leading to renal failure. InA hybrid method for inversion of 3DDC resistivity login measurements, by Ewa Gajda-Zagorska, Robert Schaefer, Maciej Smolka, Maciej Paszynski, and David Pardo, the authors present a new hybrid method for solving the challenging inversion of 3D direct current (DC) resistivity logging measurements. This methodology consists of a hp hierarchic genetic strategy (hp-HGS), and a gradient based optimization method for a local search. The problem has been formulated as a global optimization problem, and simulations have been performed using a self-adaptive hpfinite element method. The experimental results demonstrate the suitability of the proposed method for the tackled inversion problem. In An evolutionary procedure for inferring MP systems regulation functions of biological networks, by Alberto & Mario Pavone mpavone@dmi.unict.it

Assessing Item-Level Fit for the DINA Model.

This research focuses on developing item-level fit checking procedures in the context of diagnostic classification models (DCMs), and more specifically for the "Deterministic Input; Noisy 'And' gate" (DINA) model. Although there is a growing body of literature discussing model fit checking methods for DCM, the item-level fit analysis is not adequately discussed in literature. This study intends to take an initiative to fill in this gap. Two approaches are proposed, one stems from classical goodness-of-fit test statistics coupled with the Expectation-Maximization algorithm for model estimation, and the other is the posterior predictive model checking (PPMC) method coupled with the Markov chain Monte Carlo estimation. For both approaches, the chi-square statistic and a power-divergence index are considered, along with Stone's method for considering uncertainty in latent attribute estimation. A simulation study with varying manipulated factors is carried out. Results show that both approaches are promising if Stone's method is imposed, but the classical goodness-of-fit approach has a much higher detection rate (i.e., proportion of misfit items that are correctly detected) than the PPMC method.

Finding Optimal Timetables for Edinburgh Bus Routes

We present a novel application of stochastic simulation and model-checking methods to determining whether bus services are fulfilling their service-level agreement to provide on-time departures of buses from stops sufficiently often. We use open data on predicted bus arrival times to parameterise a stochastic model of a particular bus route from Edinburgh city centre out to suburban and rural areas to the south of the city. We validate and then analyse our stochastic model using both simulation and model-checking methods. Finally, we complete an optimisation study on the model and discover a better timetable for the service which would expose the bus service operator to less financial risk of penalties being applied by the regulatory authorities which define standards for bus service, punctuality and reliability.

Checking RTECTL properties of STSs via SMT-based Bounded Model Checking

We present an SMT-based bounded model checking (BMC) method for Simply-Timed Systems (STSs) and for the existential fragment of the Real-time Computation Tree Logic. We implemented the SMT-based BMC algorithm and compared it with the SAT-based BMC method for the same systems and the same property language on several benchmarks for STSs. For the SAT-based BMC we used the PicoSAT solver and for the SMT-based BMC we used the Z3 solver. The experimental results show that the SMT-based BMC performs quite well and is, in fact, sometimes significantly faster than the tested SAT-based BMC.

Posterior predictive model checks for cognitive diagnostic models

Cognitive diagnostic models (CDMs; DiBello et al., 2007) have received increasing attention in educational measurement for the purpose of diagnosing examinees' strengths and weaknesses of their latent attributes. Despite the current popularity of a number of diagnostic models, research on assessing model-data fit has been limited. The current study applies one of the Bayesian model checking methods, namely the posterior predictive model check (PPMC) method (Rubin, 1984) to investigate model misfit. Specifically, we aim to employ the technique to investigate model-data misfit from various diagnostic models using real data and a simulation study. An important issue with the application of PPMC is the choice of discrepancy measure. This study examines the performance of three discrepancy measures for assessing different aspects of model fit: observed total-scores distribution, association of item pairs, and correlation of attribute pairs as adequate measures for the diagnostic models.

Construction and verification of PLC LD programs by the LTL specification

An approach to the construction and verification of LD programs of programmable logic controllers (PLC) for discrete problems is proposed. The specification of the program behavior is performed using the linear temporal logic language (LTL). Programming is performed using the Ladder Diagram (LD) language by the LTL specification. The correctness analysis of the LTL specification is performed using the Cadence SMV symbolic model-checking tool. An approach to programming and verifying the PLC LD programs is shown using an example. The LD program, its LTL specification, and an SMV model are given for a discrete problem. This article is aimed at the description of an approach to PLC programming that will provide the correctness analysis of PLC LD programs using the model-checking method. Therefore, the variation in each programmable variable is described using a pair of LTL formulas. The first LTL formula describes situations in which the corresponding variable increases and the second LTL formula specifies conditions leading to a decrease in the variable. LTL formulas, which are considered to specify the behavior of variables, are constructive in the sense that the PLC program corresponding to the temporal properties expressed by these formulas is constructed by them. Thus, PLC programming is reduced to the construction of the LTL specification for the behavior of each programmed variable. In addition, the SMV model of the PLC LD program is constructed by the LTL specification. Then, the SMV model is analyzed for correctness (relative to additional conventional general-programming LTL properties) by the Cadence SMV symbolic model-checking tool.

The Security Study of an EC Protocol Based on Casper/FDR2

Researchers have proposed several security protocols to protect the electronic commerce security in these years; however, not all of them are secure enough. This article extends model checking method with Casper/FDR2 to model and analyze a new electronic protocol. Attacks are found in the protocol and their mechanisms are discussed. A variety of solutions are given to different security flaws. The improved protocol is proven to be robust and secure.

Modeling and Verifying of CPS Component Services Based on Hybrid Automata

In recent years, the modeling and verifying of Cyber-Physical System (CPS) is now an important aspect of CPS researches. Because of the CPS’ complex architecture, it may suffer from the state-space explosion problem when we verify CPS models by model checking methods. Therefore, we offer a method which models CPS with Component Services. The method treats the CPS components as a service provider, and models component services to further simplify the system’s state-space. We verify the correctness of this model and solve the synchronous/asynchronous communication problems.

Summarization of Boolean satisfiability verification

As a complementary technology to Binary Decision Diagram-based (BDD-based) symbolic model checking, the verification techniques on Boolean satisfiability problem have gained an increasing wide of applications over the last few decades, which brings a dramatic improvement for automatic verification. In this paper, we firstly introduce the theory about the Boolean satisfiability verification, including the description on the problem of Boolean satisfiability verification, Davis-Putnam-Logemann-Loveland (DPLL) based complete verification algorithm, and all kinds of solvers generated and the logic languages used by those solvers. Moreover, we formulate a large number optimizations of technique revolutions based on Boolean SATisfiability (SAT) and Satisfiability Modulo Theories (SMT) solving in detail, including incomplete methods such as bounded model checking, and other methods for concurrent programs model checking. Finally, we point out the major challenge pervasively in industrial practice and prospect directions for future research in the field of formal verification.

Combining flux balance analysis and model checking for metabolic network validation and analysis

Several human diseases are caused by metabolism defects. Discovering the mechanisms that govern the onset and progression of human metabolism-related diseases is not a straightforward process. Computational approaches, such as the flux balance analysis, have been successfully used to extract useful knowledge on the metabolic dysregulation processes from genome-scale network models. In this work, we propose a novel approach which integrates constraint-based techniques with model checking methods, with the aim to extract relevant qualitative information from a metabolic network model. As a case study, we applied our methodology to the simulation and analysis of the primary hyperoxaluria type I, an inherited disease in which the lack of a particular liver enzyme causes the kidney to accumulate excessive amounts of oxalate.

Computation tree logic model checking based on possibility measures

In order to deal with the systematic verification with uncertain information in possibility theory, Li and Li (2013) introduced model checking of linear-time properties in which the uncertainty is modeled by possibility measures. Xue, Lei and Li (2011) defined computation tree logic (CTL) based on possibility measures, which is called possibilistic CTL (PoCTL). This paper is a continuation of the above work. First, we study the expressiveness of PoCTL. Unlike probabilistic CTL, it is shown that PoCTL (in particular, qualitative PoCTL) is more powerful than CTL with respect to their expressiveness. The equivalent expressions of basic CTL formulae using qualitative PoCTL formulae are presented in detail. Some PoCTL formulae that cannot be expressed by any CTL formulae are presented. In particular, some qualitative properties of repeated reachability and persistence are expressed using PoCTL formulae. Next, adapting CTL model-checking algorithm, a method to solve the PoCTL model-checking problem and its time complexity are discussed in detail. Finally, an example is given to illustrate the PoCTL model-checking method.

Use of posterior predictive checks as an inferential tool for investigating individual heterogeneity in animal population vital rates.

The investigation of individual heterogeneity in vital rates has recently received growing attention among population ecologists. Individual heterogeneity in wild animal populations has been accounted for and quantified by including individually varying effects in models for mark–recapture data, but the real need for underlying individual effects to account for observed levels of individual variation has recently been questioned by the work of Tuljapurkar et al. (Ecology Letters, 12, 93, 2009) on dynamic heterogeneity. Model-selection approaches based on information criteria or Bayes factors have been used to address this question. Here, we suggest that, in addition to model-selection, model-checking methods can provide additional important insights to tackle this issue, as they allow one to evaluate a model's misfit in terms of ecologically meaningful measures. Specifically, we propose the use of posterior predictive checks to explicitly assess discrepancies between a model and the data, and we explain how to incorporate model checking into the inferential process used to assess the practical implications of ignoring individual heterogeneity. Posterior predictive checking is a straightforward and flexible approach for performing model checks in a Bayesian framework that is based on comparisons of observed data to model-generated replications of the data, where parameter uncertainty is incorporated through use of the posterior distribution. If discrepancy measures are chosen carefully and are relevant to the scientific context, posterior predictive checks can provide important information allowing for more efficient model refinement. We illustrate this approach using analyses of vital rates with long-term mark–recapture data for Weddell seals and emphasize its utility for identifying shortfalls or successes of a model at representing a biological process or pattern of interest.We show how posterior predictive checks can be used to strengthen inferences in ecological studies. We demonstrate the application of this method on analyses dealing with the question of individual reproductive heterogeneity in a population of Antarctic pinnipeds.

Architecture and software implementation of the system of verification of filtering rules

The paper describes the general architecture of the system of verification of filter rules firewall and discusses aspects of the software implementation. The implementation is based on the method of model checking. SPIN software system is used as a verifier. Also designed user interface is described in the paper. It allows to download data on verifiable system and filtering policy rules. The user interface includes elements for management verification process and presentation of its results. In addition, the proposed system allows using different strategies to resolve the anomalies.

Scheduling analysis based on model checking for multiprocessor real-time systems

Real-time systems (RTS) are omnipresent in several domains. The trend is to use multiprocessor architecture to satisfy the timing constraints of such systems. The model-checking methods have proven to be useful for making the development process reliable at a high abstraction level. Based on this approach, the present paper proposes a new technique for scheduling analysis of a partitioned multiprocessor RTS. Starting from a model with dynamic priority time Petri Nets modeling the system, we have proposed a generation of a reduced states graph. Thus, through the properties of the graph the schedulability is checked. Our approach provides an implementation of a Partition Checker tool, which produces an affirmation of the schedulability or a counterexample in the case of non-schedulable system to reduce the SW/HW space exploration.

Terminal Satisfiability in GSTE

Generalized symbolic trajectory evaluation(GSTE) is an extension of symbolic trajectory evaluation (STE) and a method of model checking. GSTE specifications are given as assertion graphs. There are four efficient methods to verify whether a circuit model obeys an assertion graph in GSTE, Model Checking Strong Satisfiability (SMC), Model Checking Normal Satisfiability (NMC), Model Checking Fair Satisfiability (FMC), and Model Checking Terminal Satisfiability (TMC). SMC, NMC, and FMC have been proved and applied in industry, but TMC has not. This paper gives a six-tuple definition and presents a new algorithm for TMC. Based on these, we prove that our algorithm is sound and complete. It solves the SMC’s limitation (resulting in false negative) without extending from finite specification to infinite specification. At last, a case of using TMC to verify a realistic hardware circuit round-robin arbiter is achieved. Avoiding verifying the undesired paths which are not related to the specifications, TMC makes it possible to reduce the computational complexity, and the experimental results suggest that the time cost by SMC is 3.14× with TMC in the case.