Symbolic model checking for self-stabilizing algorithms

A distributed system is said to be self-stabilizing if it converges to safe states regardless of its initial state. In this paper we present our results of using symbolic model checking to verify distributed algorithms against the self-stabilizing property. In general, the most difficult problem with model checking is state explosion; it is especially serious in verifying the self-stabilizing property, since it requires the examination of all possible initial states. So far applying model checking to self-stabilizing algorithms has not been successful due to the problem of state explosion. In order to overcome this difficulty, we propose to use symbolic model checking for this purpose. Symbolic model checking is a verification method which uses Ordered Binary Decision Diagrams (OBDDs) to compactly represent state spaces. Unlike other model checking techniques, this method has the advantage that most of its computations do not depend on the initial states. We show how to verify the correctness of algorithms by means of SMV, a well-known symbolic model checker. By applying the proposed approach to several algorithms in the literature, we demonstrate empirically that the state spaces of self-stabilizing algorithms can be represented by OBDDs very efficiently. Through these case studies, we also demonstrate the usefulness of the proposed approach in detecting errors     

A vertex centric parallel algorithm for linear temporal logic model checking in Pregel

Linear Temporal Logic (LTL) Model Checking is a very important and popular technique for the automatic verification of safety-critical hardware and software systems, aiming at ensuring their quality. However, it is well known that LTL model checking suffers from the state explosion problem, often leading to insurmountable scalability problems when applying it to real-world systems. While there has been work on distributed algorithms for explicit on-the-fly LTL model checking, these are not sufficiently scalable and capable of tolerating faults during computation, significantly limiting their usefulness in huge cluster environments. Moreover, implementing these algorithms is generally viewed as a very challenging, error-prone task. In this paper, we instead rely on Pregel, a simple yet powerful model for distributed computation on large graphs. Pregel has from the start been designed for efficient, scalable and fault tolerant operation on clusters of thousands of computers, including large cloud setups. To harness Pregel’s power, we propose a new vertex centric distributed algorithm for explicit LTL model checking of concurrent systems. Experimental results illustrate feasibility and scalability of the proposed algorithm. Compared with other distributed algorithms, our algorithm is more scalable, reliable and efficient.     

一种基于扩展不完全Kripke结构的三值逻辑模型检测方法

多值模型检测是解决形式化验证中状态爆炸问题的一种重要方法,三值模型检测是多值模型检测的基础,其中如何检验不确定状态的真值是一难点。针对不确定状态检验,提出了一种模型检测方法,首先对不完全Kripke结构PKS进行了扩展,然后在扩展后的模型上给出了检测不确定状态真值的方法,最后给出了基于扩展不完全Kripke结构的三值逻辑模型检测算法。与已有的三值逻辑模型检测算法相比,该算法降低了算法复杂度,完善了对于不确定或不一致信息的处理,从而增强了三值逻辑模型检测的实用性。     

Distributed Explicit Bounded LTL Model Checking

Automated formal verification becomes a significant part of an industrial design process. Favourite formal verification method  model checking  is strongly limited by the size of the model of the verified system. It suffers from the so called state explosion problem. We propose to fight this problem by applying the idea of bounding the examined state space in explicit model checking. Moreover, we combine this approach with the distribution of the computation among the network of workstations. We consider several distributed bounded LTL model checking algorithms and carry out a series of experiments to evaluate them and to compare their behaviour.     

Bounded Model Checking Using Satisfiability Solving

The phrase model checking refers to algorithms for exploring the state space of a transition system to determine if it obeys a specification of its intended behavior. These algorithms can perform exhaustive verification in a highly automatic manner, and, thus, have attracted much interest in industry. Model checking programs are now being commercially marketed. However, model checking has been held back by the state explosion problem, which is the problem that the number of states in a system grows exponentially in the number of system components. Much research has been devoted to ameliorating this problem.In this tutorial, we first give a brief overview of the history of model checking to date, and then focus on recent techniques that combine model checking with satisfiability solving. These techniques, known as bounded model checking, do a very fast exploration of the state space, and for some types of problems seem to offer large performance improvements over previous approaches. We review experiments with bounded model checking on both public domain and industrial designs, and propose a methodology for applying the technique in industry for invariance checking. We then summarize the pros and cons of this new technology and discuss future research efforts to extend its capabilities.     

并发反应式系统的组合模型检验与组合精化检验

模型检验和精化检验是两种重要的形式验证方法,其应用的主要困难在于如何缓解状态爆炸问题.基于分而治之的思想进行组合模型检验和组合精化检验是应对这个问题的重要方法,它们利用系统的组合结构对问题进行分解,通过对各子系统性质的检验和综合推理导出整个系统的性质.在一个统一的框架下对组合模型检验和组合精化检验作了系统的分析和归纳,从模块检验的角度阐述了上述两种组合验证方法的原理及其相应的组合验证策略.同时总结了各类问题的复杂性,并对上述两种方法作了比较分析,揭示了它们之间的内在联系.最后展望了组合模型检验与组合精化检验的发展方向.     

Symbolic Model Checking of Software

Model checking is a popular formal verification technique for both software and hardware. The verification of concurrent software predominantly employs explicit-state model checkers, such as SPIN, that use partial-order reduction as a main technique to deal with large state spaces efficiently. In the hardware domain, the introduction of symbolic model checking has been considered a breakthrough, allowing the verification of systems clearly out-of-reach of any explicit-state model checker.This paper introduces ImProviso, a new algorithm for model checking of software that efficiently combines the advantages of partial-order reduction with symbolic exploration. IMPROVISO uses implicit BDD representations for both the state space and the transition relation together with a new implicit in-stack proviso for efficient partial-order reduction. The new approach is inspired by the Twophase partial-order reduction algorithm for explicit-state model checking.Initial experimental results show that the proposed algorithm improves the existing symbolic model checking approach and can be used to tackle problems that are not tractable using explicit-state methods.     

模型检测新技术研究

1 引言软件是否可信赖已成为一个国家的经济、国防等系统能否正常运转的关键因素之一,尤其在一些诸如核反应堆控制、航空航天以及铁路调度等安全悠关(safety-critical)领域更是如此。这类系统要求绝对安全可靠,不容半点疏漏,否则将导致灾难性后果。如1996年6月4日,欧洲航天局阿丽亚娜(Ariane)501火箭因为其控制软件的规范和设计错误而导致发射37秒后爆炸。类似的报道屡见不鲜,如何确保这些系统的可靠性成为计算机科学与控制论领域共同关注的一个焦点问题。     

基于模拟关系的精化检测方法

精化检测是一种重要的形式化验证方法,将系统实现和性质规约用相同形式化语言进行建模,如能证明两者间存在某种精化关系且该关系能够维持性质,可得出系统实现满足性质规约.为验证不同类型的系统性质, traces、stable failures和failures-divergence精化检测方法已被提出.精化检测算法依赖于子集构造,因而其面临状态空间爆炸问题.近年来,已有学者针对NFA语言包含问题提出了基于模拟关系的状态空间消减方法,大大提高了算法性能,且该方法能直接用于traces精化检测.在此基础上,本文提出了基于模拟关系的stable failures和failures-divergence精化检测方法.此外,本文还将精化检测扩展到了时间系统的验证中,提出了基于模拟关系的时间自动机traces精化检测方法.实验结果表明,基于模拟关系的算法效率有很大提高.     

Automatic Verification of Fault-Tolerant Register Emulations

The design and verification of fault-tolerant distributed algorithms is a complicated task. Usually, the proof of correctness is done manually, and thus depends on the skill of the prover. Using automated verification methods, such as model checking, can greatly simplify the verification. However, model checking of distributed algorithms is often intractable because of the state-explosion problem. In this paper we present a novel approach to verification of quorum-based distributed register emulation algorithms with undetectable crash failures of processes. Our approach is based on projection and abstraction and allows us to reduce the task of model-checking the whole system to fair model-checking of subsystems consisting of a constant number of processes. Our method is highly scalable and can be applied to a large class of algorithms. Aside from efficient verification, it can also be used for finding redundancies in existing algorithms.     

Model checking temporal knowledge and commitments in multi-agent systems using reduction

Though modeling and verifying Multi-Agent Systems (MASs) have long been under study, there are still challenges when many different aspects need to be considered simultaneously. In fact, various frameworks have been carried out for modeling and verifying MASs with respect to knowledge and social commitments independently. However, considering them under the same framework still needs further investigation, particularly from the verification perspective. In this article, we present a new technique for model checking the logic of knowledge and commitments (CTLKC⁺). The proposed technique is fully-automatic and reduction-based in which we transform the problem of model checking CTLKC⁺ into the problem of model checking an existing logic of action called ARCTL. Concretely, we construct a set of transformation rules to formally reduce the CTLKC⁺ model into an ARCTL model and CTLKC⁺ formulae into ARCTL formulae to get benefit from the extended version of NuSMV symbolic model checker of ARCTL. Compared to a recent approach that reduces the problem of model checking CTLKC⁺ to another logic of action called GCTL¹, our technique has better scalability and efficiency. We also analyze the complexity of the proposed model checking technique. The results of this analysis reveal that the complexity of our reduction-based procedure is PSPACE-complete for local concurrent programs with respect to the size of these programs and the length of the formula being checked. From the time perspective, we prove that the complexity of the proposed approach is P-complete with regard to the size of the model and length of the formula, which makes it efficient. Finally, we implement our model checking approach on top of extended NuSMV and report verification results for the verification of the NetBill protocol, taken from business domain, against some desirable properties. The obtained results show the effectiveness of our model checking approach when the system scales up.     

A new verification technique for large processes based on identification of relevant tasks

Verification recently has become a challenging topic for business process languages. Verification techniques like model checking allow to ensure that a process complies with domain-specific requirements, prior to the execution. To execute full-state verification techniques like model checking, the state space of the process needs to be constructed. This tends to increase exponentially with the size of the process schema, or it can even be infinite. We address this issue by means of requirements-specific reduction techniques, i.e., reducing the size of the state space without changing the result of the verification. We present an approach that, for a given requirement the system must fulfill, identifies the tasks relevant for the verification. Our approach then uses these relevant tasks for a reduction that confines the process to regions of interest for the verification. To evaluate our new technique, we use real-world industrial processes and requirements. Mainly because these processes make heavy use of parallelization, full-state-search verification algorithms are not able to verify them. With our reduction in turn, even complex processes with many parallel branches can be verified in less than 10 s.     

Confirming configurations in EFSM testing

We investigate the problem of configuration verification for the extended FSM (EFSM) model. This is an extension of the FSM state identification problem. Specifically, given a configuration ("state vector") and an arbitrary set of configurations, determine an input sequence such that the EFSM in the given configuration produces an output sequence different from that of the configurations in the given set or at least in a maximal proper subset. Such a sequence can be used in a test case to confirm the destination configuration of a particular EFSM transition. We demonstrate that this problem could be reduced to the EFSM traversal problem, so that the existing methods and tools developed in the context of model checking become applicable. We introduce notions of EFSM projections and products and, based on these notions, we develop a theoretical framework for determining configuration-confirming sequences. The proposed approach is illustrated on a realistic example.     

Counterexample-guided Spatial Flow Model Checking Methods for C Code

Software verification has always been a popular research topic to ensure the correctness and security of software. However, due to the complex semantics and syntax of programming languages, the formal methods for verifying the correctness of programs have the problems of low accuracy and low efficiency. In particular, the state change in address space caused by pointer operations makes it difficult to guarantee the verification accuracy of existing model checking methods. By combining model checking and sparse value-flow analysis, this paper designs a spatial flow model to effectively describe the state behavior of C code at the symbolic-variable level and address-space level and proposes a model checking algorithm of CounterExample-Guided Abstraction refinement and Sparse value-flow strong update (CEGAS), which enables points-to-sensitive formal verification for C code. This paper establishes a C-code benchmark containing a variety of pointer operations and conducts comparative experiments on the basis of this benchmark. These experiments indicate that in the task of analyzing multi-class C code features, the model checking algorithm CEGAS proposed in this paper can achieve outstanding results compared with the existing model checking tools. The verification accuracy of CEGAS is 92.9%, and the average verification time of each line of code is 2.58 ms, both of which are better than those of existing verification tools.     

Bayesian statistical model checking with application to Stateflow/Simulink verification

We address the problem of model checking stochastic systems, i.e., checking whether a stochastic system satisfies a certain temporal property with a probability greater (or smaller) than a fixed threshold. In particular, we present a Statistical Model Checking (SMC) approach based on Bayesian statistics. We show that our approach is feasible for a certain class of hybrid systems with stochastic transitions, a generalization of Simulink/Stateflow models. Standard approaches to stochastic discrete systems require numerical solutions for large optimization problems and quickly become infeasible with larger state spaces. Generalizations of these techniques to hybrid systems with stochastic effects are even more challenging. The SMC approach was pioneered by Younes and Simmons in the discrete and non-Bayesian case. It solves the verification problem by combining randomized sampling of system traces (which is very efficient for Simulink/Stateflow) with hypothesis testing (i.e., testing against a probability threshold) or estimation (i.e., computing with high probability a value close to the true probability). We believe SMC is essential for scaling up to large Stateflow/Simulink models. While the answer to the verification problem is not guaranteed to be correct, we prove that Bayesian SMC can make the probability of giving a wrong answer arbitrarily small. The advantage is that answers can usually be obtained much faster than with standard, exhaustive model checking techniques. We apply our Bayesian SMC approach to a representative example of stochastic discrete-time hybrid system models in Stateflow/Simulink: a fuel control system featuring hybrid behavior and fault tolerance. We show that our technique enables faster verification than state-of-the-art statistical techniques. We emphasize that Bayesian SMC is by no means restricted to Stateflow/Simulink models. It is in principle applicable to a variety of stochastic models from other domains, e.g., systems biology.     

CAESAR_SOLVE: A generic library for on-the-fly resolution of alternation-free Boolean equation systems

Boolean equation systems (Bess) provide a useful framework for modeling various verification problems on finite-state concurrent systems, such as equivalence checking and model checking. These problems can be solved on the fly (i.e., without constructing explicitly the state space of the system under analysis) by using a demand-driven construction and resolution of the corresponding Bes. In this article, we present a generic software library dedicated to on-the-fly resolution of alternation-free Bess. Four resolution algorithms are currently provided by the library: algorithms A1 and A2 are general, the latter being optimized to produce small-depth diagnostics, whereas algorithms A3 and A4 are specialized for handling acyclic and disjunctive/conjunctive Bess in a memory-efficient way. The library has been developed within the Cadp verification toolbox using the generic Open/Caesar environment and is currently used for three purposes: on-the-fly equivalence checking modulo five widely used equivalence relations, on-the-fly model checking of regular alternation-free modal μ-calculus, and on-the-fly reduction of state spaces based on τ-confluence .     

Module Checking

In computer system design, we distinguish between closed and open systems. A closed system is a system whose behavior is completely determined by the state of the system. An open system is a system that interacts with its environment and whose behavior depends on this interaction. The ability of temporal logics to describe an ongoing interaction of a reactive program with its environment makes them particularly appropriate for the specification of open systems. Nevertheless, model-checking algorithms used for the verification of closed systems are not appropriate for the verification of open systems. Correct model checking of open systems should check the system with respect to arbitrary environments and should take into account uncertainty regarding the environment. This is not the case with current model-checking algorithms and tools. In this paper we introduce and examine the problem of model checking of open systems (module checking, for short). We show that while module checking and model checking coincide for the linear-time paradigm, module checking is much harder than model checking for the branching-time paradigm. We prove that the problem of module checking is EXPTIME-complete for specifications in CTL and 2EXPTIME-complete for specifications in CTL*. This bad news is also carried over when we consider the program-complexity of module checking. As good news, we show that for the commonly-used fragment of CTL (universal, possibly, and always possibly properties), current model-checking tools do work correctly, or can be easily adjusted to work correctly, with respect to both closed and open systems.     

Automated circular assume-guarantee reasoning

Model checking is a successful approach for verifying hardware and software systems. Despite its success, the technique suffers from the state explosion problem which arises due to the large state space of real-life systems. One solution to the state explosion problem is compositional verification, that aims to decompose the verification of a large system into the more manageable verification of its components. To account for dependencies between components, assume-guarantee reasoning defines rules that break-up the global verification of a system into local verification of individual components, using assumptions about the rest of the system. In recent years, compositional techniques have gained significant successes following a breakthrough in the ability to automate assume-guarantee reasoning. However, automation has been restricted to simple acyclic assume-guarantee rules. In this work, we focus on automating circular assume-guarantee reasoning in which the verification of individual components mutually depends on each other. We use a sound and complete circular assume-guarantee rule and we describe how to automatically build the assumptions needed for using the rule. Our algorithm accumulates joint constraints on the assumptions based on (spurious) counterexamples obtained from checking the premises of the rule, and uses a SAT solver to synthesize minimal assumptions that satisfy these constraints. To the best of our knowledge, our work is the first to fully automate circular assume-guarantee reasoning. We implemented our approach and compared it with established non-circular compositional methods that use learning or SAT-based techniques. The experiments show that the assumptions generated for the circular rule are generally smaller, and on the larger examples, we obtain a significant speedup.     

Common Knowledge and Update in Finite Environments

Logics of knowledge have been shown to provide a useful approach to the high level specification and analysis of distributed systems. It has been proposed that such systems can be developed using knowledge- based protocols, in which agents' actions have preconditions that test their state of knowledge. Both computer-assisted analysis of the knowledge properties of systems and automated compilation of knowledge-based protocols require the development of algorithms for the computation of states of knowledge. This paper studies one of the computational problems of interest, the model checking problem for knowledge formulae in the S5_nKripke structures generated by finite state environments in which states determine an observation for each agent. Agents are assumed to have perfect recall and may operate synchronously or asynchronously. It is shown that, in this setting, model checking of common knowledge formulae is intractable, but efficient incremental algorithms are developed for formulae containing only knowledge operators. Connections to knowledge updates and compilation of knowledge-based protocols are discussed.     

Modeling and verifying probabilistic Multi-Agent Systems using knowledge and social commitments

Multi-Agent Systems (MASs) have long been modeled through knowledge and social commitments independently. In this paper, we present a new method that merges the two concepts to model and verify MASs in the presence of uncertainty. To express knowledge and social commitments simultaneously in uncertain settings, we define a new multi-modal logic called Probabilistic Computation Tree Logic of Knowledge and Commitments (PCTL^kc in short) which combines two existing probabilistic logics namely, probabilistic logic of knowledge PCTLK and probabilistic logic of commitments PCTLC. To model stochastic MASs, we present a new version of interpreted systems that captures the probabilistic behavior and accounts for the communication between interacting components. Then, we introduce a new probabilistic model checking procedure to check the compliance of target systems against some desirable properties written in PCTL^kc and report the obtained verification results. Our proposed model checking technique is reduction-based and consists in transforming the problem of model checking PCTL^kc into the problem of model checking a well established logic, namely PCTL. So doing provides us with the privilege of re-using the PRISM model checker to implement the proposed model checking approach. Finally, we demonstrate the effectiveness of our approach by presenting a real case study. This framework can be considered as a step forward towards closing the gap of capturing interactions between knowledge and social commitments in stochastic agent-based systems.