Language-Emptiness Checking of Alternating Tree Automata Using Symbolic Reachability Analysis

AbstractAlternating tree automata and AND/OR graphs provide elegant formalisms that enable branching- time logics to be verified in linear time. The seminal work of Kupferman et al. [Orna Kupferman, Moshe Y. Vardi, and Pierre Wolper. An automata-theoretic approach to branching-time model checking. J. ACM, 47(2):312–360, 2000] showed that 1) branching-time model checking is reducible to the language non-emptiness checking of the product of two alternating automata representing the model and property under verification, and 2) the non-emptiness problem can be solved by performing a search on an AND/OR graph representing this product. Their algorithm, however, can only be implemented in an explicit-state model checker because it needs stacks to detect accept and reject runs. In this paper, we propose a BDD-based approach to check the language non-emptiness of the product automaton. We use a technique called “state recording” from Schuppan and Biere [Viktor Schuppan and Armin Biere. Efficient reduction of finite state model checking to reachability analysis. Int. Journal on Software Tools for Technology Transfer (STTT), 5(2–3):185–204, 2004] to emulate the stack mechanism from explicit-state model checking. This technique allows us to transform the product automaton into a well-defined AND/OR graph. We develop a BDD-based reachability algorithm to efficiently determine whether a solution graph for the AND/OR graph exists and thereby solve the model-checking problem. While “state recording” increases the size of the state space, the advantage of our approach lies in the memory saving BDDs can offer and the potential it opens up for optimisation of the reachability analysis. We remark that this technique always detects the shortest counter-example

Underapproximation for model-checking based on universal circuits

AbstractFor two naturals m,n such that m<n, we show how to construct a circuit C with m inputs and n outputs, that has the following property: for some 0⩽k⩽m, the circuit defines a k-universal function. This means, informally, that for every subset K of k outputs, every possible valuation of the variables in K is reachable.Now consider a circuit M with n inputs that we wish to model-check. Connecting the inputs of M to the outputs of C gives us a new circuit M′ with m inputs, that its original inputs have freedom defined by k. This is a very attractive feature for underapproximation in model-checking: on one hand the combined circuit has a smaller number of inputs, and on the other hand it is expected to find an error state fast if there is one.We show a random construction of a k-universal circuit that guarantees that k is very close to m, with an arbitrarily high probability. We also present a deterministic construction of such a circuit, but here the value of k is smaller with respect to a fixed value of m. We report initial experimental results with bounded model checking of industrial designs (the method is equally applicable to unbounded model checking and to simulation), which shows mixed results. An interesting observation, however, is that in 13 out of 17 designs, setting m to be n/5 is sufficient to detect the bug. This is in contrast to other underapproximation techniques that are based on reducing the number of inputs, which in most cases cannot detect the bug even with m=n/2

Survey on Directed Model Checking

International audienceThis article surveys and gives historical accounts to the algorithmic essentials of directed model checking, a promising bug-hunting technique to mitigate the state explosion problem. In the enumeration process, successor selection is prioritized. We discuss existing guidance and methods to automatically generate them by exploiting system abstractions. We extend the algorithms to feature partial-order reduction and show how liveness problems can be adapted by lifting the search Space. For deterministic, finite domains we instantiate the algorithms to directed symbolic, external and distributed search. For real-time domains we discuss the adaption of the algorithms to timed automata and for probabilistic domains we show the application to counterexample generation. Last but not least, we explain how directed model checking helps to accelerate finding solutions to scheduling problems

Partitioning Interpolant-Based Verificationfor effective Unbounded Model Checking

Interpolant-based model checking has been shown to be effective on large verification instances, as it efficiently combines automated abstraction and reachability fixed-point checks. On the other hand, methods based on variable quantification have proved their ability to remove free inputs, thus projecting the search space over state variables. In this paper we propose an integrated approach which combines the abstraction power of interpolation with techniques that rely on AIG and/or BDD representations of states, directly supporting variable quantification and fixed-point checks. The underlying idea of this combination is to adopt AIG- or BDD-based quantifications to limit and restrict the search space and the complexity of the interpolant-based approach. The exploited strategies, most of which are individually well-known, are integrated with a new flavor, specifically designed to improve their effectiveness on difficult verification instances. Experimental results, specifically oriented to hard-to-solve verification problems, show the robustness of our approach

BDD Minimization for Approximate Computing

We present Approximate BDD Minimization (ABM) as a problem that has application in approximate computing. Given a BDD representation of a multi-output Boolean function, ABM asks whether there exists another function that has a smaller BDD representation but meets a threshold w.r.t. an error metric. We present operators to derive approximated functions and present algorithms to exactly compute the error metrics directly on the BDD representation. An experimental evaluation demonstrates the applicability of the proposed approaches

Record and play: a structural fixed point iteration for sequential circuit verification

Abstract This paper propose

Record and play: a structural fixed point iteration for sequential circuit verification

Abstract This paper propose

Anytime Algorithms for ROBDD Symmetry Detection and Approximation

Reduced Ordered Binary Decision Diagrams (ROBDDs) provide a dense and memory efficient representation of Boolean functions. When ROBDDs are applied in logic synthesis, the problem arises of detecting both classical and generalised symmetries. State-of-the-art in symmetry detection is represented by Mishchenko's algorithm. Mishchenko showed how to detect symmetries in ROBDDs without the need for checking equivalence of all co-factor pairs. This work resulted in a practical algorithm for detecting all classical symmetries in an ROBDD in O(|G|3) set operations where |G| is the number of nodes in the ROBDD. Mishchenko and his colleagues subsequently extended the algorithm to find generalised symmetries. The extended algorithm retains the same asymptotic complexity for each type of generalised symmetry. Both the classical and generalised symmetry detection algorithms are monolithic in the sense that they only return a meaningful answer when they are left to run to completion. In this thesis we present efficient anytime algorithms for detecting both classical and generalised symmetries, that output pairs of symmetric variables until a prescribed time bound is exceeded. These anytime algorithms are complete in that given sufficient time they are guaranteed to find all symmetric pairs. Theoretically these algorithms reside in O(n3+n|G|+|G|3) and O(n3+n2|G|+|G|3) respectively, where n is the number of variables, so that in practice the advantage of anytime generality is not gained at the expense of efficiency. In fact, the anytime approach requires only very modest data structure support and offers unique opportunities for optimisation so the resulting algorithms are very efficient. The thesis continues by considering another class of anytime algorithms for ROBDDs that is motivated by the dearth of work on approximating ROBDDs. The need for approximation arises because many ROBDD operations result in an ROBDD whose size is quadratic in the size of the inputs. Furthermore, if ROBDDs are used in abstract interpretation, the running time of the analysis is related not only to the complexity of the individual ROBDD operations but also the number of operations applied. The number of operations is, in turn, constrained by the number of times a Boolean function can be weakened before stability is achieved. This thesis proposes a widening that can be used to both constrain the size of an ROBDD and also ensure that the number of times that it is weakened is bounded by some given constant. The widening can be used to either systematically approximate an ROBDD from above (i.e. derive a weaker function) or below (i.e. infer a stronger function). The thesis also considers how randomised techniques may be deployed to improve the speed of computing an approximation by avoiding potentially expensive ROBDD manipulation