Regular Model Checking Using Solver Technologies and Automata Learning

Neider, Daniel; Jansen, Nils

doi:10.1007/978-3-642-38088-4_2

Cited by 23 publications

(40 citation statements)

References 12 publications

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“…1<i≤n 1≤j<i 9) constants true and false, which are implemented analogously. Moreover, our SAT encoding is extensible, and additional LTL operators such as weak until or weak and strong release can easily be added.…”

Section: A Sat-based Learning Algorithmmentioning

confidence: 99%

Learning Linear Temporal Properties

Neider

Gavran

2018

2018 Formal Methods in Computer Aided Design (FMCAD)

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We present two novel algorithms for learning formulas in Linear Temporal Logic (LTL) from examples. The first learning algorithm reduces the learning task to a series of satisfiability problems in propositional Boolean logic and produces a smallest LTL formula (in terms of the number of subformulas) that is consistent with the given data. Our second learning algorithm, on the other hand, combines the SAT-based learning algorithm with classical algorithms for learning decision trees. The result is a learning algorithm that scales to real-world scenarios with hundreds of examples, but can no longer guarantee to produce minimal consistent LTL formulas. We compare both learning algorithms and demonstrate their performance on a wide range of synthetic benchmarks. Additionally, we illustrate their usefulness on the task of understanding executions of a leader election protocol.

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Section: A Sat-based Learning Algorithmmentioning

confidence: 99%

Learning Linear Temporal Properties

Neider

Gavran

2018

2018 Formal Methods in Computer Aided Design (FMCAD)

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“…The framework of regular proofs is incomplete in general since it could happen that there is a proof, but no regular proof. The pathological cases when only non-regular proofs exist do not, however, seem to frequently occur in practice, e.g., see [44,45,18,14,19,46,47,15,48].…”

Section: Regular Model Checking: a Symbolic Frameworkmentioning

confidence: 99%

Fair Termination for Parameterized Probabilistic Concurrent Systems

Lengál

Lin

Majumdar

et al. 2017

Tools and Algorithms for the Construction and Analysis of Systems

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Abstract. We consider the problem of automatically verifying that a parameterized family of probabilistic concurrent systems terminates with probability one for all instances against adversarial schedulers. A parameterized family defines an infinite-state system: for each number n, the family consists of an instance with n finite-state processes. In contrast to safety, the parameterized verification of liveness is currently still considered extremely challenging especially in the presence of probabilities in the model. One major challenge is to provide a sufficiently powerful symbolic framework. One well-known symbolic framework for the parameterized verification of non-probabilistic concurrent systems is regular model checking. Although the framework was recently extended to probabilistic systems, incorporating fairness in the framework -often crucial for verifying termination -has been especially difficult due to the presence of an infinite number of fairness constraints (one for each process). Our main contribution is a systematic, regularity-preserving, encoding of finitary fairness (a realistic notion of fairness proposed by Alur & Henzinger) in the framework of regular model checking for probabilistic parameterized systems. Our encoding reduces termination with finitary fairness to verifying parameterized termination without fairness over probabilistic systems in regular model checking (for which a verification framework already exists). We show that our algorithm could verify termination for many interesting examples from distributed algorithms (Herman's protocol) and evolutionary biology (Moran process, cell cycle switch), which do not hold under the standard notion of fairness. To the best of our knowledge, our algorithm is the first fully-automatic method that can prove termination for these examples.

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“…may be asked by the L * algorithm, which amounts to checking reachability in an infinite-state system, which is undecidable in general. This problem was already noted in [55], [56], [63], [64]. Secondly, a regular inductive invariant satisfying (i) and (ii) might not be unique, and so strictly speaking we are not dealing with a well-defined learning problem.…”

Section: Introductionmentioning

confidence: 95%

“…In this paper, we revisit the classic problem of verifying safety in the regular model checking framework. Many sophisticated semi-algorithms for dealing with this problem have been developed in the literature using methods such as abstraction [4], [5], [21], [20], widening [15], [23], acceleration [58], [43], [11], and learning [55], [56], [39], [64], [63]. One standard technique for proving safety for an infinite-state systems is by exhibiting an inductive invariant Inv (i.e.…”

Section: Introductionmentioning

confidence: 99%

“…The regular set Inv can be constructed as a "regular proof" for safety since checking that a candidate regular set Inv is a proof for safety is decidable. A few semi-algorithms inspired by automata learning -some based on the passive learning algorithms [39], [56], [2] and some others based on active learning algorithms [56], [63]-have been proposed to synthesise a regular inductive invariant in regular model checking. Despite these semi-algorithms, not much attention has been paid to applications of automata learning in regular model checking.…”

Section: Introductionmentioning

confidence: 99%

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Learning to prove safety over parameterised concurrent systems

Chen

Hong

Lin

et al. 2017

2017 Formal Methods in Computer Aided Design (FMCAD)

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Learning to prove safety over parameterised concurrent systems. In: Proc. 17th International Conference on Formal Methods in Computer-AidedDesign (pp. 76-83 Abstract-We revisit the classic problem of proving safety over parameterised concurrent systems, i.e., an infinite family of finite-state concurrent systems that are represented by some finite (symbolic) means. An example of such an infinite family is a dining philosopher protocol with any number n of processes (n being the parameter that defines the infinite family). Regular model checking is a well-known generic framework for modelling parameterised concurrent systems, where an infinite set of configurations (resp. transitions) is represented by a regular set (resp. regular transducer). Although verifying safety properties in the regular model checking framework is undecidable in general, many sophisticated semi-algorithms have been developed in the past fifteen years that can successfully prove safety in many practical instances. In this paper, we propose a simple solution to synthesise regular inductive invariants that makes use of Angluin's classic L * algorithm (and its variants). We provide a termination guarantee when the set of configurations reachable from a given set of initial configurations is regular. We have tested L * algorithm on standard (as well as new) examples in regular model checking including the dining philosopher protocol, the dining cryptographer protocol, and several mutual exclusion protocols (e.g. Bakery, Burns, Szymanski, and German). Our experiments show that, despite the simplicity of our solution, it can perform at least as well as existing semi-algorithms.

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Regular Model Checking Using Solver Technologies and Automata Learning

Cited by 23 publications

References 12 publications

Learning Linear Temporal Properties

Learning Linear Temporal Properties

Fair Termination for Parameterized Probabilistic Concurrent Systems

Learning to prove safety over parameterised concurrent systems

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