The mCRL2 Toolset for Analysing Concurrent Systems

Bunte, Olav; Groote, Jan Friso; Keiren, Jeroen J. A.; Laveaux, Maurice; Neele, Thomas; Vink, Erik P. de; Wesselink, Wieger; Wijs, Anton; Willemse, Tim A. C.

doi:10.1007/978-3-030-17465-1_2

Cited by 97 publications

(28 citation statements)

References 46 publications

(50 reference statements)

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“…In the context of timed systems, we are aware of only one paper about verification of failure detectors [6]. In this paper, the authors used three tools, namely UPPAAL [33], mCRL2 [12], and FDR2 [39] to verify small instances of a failure detector based on a logical ring arrangement of processes. Their verification approach required that message buffers were bounded, and had restricted behaviors in the specifications.…”

Section: : Ssndmentioning

confidence: 99%

A case study on parametric verification of failure detectors

Tran¹,

Konnov²,

Widder³

2021

Preprint

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Partial synchrony is a model of computation in many distributed algorithms and modern blockchains. These algorithms are typically parameterized in the number of participants, and their correctness requires the existence of bounds on message delays and on the relative speed of processes after reaching Global Stabilization Time (GST). These characteristics make partially synchronous algorithms parameterized in the number of processes, and parametric in time bounds, which render automated verification of partially synchronous algorithms challenging. In this paper, we present a case study on formal verification of both safety and liveness of the Chandra and Toueg failure detector that is based on partial synchrony. To this end, we first introduce and formalize the class of symmetric point-to-point algorithms that contains the failure detector. Second, we show that these symmetric point-to-point algorithms have a cutoff, and the cutoff results hold in three models of computation: synchrony, asynchrony, and partial synchrony. As a result, one can verify them by model checking small instances, but the verification problem stays parametric in time. Next, we specify the failure detector and the partial synchrony assumptions in three frameworks: TLA + , IVy, and counter automata. Importantly, we tune our modeling to use the strength of each method: (1) We are using counters to encode message buffers with counter automata, (2) we are using first-order relations to encode message buffers in IVy, and (3) we are using both approaches in TLA + . By running the tools for TLA + (TLC and APALACHE) and counter automata (FAST), we demonstrate safety for fixed time bounds. This helped us to find the inductive invariants for fixed parameters, which we used as a starting point for the proofs with IVy. By running IVy, we prove safety for arbitrary time bounds. Moreover, we show how to verify liveness of the failure detector by reducing the verification problem to safety verification. Thus, both properties are verified by developing inductive invariants with IVy. We conjecture that correctness of other partially synchronous algorithms may be proven by following the presented methodology.

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Section: : Ssndmentioning

confidence: 99%

A case study on parametric verification of failure detectors

Tran¹,

Konnov²,

Widder³

2021

Preprint

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“…In this experiment, we chose to simulate the described part of the X-ray machine in the mCRL2 toolset [6]. We wrote a specification in the mCRL2 language, shown in Appendix A, capturing the informal description above.…”

Section: The System Under Learningmentioning

confidence: 99%

Active Learning of Decomposable Systems

Duhaiby

Groote

2020

Proceedings of the 8th International Conference on Formal Methods in Software Engineering

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Active automata learning is a technique of querying black box systems and modelling their behaviour. In this paper, we aim to apply active learning in parts. We formalise the conditions on systemswith a decomposable set of actions-that make learning in parts possible. The systems are themselves decomposable through nonintersecting subsets of actions. Learning these subsystems/components requires less time and resources. We prove that the technique works for both two components as well as an arbitrary number of components. We illustrate the usefulness of this technique through a classical example and through a real example from the industry. CCS CONCEPTS • Computing methodologies → Model development and analysis; • Theory of computation → Formal languages and automata theory; Active learning; • Software and its engineering → Model-driven software engineering.

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“…The notion of bisimilarity for Kripke structures and Labelled Transition Systems (LTSs) is commonly used to define behavioural equivalence. Deciding this behavioural equivalence is important in the field of modelling and verifying concurrent and multi-component systems [4,15]. Kanellakis and Smolka proposed a partition refinement algorithm for obtaining the bisimilarity relation for Kripke structures [11].…”

Section: Introductionmentioning

confidence: 99%

A Linear Parallel Algorithm to Compute Bisimulation and Relational Coarsest Partitions

Martens

Groote

Haak

et al. 2021

Formal Aspects of Component Software

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The most efficient way to calculate strong bisimilarity is by finding the relational coarsest partition of a transition system. We provide the first linear-time algorithm to calculate strong bisimulation using parallel random access machines (PRAMs). More precisely, with n states, m transitions and $$| Act |\le m$$ | A c t | ≤ m action labels, we provide an algorithm for $$\max (n,m)$$ max ( n , m ) processors that calculates strong bisimulation in time $$\mathcal {O}(n+| Act |)$$ O ( n + | A c t | ) and space $$\mathcal {O}(n+m)$$ O ( n + m ) . The best-known PRAM algorithm has time complexity $$\mathcal {O}(n\log n)$$ O ( n log n ) on a smaller number of processors making it less suitable for massive parallel devices such as GPUs. An implementation on a GPU shows that the linear time-bound is achievable on contemporary hardware.

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The mCRL2 Toolset for Analysing Concurrent Systems

Cited by 97 publications

References 46 publications

A case study on parametric verification of failure detectors

A case study on parametric verification of failure detectors

Active Learning of Decomposable Systems

A Linear Parallel Algorithm to Compute Bisimulation and Relational Coarsest Partitions

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