Solving the ignoring problem for partial order reduction

Évangelista, Sami; Pajault, Christophe

doi:10.1007/s10009-010-0137-y

Cited by 34 publications

(28 citation statements)

References 29 publications

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“…Since then, a number of cycle provisos has been proposed as solution in combination with different search strategies, see e.g. [9,3,7]. However, in the context of SSR, these provisos are unsuitable, since they are too restrictive.…”

Section: Contributionsmentioning

confidence: 99%

“…However, in the context of SSR, these provisos are unsuitable, since they are too restrictive. In this paper we use an adapted proviso from [7], for the combination of POR and SSR. To the best of our knowledge, such a proviso has not yet been proposed.…”

Section: Contributionsmentioning

confidence: 99%

“…Otherwise, a relevant transition might be permanently ignored due to a cycle in the reduced state space. We have adapted the proviso C S 2 from [7], to the notion of weak reachability that arises in SSR, resulting in the novel proviso C S 2W . C S 2W For every state s in AR there exists a weakly reachable state s from s in AR, such that s is fully expanded, i.e.…”

Section: Cycle Provisomentioning

confidence: 99%

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Verifying SystemC using stateful symbolic simulation

Herdt

Drechsler

2015

Proceedings of the 52nd Annual Design Automation Conference

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Formal verification of high-level SystemC designs is an important and challenging problem. Recent works have proposed symbolic simulation in combination with Partial Order Reduction (POR) as a promising solution and experimentally demonstrated its potential. However, these symbolic simulation approaches have a fundamental limitation in handling cyclic state spaces. The reason is that they are based on stateless model checking and thus unable to avoid revisiting states in a cycle. In this paper, we propose a novel stateful symbolic simulation approach for SystemC. For the efficient detection of revisited symbolic states, we apply symbolic subsumption checking. Furthermore, our implementation integrates a cycle proviso to preserve the soundness of POR in the presence of cycles. We demonstrate the scalability and the efficiency of the proposed approach using an extensive set of experiments.

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Section: Contributionsmentioning

confidence: 99%

Section: Contributionsmentioning

confidence: 99%

Section: Cycle Provisomentioning

confidence: 99%

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Verifying SystemC using stateful symbolic simulation

Herdt

Drechsler

2015

Proceedings of the 52nd Annual Design Automation Conference

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“…This way, the possibility exists that confluent transitions are taken indefinitely, ignoring the presence of other actions. This problem is well known in the theory of partial order reduction as the ignoring problem [3,25], and is dealt with by the cycle condition A3 of the ample set method. With strong confluence this problem can be dealt with by requiring acyclicity, and in Section 5 we will look at an alternative approach.…”

Section: Definition 20 (Confluence Reduction) Given An Mdpmentioning

confidence: 99%

A comparison of confluence and ample sets in probabilistic and non-probabilistic branching time

Hansen

Timmer

2014

Theoretical Computer Science

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Confluence reduction and partial order reduction by means of ample sets are two different techniques for state space reduction in both traditional and probabilistic model checking. This paper provides an extensive comparison between these two methods, and answers the question how they relate in terms of reduction power when preserving branching time properties. We prove that, while both preserve the same properties, confluence reduction is strictly more powerful than partial order reduction: every reduction that can be obtained with partial order reduction can also be obtained with confluence reduction, but the converse is not true.The main challenge for the comparison is that confluence reduction was defined in an action-based setting, whereas ample set reduction is often defined in a state-based setting. We therefore redefine confluence reduction in the state-based setting of Markov decision processes, and provide a nontrivial proof of its correctness. Additionally, we pinpoint precisely in what way confluence reduction is more general, and provide conditions under which the two notions coincide. The results we present also hold for non-probabilistic models, as they can just as well be applied in a context where all transitions are non-probabilistic.To discuss the practical applicability of our results, we adapt a state space generation technique based on representative states, already known in combination with confluence reduction, so that it can also be applied to ample sets.

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“…The phenomenon is a manifestation of the so-called ignoring problem [20], [21], whereby a cycle of actions is considered completed, with some transition enabled on the way and never explored. When looking for global deadlocks, this is not a problem.…”

Section: Partial Deadlockmentioning

confidence: 99%

Partial Order Reduction for Model Checking Markov Decision Processes under Unconditional Fairness

Hansen

Kwiatkowska

2011

2011 Eighth International Conference on Quantitative Evaluation of SysTems

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Abstract-Fairness assumptions are needed to verify liveness properties of concurrent systems. In this paper we explore the so-called unconditional fairness in Markov decision processes (MDPs), which is a prerequisite for quantitative assumeguarantee reasoning. Unconditional fairness refers to executions where all processes are guaranteed to participate. We prove that realisability of unconditional fairness coincides with the absence of partial deadlocks, i.e., end components where a process suffers from starvation. We propose a weak variant of the stubborn set method to reduce MDPs, while preserving the realisability of unconditional fairness and maximal probabilities of reaching bottom end components under fair schedulers.

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Solving the ignoring problem for partial order reduction

Cited by 34 publications

References 29 publications

Verifying SystemC using stateful symbolic simulation

Verifying SystemC using stateful symbolic simulation

A comparison of confluence and ample sets in probabilistic and non-probabilistic branching time

Partial Order Reduction for Model Checking Markov Decision Processes under Unconditional Fairness

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