Multi-core SCC-Based LTL Model Checking

Bloemen, Vincent; Pol, Jaco van de

doi:10.1007/978-3-319-49052-6_2

Cited by 16 publications

(39 citation statements)

References 37 publications

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“…Recall that our notion of causality relies on the complete enumeration of system traces. Hence, the main challenge is to determine all (lasso-shaped) counterexamples in an efficient way (e.g., on-the-fly [8,24,3,4]). Further future research comprises significant case studies in order to asses the scalability of our approach.…”

Section: Discussionmentioning

confidence: 99%

Causality for General LTL-definable Properties

Caltais

Guetlein

Leue

2019

Electron. Proc. Theor. Comput. Sci.

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In this paper we provide a notion of causality for the violation of general Linear Temporal Logic (LTL) properties. The current work is a natural extension of the previously proposed approach handling causality in the context of LTL-definable safety properties [20,19]. The major difference is that now, counterexamples of general LTL properties are not merely finite traces, but infinite lasso-shaped traces. We analyze such infinite counterexamples and identify the relevant ordered occurrences of causal events, obtained by unfolding the looping part of the lasso shaped counterexample sufficiently many times. The focus is on LTL properties from practical considerations: the current results are to be implemented in QuantUM, a tool for causality checking, that exploits explicit state LTL model checking.

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Section: Discussionmentioning

confidence: 99%

Causality for General LTL-definable Properties

Caltais

Guetlein

Leue

2019

Electron. Proc. Theor. Comput. Sci.

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“…With current hardware systems, one can further improve the model checking performance by using multiple cores. This way, the time to model check can be significantly reduced; related work shows that even though the problem is difficult to parallelize, in practice an almost linear improvement with respect to the number of cores can be obtained [8,16,20,32].…”

Section: Model Checkingmentioning

confidence: 99%

“…Our objective is to study whether the speedups observed with TGRAs in probabilistic model checking also hold for non-probabilistic explicit model checking. There are plenty of algorithms for checking BAs and TGBAs (both sequentially and multi-core) [8,16,32,33]; however, for Rabin acceptance there is only a recent work on a GPU algorithm for checking (non-generalized) RAs [35] and a TGRA checking algorithm for probabilistic model checking [9].…”

Section: Our Goal: Emptiness Checks Using Generalized Rabin Automatamentioning

confidence: 99%

Model checking with generalized Rabin and Fin-less automata

Bloemen

Duret-Lutz

Pol

2019

Int J Softw Tools Technol Transfer

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In the automata theoretic approach to explicit state LTL model checking, the synchronized product of the model and an automaton that represents the negated formula is checked for emptiness. In practice, a (transition-based generalized) Büchi automaton (TGBA) is used for this procedure. This paper investigates whether using a more general form of acceptance, namely a transition-based generalized Rabin automaton (TGRA), improves the model checking procedure. TGRAs can have significantly fewer states than TGBAs; however, the corresponding emptiness checking procedure is more involved. With recent advances in probabilistic model checking and LTL to TGRA translators, it is only natural to ask whether checking a TGRA directly is more advantageous in practice. We designed a multi-core TGRA checking algorithm and performed experiments on a subset of the models and formulas from the 2015 Model Checking Contest and generated LTL formulas for models from the BEEM database. While we found little to no improvement by checking TGRAs directly, we show how various aspects of a TGRA's structure influences the model checking performance. In this paper, we also introduce a Finless acceptance condition, which is a disjunction of TGBAs. We show how to convert TGRAs into automata with Fin-less acceptance and show how a TGBA emptiness procedure can be extended to check Fin-less automata.

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“…In case we want to know whether an LTS satisfies a formula φ, the product of an LTS and a Büchi automaton of Words(φ) is computed, while checking for a witness that visits an accepting state in the Büchi automaton infinitely often. Searching for such a witness can be done on-the-fly [9] with (concurrent) nested depth-first search [8,25] or SCC-based approaches [5,42].…”

Section: Black-box Checking With Model Checkingmentioning

confidence: 99%

“…The counterexamples are finite words and are a subset of the language of the hypothesis. LTSmin [5,22] is an available implementation of a ModelChecker for Monitors in the LearnLib. A ModelCheckerLasso is a refinement of a ModelChecker that uses Büchi automata and where the counterexamples are lassos instead of finite words.…”

Section: The New Api In the Learnlibmentioning

confidence: 99%

Sound black-box checking in the LearnLib

Meijer

Pol

2019

Innovations Syst Softw Eng

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In black-box checking (BBC) incremental hypotheses on the behavior of a system are learned in the form of finite automata, using information from a given set of requirements, specified in Linear-time Temporal Logic (LTL). The LTL formulae are checked on intermediate automata and potential counterexamples are validated on the actual system. Spurious counterexamples are used by the learner to refine these automata. We improve BBC in two directions. First, we improve checking lasso-like counterexamples by assuming a check for state equivalence. This provides a sound method without knowing an upper-bound on the number of states in the system. Second, we propose to check the safety portion of an LTL property first, by deriving simple counterexamples using monitors. We extended LearnLib's system under learning API to make our methods accessible, using LTSmin as model checker under the hood. We illustrate how LearnLib's most recent active learning algorithms can be used for BBC in practice. Using the RERS 2017 challenge, we provide experimental results on the performance of all LearnLib's active learning algorithms when applied in a BBC setting. We will show that the novel incremental algorithms TTT and ADT perform the best. We also provide experiments on the efficiency of various BBC strategies.

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Multi-core SCC-Based LTL Model Checking

Cited by 16 publications

References 37 publications

Causality for General LTL-definable Properties

Causality for General LTL-definable Properties

Model checking with generalized Rabin and Fin-less automata

Sound black-box checking in the LearnLib

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