ROLL 1.0: $$\omega $$ -Regular Language Learning Library

Li, Yong; Sun, Xizhe; Turrini, Andrea; Chen, Yu‐Fang; Xu, Junnan

doi:10.1007/978-3-030-17462-0_23

Cited by 7 publications

(6 citation statements)

References 28 publications

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“…We compared the improved version of Ranker presented in this paper with other state-of-the-art tools, namely, Goal [33] (implementing Piterman [10], Safra [9], and Fribourg [16]), Spot 2.9.3 [31] (implementing Redziejowski's algorithm [11]), Seminator 2 [34], LTL2dstar 0.5.4 [35], Roll [36], and the previous version of Ranker from [19], denoted as Ranker Old . All tools were set to the mode where they output a state-based BA.…”

Section: Experimental Evaluationmentioning

confidence: 99%

Complementing Büchi Automata with Ranker

Havlena

Lengál

Šmahlíková

2022

Computer Aided Verification

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We present the tool Ranker for complementing Büchi automata (BAs). Ranker builds on our previous optimizations of rank-based BA complementation and pushes them even further using numerous heuristics to produce even smaller automata. Moreover, it contains novel optimizations of specialized constructions for complementing (i) inherently weak automata and (ii) semi-deterministic automata, all delivered in a robust tool. The optimizations significantly improve the usability of Ranker, as shown in an extensive experimental evaluation with real-world benchmarks, where Ranker produced in the majority of cases a strictly smaller complement than other state-of-the-art tools.

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Section: Experimental Evaluationmentioning

confidence: 99%

Complementing Büchi Automata with Ranker

Havlena

Lengál

Šmahlíková

2022

Computer Aided Verification

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“…We tested the correctness of our implementation using S 's autcross on all BAs in our benchmark. We compared modified R with other state-of-the-art tools, namely, G [22] (implementing P [23], S [8], S [24], and F [25]), S 2.9.3 [26] (implementing Redziejowski's algorithm [27]), S 2 [17], LTL2 0.5.4 [28], and R [29]. All tools were set to the mode where they output an automaton with the standard state-based Büchi acceptance condition.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…Slice-based complementation tracks the acceptance condition using a reduced abstraction on a run tree [37,38]. A learningbased approach was introduced in [39,29]. Allred and Utes-Nitsche then presented a novel optimal complementation algorithm in [25].…”

Section: Related Workmentioning

confidence: 99%

Sky Is Not the Limit: Tighter Rank Bounds for Elevator Automata in Büchi Automata Complementation (Technical Report)

Havlena¹,

Lengál²,

Šmahlíková³

2021

Preprint

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We propose several heuristics for mitigating one of the main causes of combinatorial explosion in rank-based complementation of Büchi automata (BAs): unnecessarily high bounds on the ranks of states. First, we identify elevator automata, which is a large class of BAs (generalizing semi-deterministic BAs), occurring often in practice, where ranks of states are bounded according to the structure of strongly connected components. The bounds for elevator automata also carry over to general BAs that contain elevator automata as a sub-structure. Second, we introduce two techniques for refining bounds on the ranks of BA states using data-flow analysis of the automaton. We implement out techniques as an extension of the tool R for BA complementation and show that they indeed greatly prune the generated state space, obtaining significantly better results and outperforming other state-of-the-art tools on a large set of benchmarks.

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“…We have implemented the Monte Carlo sampling algorithm proposed in Section 3 in ROLL [22] to evaluate it. We performed our experiments on a desktop PC equipped with a 3.6 GHz Intel i7-4790 processor with 16 GB of RAM, of which 4 GB were assigned to the tool.…”

Section: Experimental Evaluationmentioning

confidence: 99%

Proving Non-Inclusion of Büchi Automata based on Monte Carlo Sampling

Li¹,

Turrini²,

Sun³

et al. 2020

Preprint

Self Cite

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The search for a proof of correctness and the search for counterexamples (bugs) are complementary aspects of verification. In order to maximize the practical use of verification tools it is better to pursue them at the same time. While this is well-understood in the termination analysis of programs, this is not the case for the language inclusion analysis of Büchi automata, where research mainly focused on improving algorithms for proving language inclusion, with the search for counterexamples left to the expensive complementation operation. In this paper, we present IMC 2 , a specific algorithm for proving Büchi automata non-inclusion L(A) ⊆ L(B), based on Grosu and Smolka's algorithm MC 2 developed for Monte Carlo model checking against LTL formulas. The algorithm we propose takes M = ln δ/ ln(1 − ε) random lasso-shaped samples from A to decide whether to reject the hypothesis L(A) ⊆ L(B), for given error probability ε and confidence level 1 − δ. With such a number of samples, IMC 2 ensures that the probability of witnessing L(A) ⊆ L(B) via further sampling is less than δ, under the assumption that the probability of finding a lasso counterexample is larger than ε. Extensive experimental evaluation shows that IMC 2 is a fast and reliable way to find counterexamples to Büchi automata inclusion.

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ROLL 1.0: $$\omega $$ -Regular Language Learning Library

Cited by 7 publications

References 28 publications

Complementing Büchi Automata with Ranker

Complementing Büchi Automata with Ranker

Sky Is Not the Limit: Tighter Rank Bounds for Elevator Automata in Büchi Automata Complementation (Technical Report)

Proving Non-Inclusion of Büchi Automata based on Monte Carlo Sampling

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