Combining Explicit and Symbolic LTL Model Checking Using Generalized Testing Automata

A. Salem, Mohamed Graiet
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引用次数: 1

Abstract

In automata-theoretic model checking, there are mainly two approaches: explicit and symbolic. In the explicit approach [1], the state-space is constructed explicitly and lazily during exploration (i.e., on-the-fly). The symbolic approach [2] tries to overcome the state-space explosion obstacle by symbolically encoding the state-space in a concise way using decision diagrams. However, this symbolic construction is not performed on-the-fly as in the explicit approach. In order to take advantage of the best of both worlds, hybrid approaches [3, 4, 5] are proposed as combinations of explicit and symbolic approaches. A hybrid approach is usually based on an on-the-fly construction of an explicit graph of symbolic nodes, where each symbolic node encodes a subset of states by means of binary decision diagrams. An alternative to the standard Büchi automaton, called Testing automaton [6] has never been used before for hybrid model checking. In addition, in previous work [7, 8], we have shown that Generalized Testing Automata (TGTA) can outperform the Büchi automata for explicit and symbolic model checking of stutter-invariant LTL properties. In this work, we investigate the use of these TGTA to improve hybrid model checking. We show how traditional hybrid approaches based on Generalized Büchi Automata (TGBA) can be adapted to obtain TGTA-based hybrid approaches. Then, each original approach is experimentally compared against its TGTA variant. The results show that these new variants are statistically more efficient.
用广义测试自动机结合显式和符号LTL模型检验
在自动机理论模型检验中,主要有显式检验和符号检验两种方法。在显式方法[1]中,状态空间是在探索过程中显式和惰性地构建的(即,在运行中)。符号方法[2]试图通过使用决策图以简洁的方式对状态空间进行符号编码来克服状态空间爆炸障碍。然而,这种符号构建并不像显式方法那样即时进行。为了充分利用这两个世界的优点,混合方法[3,4,5]被提出作为显式和符号方法的组合。混合方法通常基于符号节点的显式图的动态构造,其中每个符号节点通过二元决策图对状态子集进行编码。标准b chi自动机的替代方案,称为测试自动机[6],以前从未用于混合模型检查。此外,在之前的工作[7,8]中,我们已经证明广义测试自动机(TGTA)在口吃不变LTL属性的显式和符号模型检查方面优于b自动机。在这项工作中,我们研究了使用这些TGTA来改进混合模型检查。我们展示了基于广义 chi自动机(TGBA)的传统混合方法如何适应于基于tgta的混合方法。然后,将每种原始方法与其TGTA变体进行实验比较。结果表明,这些新变体在统计上效率更高。
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