Mechanizing the Powerset Construction for Restricted Classes of ω-Automata

2014

IJCCBS

Section: Wdba Minimizationmentioning

confidence: 99%

“…Table 3 completes the benchmark of Dax et al (2007) using the sizes of the degeneralized automata generated by spot-0.7.1. The first column is the number of the formula, so you can compare with the figure for other tools displayed at http:// www.daxc.de/eth/atva07/index.html.…”

Section: Wdba Minimizationmentioning

confidence: 99%

LTL translation improvements in Spot 1.0

2014

IJCCBS

“…5, the level reset alone is enough to replace transition ((1, 1), , (2, 1)) by transition ( (1, 1), , (2, 0)), therefore avoiding state (2, 1) and all its descendants. Using level caching without level reset, and assuming the descendants of (2, 0) have been built before those of (2, 1), then state (2, 1) would be connected to states (3, 0) and (4, 0) instead of states (3,1) and (4,1). It is hard to find a small example to illustrate that both optimizations are useful together: the smallest such occurrence in our benchmarks has 20 states.…”

Section: Scc-based Degeneralizationmentioning

confidence: 99%

“…Besides the techniques discussed in this paper, Spot implements the WDBA-minimization algorithm of Dax et al [4] that converts any TGBA representing an obligation property [14] into a minimal Weak Deterministic Büchi Automaton. The corresponding function WDBA minimize(T , ϕ) requires the formula ϕ represented by automaton T to check the validity of the minimized automaton.…”

Section: Translation Scenariosmentioning

confidence: 99%

Compositional Approach to Suspension and Other Improvements to LTL Translation

Babiak

Badie

Model Checking Software

et al. 2013

Abstract. Recently, there was defined a fragment of LTL (containing fairness properties among other interesting formulae) whose validity over a given infinite word depends only on an arbitrary suffix of the word. Building upon an existing translation from LTL to Büchi automata, we introduce a compositional approach where subformulae of this fragment are translated separately from the rest of an input formula and the produced automata are composed in a way that the subformulae are checked only in relevant accepting strongly connected components of the final automaton. Further, we suggest improvements over some procedures commonly applied to generalized Büchi automata, namely over generalized acceptance simplification and over degeneralization. Finally we show how existing simulation-based reductions can be implemented in a signature-based framework in a way that improves the determinism of the automaton.

“…For instance, weak and terminal automata can be reduced very efficiently with techniques such as WDBA minimization [10]. Also simulations reductions [24] will be more efficient on automata with less acceptance conditions.…”

Section: Decomposing the Property Automaton According To Its Sccs Strmentioning

confidence: 99%

Strength-Based Decomposition of the Property Büchi Automaton for Faster Model Checking

Renault

Tools and Algorithms for the Construction and Analysis of Systems

Kordon

et al. 2013

Abstract. The automata-theoretic approach for model checking of lineartime temporal properties involves the emptiness check of a large Büchi automaton. Specialized emptiness-check algorithms have been proposed for the cases where the property is represented by a weak or terminal automaton. When the property automaton does not fall into these categories, a general emptiness check is required. This paper focuses on this class of properties. We refine previous approaches by classifying strongly-connected components rather than automata, and suggest a decomposition of the property automaton into three smaller automata capturing the terminal, weak, and the remaining strong behaviors of the property. The three corresponding emptiness checks can be performed independently, using the most appropriate algorithm. Such a decomposition approach can be used with any automata-based model checker. We illustrate the interest of this new approach using explicit and symbolic LTL model checkers.