M. De Wulf

Bio: M. De Wulf is an academic researcher from Université libre de Bruxelles. The author has contributed to research in topics: Deterministic finite automaton & Model checking. The author has an hindex of 2, co-authored 2 publications receiving 220 citations.

Antichains: a new algorithm for checking universality of finite automata

Abstract: We propose and evaluate a new algorithm for checking the universality of nondeterministic finite automata. In contrast to the standard algorithm, which uses the subset construction to explicitly determinize the automaton, we keep the determinization step implicit. Our algorithm computes the least fixed point of a monotone function on the lattice of antichains of state sets. We evaluate the performance of our algorithm experimentally using the random automaton model recently proposed by Tabakov and Vardi. We show that on the difficult instances of this probabilistic model, the antichain algorithm outperforms the standard one by several orders of magnitude. We also show how variations of the antichain method can be used for solving the language-inclusion problem for nondeterministic finite automata, and the emptiness problem for alternating finite automata.

Antichains: alternative algorithms for LTL satisfiability and model-checking

Abstract: The linear temporal logic (LTL) was introduced by Pnueli as a logic to express properties over the computations of reactive systems Since this seminal work, there have been a large number of papers that have studied deductive systems and algorithmic methods to reason about the correctness of reactive programs with regard to LTL properties In this paper, we propose new efficient algorithms for LTL satisfiability and model-checking Our algorithms do not construct nondeterministic automata from LTL formulas but work directly with alternating automata using efficient exploration techniques based on antichains

Featured Transition Systems: Foundations for Verifying Variability-Intensive Systems and Their Application to LTL Model Checking

Abstract: The premise of variability-intensive systems, specifically in software product line engineering, is the ability to produce a large family of different systems efficiently. Many such systems are critical. Thorough quality assurance techniques are thus required. Unfortunately, most quality assurance techniques were not designed with variability in mind. They work for single systems, and are too costly to apply to the whole system family. In this paper, we propose an efficient automata-based approach to linear time logic (LTL) model checking of variability-intensive systems. We build on earlier work in which we proposed featured transitions systems (FTSs), a compact mathematical model for representing the behaviors of a variability-intensive system. The FTS model checking algorithms verify all products of a family at once and pinpoint those that are faulty. This paper complements our earlier work, covering important theoretical aspects such as expressiveness and parallel composition as well as more practical things like vacuity detection and our logic feature LTL. Furthermore, we provide an in-depth treatment of the FTS model checking algorithm. Finally, we present SNIP, a new model checker for variability-intensive systems. The benchmarks conducted with SNIP confirm the speedups reported previously.

Algorithms for Omega-Regular Games with Imperfect Information

Abstract: We study observation-based strategies for two-player turn-based games on graphs with omega-regular objectives. An observation-based strategy relies on imperfect information about the history of a play, namely, on the past sequence of observations. Such games occur in the synthesis of a controller that does not see the private state of the plant. Our main results are twofold. First, we give a fixed-point algorithm for computing the set of states from which a player can win with a deterministic observation-based strategy for any omega-regular objective. The fixed point is computed in the lattice of antichains of state sets. This algorithm has the advantages of being directed by the objective and of avoiding an explicit subset construction on the game graph. Second, we give an algorithm for computing the set of states from which a player can win with probability 1 with a randomized observation-based strategy for a Buechi objective. This set is of interest because in the absence of perfect information, randomized strategies are more powerful than deterministic ones. We show that our algorithms are optimal by proving matching lower bounds.

Checking NFA equivalence with bisimulations up to congruence

Abstract: We introduce bisimulation up to congruence as a technique for proving language equivalence of non-deterministic finite automata. Exploiting this technique, we devise an optimisation of the classical algorithm by Hopcroft and Karp. We compare our approach to the recently introduced antichain algorithms, by analysing and relating the two underlying coinductive proof methods. We give concrete examples where we exponentially improve over antichains; experimental results moreover show non negligible improvements.

Acacia+, a tool for LTL synthesis

Abstract: We present Acacia+, a tool for solving the LTL realizability and synthesis problems. We use recent approaches that reduce these problems to safety games, and can be solved efficiently by symbolic incremental algorithms based on antichains. The reduction to safety games offers very interesting properties in practice: the construction of compact solutions (when they exist) and a compositional approach for large conjunctions of LTL formulas.

Advanced Ramsey-based Büchi automata inclusion testing

Abstract: Checking language inclusion between two nondeterministic Buchi automata A and B is computationally hard (PSPACE-complete). However, several approaches which are efficient in many practical cases have been proposed. We build on one of these, which is known as the Ramsey-based approach. It has recently been shown that the basic Ramsey-based approach can be drastically optimized by using powerful subsumption techniques, which allow one to prune the search-space when looking for counterexamples to inclusion. While previous works only used subsumption based on set inclusion or forward simulation on A and B, we propose the following new techniques: (1) A larger subsumption relation based on a combination of backward and forward simulations on A and B. (2) A method to additionally use forward simulation between A and B. (3) Abstraction techniques that can speed up the computation and lead to early detection of counterexamples. The new algorithm was implemented and tested on automata derived from real-world model checking benchmarks, and on the Tabakov-Vardi random model, thus showing the usefulness of the proposed techniques.