Publications

Go2Pins: A framework for the LTL verification of Go programs (extended version)

Abstract

We introduce Go2Pins, a tool that takes a program written in Go and links it with two model-checkers: LTSMin and Spot. Go2Pins is an effort to promote the integration of both formal verification and testing inside industrial-size projects. With this goal in mind, we introduce black-box transitions, an efficient and scalable technique for handling the Go runtime. This approach, inspired by hardware verification techniques, allows easy, automatic and efficient abstractions. Go2Pins also handles basic concurrent programs through the use of a dedicated scheduler. Moreover, in order to efficiently handle recursive programs, we introduce PSLRec, a formalism that augments PSL without changing the complexity of the underlying verification process.

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Towards better heuristics for solving bounded model checking problems

Abstract

This paper presents a new way to improve the performance of the SAT-based bounded model checking problem on sequential and parallel procedures by exploiting relevant information identified through the characteristics of the original problem. This led us to design a new way of building interesting heuristics based on the structure of the underlying problem. The proposed methodology is generic and can be applied for any SAT problem. This paper compares the state-of-the-art approaches with two new heuristics for sequential procedures: Structure-based and Linear Programming heuristics. We extend these study and applied the above methodology on parallel approaches, especially to refine the sharing measure which shows promising results.

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Tuning SAT solvers for LTL model checking

Abstract

Bounded model checking (BMC) aims at checking whether a model satisfies a property. Most of the existing SAT-based BMC approaches rely on generic strategies, which are supposed to work for any SAT problem. The key idea defended in this paper is to tune SAT solvers algorithm using: (1) a static classification based on the variables used to encode the BMC into a Boolean formula; (2) and use the hierarchy of Manna&Pnueli that classifies any property expressed through Linear-time Temporal Logic (LTL). By combining these two information with the classical Literal Block Distance (LBD) measure, we designed a new heuristic, well suited for solving BMC problems. In particular, our work identifies and exploits a new set of relevant (learnt) clauses. We experiment with these ideas by developing a tool dedicated for SAT-based LTL BMC solvers, called BSaLTic. Our experiments over a large database of BMC problems, show promising results. In particular, BSaLTic provides good performance on UNSAT problems. This work highlights the importance of considering the structure of the underlying problem in SAT procedures.

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CosySEL: Improving SAT solving using local symmetries

Abstract

Many satisfiability problems exhibit symmetry properties. Thus, the development of symmetry exploitation techniques seems a natural way to try to improve the efficiency of solvers by preventing them from exploring isomorphic parts of the search space. These techniques can be classified into two categories: dynamic and static symmetry breaking. Static approaches have often appeared to be more effective than dynamic ones. But although these approaches can be considered as complementary, very few works have tried to combine them. In this paper, we present a new tool, CosySEL, that implements a composition of the static Effective Symmetry Breaking Predicates (esbp) technique with the dynamic Symmetric Explanation Learning (sel). esbp exploits symmetries to prune the search tree and sel uses symmetries to speed up the tree traversal. These two accelerations are complementary and their combination was made possible by the introduction of Local symmetries. We conduct our experiments on instances issued from the last ten sat competitions and the results show that our tool outperforms the existing tools on highly symmetrical problems.

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Diversifying a parallel SAT solver with bayesian moment matching

Abstract

In this paper, we present a Bayesian Moment Matching (BMM) in-processing technique for Conflict-Driven Clause-Learning (CDCL) SAT solvers. BMM is a probabilistic algorithm which takes as input a Boolean formula in conjunctive normal form and a prior on a possible satisfying assignment, and outputs a posterior for a new assignment most likely to maximize the number of satisfied clauses. We invoke this BMM method, as an in-processing technique, with the goal of updating the polarity and branching activity scores. The key insight underpinning our method is that Bayesian reasoning is a powerful way to guide the CDCL search procedure away from fruitless parts of the search space of a satisfiable Boolean formula, and towards those regions that are likely to contain satisfying assignments.

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Energy problems in finite and timed automata with Büchi conditions

Abstract

We show how to efficiently solve energy Büchi problems in finite weighted automata and in one-clock weighted timed automata. Solving the former problem is our main contribution and is handled by a modified version of Bellman-Ford interleaved with Couvreur’s algorithm. The latter problem is handled via a reduction to the former relying on the corner-point abstraction. All our algorithms are freely available and implemented in a tool based on the open-source tools TChecker and Spot.

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Higher-dimensional timed and hybrid automata

Abstract

We introduce a new formalism of higher-dimensional timed automata, based on Pratt and van Glabbeek’s higher-dimensional automata and Alur and Dill’s timed automata. We prove that their reachability is PSPACE-complete and can be decided using zone-based algorithms. We also extend the setting to higher-dimensional hybrid automata. The interest of our formalism is in modeling systems which exhibit both real-time behavior and concurrency. Other existing formalisms for real-time modeling identify concurrency and interleaving, which, as we shall argue, is problematic.

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Introduction to the special issue on distributed hybrid systems

Abstract

This special issue contains seven papers within the broad subject of Distributed Hybrid Systems, that is, systems combining hybrid discrete-continuous state spaces with elements of concurrency and logical or spatial distribution. It follows up on several workshops on the same theme which were held between 2017 and 2019 and organized by the editors of this volume. The first of these workshops was held in Aalborg, Denmark, in August 2017 and associated with the MFCS conference. It featured invited talks by Alessandro Abate, Martin Fränzle, Kim G. Larsen, Martin Raussen, and Rafael Wisniewski. The second workshop was held in Palaiseau, France, in July 2018, with invited talks by Luc Jaulin, Thao Dang, Lisbeth Fajstrup, Emmanuel Ledinot, and André Platzer. The third workshop was held in Amsterdam, The Netherlands, in August 2019, associated with the CONCUR conference. It featured a special theme on distributed robotics and had invited talks by Majid Zamani, Hervé de Forges, and Xavier Urbain. The vision and purpose of the DHS workshops was to connect researchers working in real-time systems, hybrid systems, control theory, formal verification, distributed computing, and concurrency theory, in order to advance the subject of distributed hybrid systems. Such systems are abundant and often safety-critical, but ensuring their correct functioning can in general be challenging. The investigation of their dynamics by analysis tools from the aforementioned domains remains fragmentary, providing the rationale behind the workshops: it was conceived that convergence and interaction of theories, methods, and tools from these different areas was needed in order to advance the subject.

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An efficient cascade of U-Net-like convolutional neural networks devoted to brain tumor segmentation

Abstract

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The Dahu graph-cut for interactive segmentation on 2D/3D images

Abstract

Interactive image segmentation is an important application in computer vision for selecting objects of interest in images. Several interactive segmentation methods are based on distance transform algorithms. However, the most known distance transform, geodesic distance, is sensitive to noise in the image and to seed placement. Recently, the Dahu pseudo-distance, a continuous version of the minimum barrier distance (MBD), is proved to be more powerful than the geodesic distance in noisy and blurred images. This paper presents a method for combining the Dahu pseudo-distance with edge information in a graph-cut optimization framework and leveraging each’s complementary strengths. Our method works efficiently on both 2D/3D images and videos. Results show that our method achieves better performance than other distance-based and graph-cut methods, thereby reducing the user’s efforts.

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