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Register a new userUltimate TreeAutomizer (CHC-COMP Tool Description)
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EPTCS
Publication date
2019
fields
Informatics Engineering
and research's language is
English
Authors
Daniel Dietsch
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We present Ultimate TreeAutomizer, a solver for satisfiability of sets of constrained Horn clauses. Constrained Horn clauses (CHC) are a fragment of first order logic with attractive properties in terms of expressiveness and accessibility to algorithmic solving. Ultimate TreeAutomizer is based on the techniques of trace abstraction, tree automata and tree interpolation. This paper serves as a tool description for TreeAutomizer in CHC-COMP 2019.
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CHC-COMP-21 is the fourth competition of solvers for Constrained Horn Clauses. In this year, 7 solvers participated at the competition, and were evaluated in 7 separate tracks on problems in linear integer arithmetic, linear real arithmetic, arrays, and algebraic data-types. The competition was run in March 2021 using the StarExec computing cluster. This report gives an overview of the competition design, explains the organisation of the competition, and presents the competition results.
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This report summarizes the second International Verification of Neural Networks Competition (VNN-COMP 2021), held as a part of the 4th Workshop on Formal Methods for ML-Enabled Autonomous Systems that was collocated with the 33rd International Conference on Computer-Aided Verification (CAV). Twelve teams participated in this competition. The goal of the competition is to provide an objective comparison of the state-of-the-art methods in neural network verification, in terms of scalability and speed. Along this line, we used standard formats (ONNX for neural networks and VNNLIB for specifications), standard hardware (all tools are run by the organizers on AWS), and tool parameters provided by the tool authors. This report summarizes the rules, benchmarks, participating tools, results, and lessons learned from this competition.
The design of IoT systems could benefit from the combination of two different analyses. We perform a first analysis to approximate how data flow across the system components, while the second analysis checks their communication soundness. We show how the combination of these two analyses yields further benefits hardly achievable by separately using each of them. We exploit two independently developed tools for the analyses. Firstly, we specify IoT systems in IoT-LySa, a simple specification language featuring asynchronous multicast communication of tuples. The values carried by the tuples are drawn from a term-algebra obtained by a parametric signature. The analysis of communication soundness is supported by ChorGram, a tool developed to verify the compatibility of communicating finite-state machines. In order to combine the analyses we implement an encoding of IoT-LySa processes into communicating machines. This encoding is not completely straightforward because IoT-LySa has multicast communications with data, while communication machines are based on point-to-point communications where only finitely many symbols can be exchanged. To highlight the benefits of our approach we appeal to a simple yet illustrative example.
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