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Register a new userCompetition Report: CHC-COMP-21
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Publication date
2021
fields
Informatics Engineering
and research's language is
English
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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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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.
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.
VerifyThis is a series of program verification competitions that emphasize the human aspect: participants tackle the verification of detailed behavioral properties -- something that lies beyond the capabilities of fully automatic verification, and requires instead human expertise to suitably encode programs, specifications, and invariants. This paper describes the 8th edition of VerifyThis, which took place at ETAPS 2019 in Prague. Thirteen teams entered the competition, which consisted of three verification challenges and spanned two days of work. The report analyzes how the participating teams fared on these challenges, reflects on what makes a verification challenge more or less suitable for the typical VerifyThis participants, and outlines the difficulties of comparing the work of teams using wildly different verification approaches in a competition focused on the human aspect.
Despite the recent advance of automated program verification, reasoning about recursive data structures remains as a challenge for verification tools and their backends such as SMT and CHC solvers. To address the challenge, we introduce the notion of symbolic automatic relations (SARs), which combines symbolic automata and automatic relations, and inherits their good properties such as the closure under Boolean operations. We consider the satisfiability problem for SARs, and show that it is undecidable in general, but that we can construct a sound (but incomplete) and automated satisfiability checker by a reduction to CHC solving. We discuss applications to SMT and CHC solving on data structures, and show the effectiveness of our approach through experiments.
A pairing function J associates a unique natural number z to any two natural numbers x,y such that for two unpairing functions K and L, the equalities K(J(x,y))=x, L(J(x,y))=y and J(K(z),L(z))=z hold. Using pairing functions on natural number representations of truth tables, we derive an encoding for Binary Decision Diagrams with the unique property that its boolean evaluation faithfully mimics its structural conversion to a a natural number through recursive application of a matching pairing function. We then use this result to derive {em ranking} and {em unranking} functions for BDDs and reduced BDDs. The paper is organized as a self-contained literate Prolog program, available at http://logic.csci.unt.edu/tarau/research/2008/pBDD.zip Keywords: logic programming and computational mathematics, pairing/unpairing functions, encodings of boolean functions, binary decision diagrams, natural number representations of truth tables
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