ing WS1S Systems to Verify Parameterized Networks . . . . . . . . . . . . 188 Kai Baukus, Saddek Bensalem, Yassine Lakhnech and Karsten Stahl FMona: A Tool for Expressing Validation Techniques over Infinite State Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204 J.-P. Bodeveix and M. Filali Transitive Closures of Regular Relations for Verifying Infinite-State Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220 Bengt Jonsson and Marcus Nilsson Diagnostic and Test Generation Using Static Analysis to Improve Automatic Test Generation . . . . . . . . . . . . . 235 Marius Bozga, Jean-Claude Fernandez and Lucian Ghirvu Efficient Diagnostic Generation for Boolean Equation Systems . . . . . . . . . . . . 251 Radu Mateescu Efficient Model-Checking Compositional State Space Generation with Partial Order Reductions for Asynchronous Communicating Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266 Jean-Pierre Krimm and Laurent Mounier Checking for CFFD-Preorder with Tester Processes . . . . . . . . . . . . . . . . . . . . . . . 283 Juhana Helovuo and Antti Valmari Fair Bisimulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299 Thomas A. Henzinger and Sriram K. Rajamani Integrating Low Level Symmetries into Reachability Analysis . . . . . . . . . . . . . 315 Karsten Schmidt Model-Checking Tools Model Checking Support for the ASM High-Level Language . . . . . . . . . . . . . . 331 Giuseppe Del Castillo and Kirsten Winter Table of

Tools and Algorithms for the Construction and Analysis of Systems. Proc. TACAS 2009

Boolean Satisfiability (SAT) has seen many successful applications in various fields such as Electronic Design Automation and Artificial Intelligence. However, in some cases, it may be required/preferable to use variations of the general SAT problem. In this paper, we consider one important variation, the Partial MAX-SAT problem. Unlike SAT, Partial MAX-SAT has certain constraints (clauses) that are marked as relaxable and the rest are hard, i.e. non-relaxable. The objective is to find a variable assignment that satisfies all non-relaxable clauses together with the maximum number of relaxable ones. We have implemented two solvers for the Partial MAX-SAT problem using a contemporary SAT solver, zChaff. The first approach is a novel diagnosis based algorithm; it iteratively analyzes the UNSAT core of the current SAT instance and eliminates the core through a modification of the problem instance by adding relaxation variables. The second approach is encoding based; it constructs an efficient auxiliary counter that constrains the number of relaxed clauses and supports binary search or linear scan for the optimal solution. Both solvers are complete as they guarantee the optimality of the solution. We discuss the relative strengths and thus applicability of the two solvers for different solution scenarios. Further, we show how both techniques benefit from the persistent learning techniques of incremental SAT. Experiments using practical instances of this problem show significant improvements over the best known solvers.

On solving the partial MAX-SAT problem

Boolean satisfiability (SAT) solvers are at the core of efficient approaches for solving a vast multitude of practical problems. Moreover, albeit targeting an NP-complete problem, SAT solvers are increasingly used for tackling problems beyond NP. Despite the success of SAT in practice, modeling with SAT and more importantly implementing SAT-based problem solving solutions is often a difficult and error-prone task. This paper proposes the PySAT toolkit, which enables fast Python-based prototyping using SAT oracles and SAT-related technology. PySAT provides a simple API for working with a few state-of-the-art SAT oracles and also integrates a number of cardinality constraint encodings, all aiming at simplifying the prototyping process. Experimental results presented in the paper show that PySAT-based implementations can be as efficient as those written in a low-level language.

PySAT: A Python Toolkit for Prototyping with SAT Oracles

Previous version of a program can be a powerful enabler for program analysis by defining new relative specifications and making the results of current program analysis more relevant. In this paper, we describe the approach of differential assertion checking (DAC) for comparing different versions of a program with respect to a set of assertions. DAC provides a natural way to write relative specifications over two programs. We introduce a novel modular approach to DAC by reducing it to safety checking of a composed program, which can be accomplished by standard program verifiers. In particular, we leverage automatic invariant generation to synthesize relative specifications for pairs of loops and procedures. We provide a preliminary evaluation of a prototype implementation within the SymDiff tool along two directions (a) soundly verifying bug fixes in the presence of loops and (b) providing a knob for suppressing alarms when checking a new version of a program.

/pdf/differential-assertion-checking-6hljx5n48k.pdf

Differential assertion checking

This report describes the 2019 Competition on Software Verification (SV-COMP), the 8\(^{\text {th}}\) edition of a series of comparative evaluations of fully automatic software verifiers for C programs, and now also for Java programs. The competition provides a snapshot of the current state of the art in the area, and has a strong focus on replicability of its results. The repository of benchmark verification tasks now supports a new, more flexible format for task definitions (based on YAML), which was a precondition for conveniently benchmarking Java programs in the same controlled competition setting that was successfully applied in the previous years. The competition was based on 10 522 verification tasks for C programs and 368 verification tasks for Java programs. Each verification task consisted of a program and a property (reachability, memory safety, overflows, termination). SV-COMP 2019 had 31 participating verification systems from 14 countries.

/pdf/automatic-verification-of-c-and-java-programs-sv-comp-2019-3xnz319p63.pdf

Automatic Verification of C and Java Programs: SV-COMP 2019

Maximum Satisfiability (MaxSAT) is an optimization variant of the Boolean Satisfiability (SAT) problem. In general, MaxSAT algorithms perform a succession of SAT solver calls to reach an optimum solution making extensive use of cardinality constraints. Many of these algorithms are non-incremental in nature, i.e. at each iteration the formula is rebuilt and no knowledge is reused from one iteration to another. In this paper, we exploit the knowledge acquired across iterations using novel schemes to use cardinality constraints in an incremental fashion. We integrate these schemes with several MaxSAT algorithms. Our experimental results show a significant performance boost for these algo- rithms as compared to their non-incremental counterparts. These results suggest that incremental cardinality constraints could be beneficial for other constraint solving domains.

Incremental Cardinality Constraints for MaxSAT

Maximum Satisfiability (MaxSAT) is an optimization variant of the Boolean Satisfiability (SAT) problem. In general, MaxSAT algorithms perform a succession of SAT solver calls to reach an optimum solution making extensive use of cardinality constraints. Many of these algorithms are non-incremental in nature, i.e. at each iteration the formula is rebuilt and no knowledge is reused from one iteration to another. In this paper, we exploit the knowledge acquired across iterations using novel schemes to use cardinality constraints in an incremental fashion. We integrate these schemes with several MaxSAT algorithms. Our experimental results show a significant performance boost for these algorithms as compared to their non-incremental counterparts. These results suggest that incremental cardinality constraints could be beneficial for other constraint solving domains.

The Message Passing Interface (MPI) is the standard API for parallelization in high-performance and scientific computing. Communication deadlocks are a frequent problem in MPI programs, and this article addresses the problem of discovering such deadlocks. We begin by showing that if an MPI program is single path, the problem of discovering communication deadlocks is NP-complete. We then present a novel propositional encoding scheme that captures the existence of communication deadlocks. The encoding is based on modeling executions with partial orders and implemented in a tool called MOPPER. The tool executes an MPI program, collects the trace, builds a formula from the trace using the propositional encoding scheme, and checks its satisfiability. Finally, we present experimental results that quantify the benefit of the approach in comparison to other analyzers and demonstrate that it offers a scalable solution for single-path programs.

http://qav.comlab.ox.ac.uk/papers/fm14.pdf

Precise Predictive Analysis for Discovering Communication Deadlocks in MPI Programs

Most software verification tools can be classified into one of a number of established families, each of which has their own focus and strengths. For example, concrete counterexample generation in model checking, invariant inference in abstract interpretation and completeness via annotation for deductive verification. This creates a significant and fundamental usability problem as users may have to learn and use one technique to find potential problems but then need an entirely different one to show that they have been fixed. This paper presents a single, unified algorithm kIkI, which strictly generalises abstract interpretation, bounded model checking and k-induction. This not only combines the strengths of these techniques but allows them to interact and reinforce each other, giving a ‘single-tool’ approach to verification.

Safety Verification and Refutation by k-invariants and k-induction

/pdf/safety-verification-and-refutation-by-k-invariants-and-k-3i3f5mi3er.pdf

Saurabh Joshi

Papers

Incremental Cardinality Constraints for MaxSAT

Incremental Cardinality Constraints for MaxSAT

Precise Predictive Analysis for Discovering Communication Deadlocks in MPI Programs

Safety Verification and Refutation by k-invariants and k-induction

Safety Verification and Refutation by k-invariants and k-induction