Satisfiability Modulo Theories (SMT) problem is a decision problem for logical first order formulas with respect to combinations of background theories such as: arithmetic, bit-vectors, arrays, and uninterpreted functions. Z3 is a new and efficient SMT Solver freely available from Microsoft Research. It is used in various software verification and analysis applications.

https://link.springer.com/content/pdf/10.1007%2F978-3-540-78800-3_24.pdf

Z3: an efficient SMT solver

Lots of learning tasks require dealing with graph data which contains rich relation information among elements. Modeling physics systems, learning molecular fingerprints, predicting protein interface, and classifying diseases demand a model to learn from graph inputs. In other domains such as learning from non-structural data like texts and images, reasoning on extracted structures (like the dependency trees of sentences and the scene graphs of images) is an important research topic which also needs graph reasoning models. Graph neural networks (GNNs) are neural models that capture the dependence of graphs via message passing between the nodes of graphs. In recent years, variants of GNNs such as graph convolutional network (GCN), graph attention network (GAT), graph recurrent network (GRN) have demonstrated ground-breaking performances on many deep learning tasks. In this survey, we propose a general design pipeline for GNN models and discuss the variants of each component, systematically categorize the applications, and propose four open problems for future research.

Graph Neural Networks: A Review of Methods and Applications

Artificial intelligence (AI) has undergone a renaissance recently, making major progress in key domains such as vision, language, control, and decision-making. This has been due, in part, to cheap data and cheap compute resources, which have fit the natural strengths of deep learning. However, many defining characteristics of human intelligence, which developed under much different pressures, remain out of reach for current approaches. In particular, generalizing beyond one's experiences--a hallmark of human intelligence from infancy--remains a formidable challenge for modern AI. 
The following is part position paper, part review, and part unification. We argue that combinatorial generalization must be a top priority for AI to achieve human-like abilities, and that structured representations and computations are key to realizing this objective. Just as biology uses nature and nurture cooperatively, we reject the false choice between "hand-engineering" and "end-to-end" learning, and instead advocate for an approach which benefits from their complementary strengths. We explore how using relational inductive biases within deep learning architectures can facilitate learning about entities, relations, and rules for composing them. We present a new building block for the AI toolkit with a strong relational inductive bias--the graph network--which generalizes and extends various approaches for neural networks that operate on graphs, and provides a straightforward interface for manipulating structured knowledge and producing structured behaviors. We discuss how graph networks can support relational reasoning and combinatorial generalization, laying the foundation for more sophisticated, interpretable, and flexible patterns of reasoning. As a companion to this paper, we have released an open-source software library for building graph networks, with demonstrations of how to use them in practice.

Relational inductive biases, deep learning, and graph networks

A code clone is a code portion in source files that is identical or similar to another. Since code clones are believed to reduce the maintainability of software, several code clone detection techniques and tools have been proposed. This paper proposes a new clone detection technique, which consists of the transformation of input source text and a token-by-token comparison. For its implementation with several useful optimization techniques, we have developed a tool, named CCFinder (Code Clone Finder), which extracts code clones in C, C++, Java, COBOL and other source files. In addition, metrics for the code clones have been developed. In order to evaluate the usefulness of CCFinder and metrics, we conducted several case studies where we applied the new tool to the source code of JDK, FreeBSD, NetBSD, Linux, and many other systems. As a result, CCFinder has effectively found clones and the metrics have been able to effectively identify the characteristics of the systems. In addition, we have compared the proposed technique with other clone detection techniques.

/pdf/ccfinder-a-multilinguistic-token-based-code-clone-detection-3uimzrhga0.pdf

CCFinder: a multilinguistic token-based code clone detection system for large scale source code

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

We describe our work in the Trustworthy Systems group at Data61 (formerly NICTA) in reasoning about concurrency in highassurance, high-performance software systems, in which concurrency may come from three different sources: multiple cores, interrupts and application-level interleaving. 1 Formal Verification – Mentality Shift Recent years have seen a shift in the perception of formal software verification in the academic community and, to some more emerging extent, in the industrial community. The strength of a mathematical proof to guarantee the correctness, security and safety of programs deployed in high-assurance systems has made its way from utopia to reality, and the absence of such strong evidence will hopefully soon be considered negligence for critical systems. This shift was possible thanks to highly successful verified artifacts, such as the CompCert compiler [16] and the seL4 operating system (OS) kernel [14,15]. A remaining grand challenge in formal software verification is concurrency reasoning, much harder than sequential reasoning because of the explosion of the number of interleaved executions that need to be considered. Concurrency in software systems can have three different sources: multiple cores, interrupts and application-level interleaving. In this paper we first briefly explain these kinds of concurrency and their challenges, and we then describe our recent and current work in providing concurrency reasoning framework and verifying concurrent software systems in these three areas. 2 Software Systems and Concurrency – Background Multicore platforms provide a computing power boost that is hard to resist for a very competitive software system market, even for high-security solutions. Code execution can be parallelised on different cores, and the challenge, for implementation as well as verification, is to ensure safe sharing between cores. This can be done using locking mechanisms: a core can access shared data only after acquiring a lock guaranteeing that no other core is manipulating the data c © Springer International Publishing AG 2017 L. de Moura (Ed.): CADE 2017, LNAI 10395, pp. 1–7, 2017. DOI: 10.1007/978-3-319-63046-5 1

Automated Deduction – CADE 26

1 Tractability and Modern Satisfiability Modulo Theories Solvers page 3 1.

Tractability and Modern Satisfiability Modulo Theories Solvers.

In the last decade, advances in satisfiability-modulo-theories (SMT) solvers have powered a new generation of software tools for verification and testing. These tools transform various program analysis problems into the problem of satisfiability of formulas in propositional or first-order logic, where they are discharged by SMT solvers, such as Z3 from Microsoft Research. This paper briefly summarizes four initiatives from Microsoft Research that build upon Z3 and move beyond first-order satisfaction: Fixed points--μZ is a scalable, efficient engine for discharging fixed point queries over recursive predicates with logical constraints, integrated in Z3; Interpolants--Interpolating Z3 uses Z3's proof generation capability to generate Craig interpolants in the first-order theory of uninterpreted functions, arrays and linear arithmetic; Automata--The symbolic automata toolkit lifts classical automata analyses to work modulo symbolic constraints on alphabets; Polynomials--a new decision procedure for the existential theory of the reals allows efficient solving of systems of non-linear arithmetic constraints.

Beyond first-order satisfaction: fixed points, interpolants, automata and polynomials

Relevancy propagation for efficient theory combination is described. In one implementation, an efficient SMT solver dynamically applies relevancy propagation to limit propagation of unnecessary constraints in a DPLL-based solver. This provides a drastic increase in speed and performance over conventional DPLL-based solvers. The relevancy propagation is guided by relevancy rules, which in one implementation emulate Tableau rules for limiting constraint propagation, while maintaining the performance of efficient DPLL-based solvers. An exemplary solver propagates truth assignments to constraints of a formula, and tracks which truth assignments are relevant for determining satisfiability of the formula. The solver propagates truth assignments that were marked relevant to a theory solver, while avoiding propagation of irrelevant truth assignments. The efficient SMT solver provides a drastic reduction in search space covered during quantifier instantiation and offers profound acceleration during bit-vectors reasoning.

Propagation of don't cares for efficient theory combination

Functional programming languages are particularly well-suited for building automated reasoning systems, since (among other reasons) a logical term is well modeled by an inductive type, traversing a term can be implemented generically as a higher-order combinator, and backtracking is dramatically simplified by persistent datastructures. However, existing pure functional programming languages all suffer a major limitation in these domains: traversing a term requires time proportional to the tree size of the term as opposed to its graph size. This limitation would be particularly devastating when building automation for interactive theorem provers such as Lean and Coq, for which the exponential blowup of term-tree sizes has proved to be both common and difficult to prevent. All that is needed to recover the optimal scaling is the ability to perform simple operations on the memory addresses of terms, and yet allowing these operations to be used freely would clearly violate the basic premise of referential transparency. We show how to use dependent types to seal the necessary pointer-address manipulations behind pure functional interfaces while requiring only a negligible amount of additional trust. We have implemented our approach for the upcoming version (v4) of Lean, and our approach could be adopted by other languages based on dependent type theory as well.

Leonardo de Moura

Papers

Automated Deduction – CADE 26

Tractability and Modern Satisfiability Modulo Theories Solvers.

Beyond first-order satisfaction: fixed points, interpolants, automata and polynomials

Propagation of don't cares for efficient theory combination

Sealing Pointer-Based Optimizations Behind Pure Functions